Amino Acid To Binary (AA2Binary)

Description

This function transforms an amino acid to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

AA2Binary(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*20. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AA2Binary(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Amino Acid to K Part Composition (AAKpartComposition)

Description

In this function, each sequence is divided into k equal partitions. The length of each part is equal to ceiling(l(lenght of the sequence)/k). The last part can have a different length containing the residual amino acids. The amino acid composition is calculated for each part.

Usage

AAKpartComposition(seqs, k = 3, normalized = TRUE, label = c())

Arguments

Value

a feature matrix with k*20 number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-AAKpartComposition(seqs=filePrs,k=5,normalized=FALSE)

Amino Acid Index (AAindex)

Description

This function converts the amino acids of a sequence to a list of physicochemical properties in the aaIndex file. For each amino acid, the function uses a numeric vector which shows the aaIndex of the amino acid.

Usage

AAindex(
  seqs,
  selectedAAidx = 1:554,
  standardized = TRUE,
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

In this function each amino acid is converted to a numeric vector. Elements of the vector represent a physicochemical property for the amino acid. In the aaIndex database, there are 554 amino acid indices. Users can choose the desired aaindex by specifying aaindexes through their ids or indexes in the aaIndex file, via selectedAAidx parameter.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(number of selected amino acid indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AAindex(seqs = ptmSeqsVect, selectedAAidx=1:5,outFormat="mat")

ad<-paste0(dir,"/aaidx.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
AAindex(seqs = filePrs, selectedAAidx=1:5,standardized=TRUE,threshold=1,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Amino Acid Autocorrelation-Autocovariance (AAutoCor)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AAutoCor(
  seqs,
  selectedAAidx = list(c("CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102",
    "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201")),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-AAutoCor(seqs=filePrs,maxlag=20,threshold=0.9,
type=c("Moran","Geary","NormalizeMBorto","AC"))

mat2<-AAutoCor(seqs=filePrs,maxlag=20,threshold=0.9,selectedAAidx=
list(c('CIDH920105','BHAR880101','CHAM820101','CHAM820102'),c('CHOC760101','BIGC670101')
,c('CHAM810101','DAYM780201')),type=c("AC","CC","ACC"))

Learn from alignments (AESNN3)

Description

This function replace each amino acid of the sequence with a three-dimensional vector. Values are taken from the three hidden units of the neural network trained on structure alignments. The AESNN3 function can be applied to encode peptides of equal length.

Usage

AESNN3(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(5) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

References

Lin K, May AC, Taylor WR. Amino acid encoding schemes from protein structure alignments: multi-dimensional vectors to describe residue types. J Theor Biol (2002).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-AESNN3(seqs = ptmSeqsVect,outFormat="mat")

Accumulated Nucleotide Frequency (ANF_DNA)

Description

This function replaces nucleotides with a four-length vector. The first three elements represent the nucleotides and the forth holds the frequency of the nucleotide from the beginning of the sequence until the position of the nucleotide in the sequence. 'A' will be replaced with c(1, 1, 1, freq), 'C' with c(0, 1, 0, freq),'G' with c(1, 0, 0, freq), and 'T' with c(0, 0, 1, freq).

Usage

ANF_DNA(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(4) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, W., Tran, H., Liang, Z. et al. Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 5, 13859 (2015).

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-ANF_DNA(seqs = LNC50Nuc,outFormat="mat")

Accumulated riboNucleotide Frequency (ANF_RNA)

Description

This function replaces ribonucleotides with a four-length vector. The first three elements represent the ribonucleotides and the forth holds the frequency of the ribonucleotide from the beginning of the sequence until the position of the ribonucleotide in the sequence. 'A' will be replaced with c(1, 1, 1, freq), 'C' with c(0, 1, 0, freq),'G' with c(1, 0, 0, freq), and 'U' with c(0, 0, 1, freq).

Usage

ANF_RNA(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(4) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, W., Tran, H., Liang, Z. et al. Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 5, 13859 (2015).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ANF_RNA(seqs = fileLNC,outFormat="mat")

Amphiphilic Pseudo-Amino Acid Composition(series) (APAAC)

Description

This function calculates the amphiphilic pseudo amino acid composition (Series) for each sequence.

Usage

APAAC(
  seqs,
  aaIDX = c("ARGP820101", "HOPT810101"),
  lambda = 30,
  w = 0.05,
  l = 1,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo amino acid composition for each physicochemical property. We have provided users with the ability to choose among different properties (i.e., not confined to hydrophobicity or hydrophilicity).

Value

A feature matrix such that the number of columns is 20^l+(number of chosen aaIndex*lambda) and the number of rows equals the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-APAAC(seqs=filePrs,l=2,lambda=3,threshold=1)

Amphiphilic Pseudo-k Nucleotide Composition-Tri(series) (APkNUCTri_DNA)

Description

This function calculates the amphiphilic pseudo k nucleotide composition(Tri) (Series) for each sequence.

Usage

APkNUCTri_DNA(
  seqs,
  selectedIdx = c("Dnase I", "Bendability (DNAse)"),
  lambda = 3,
  w = 0.05,
  l = 3,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of trinucleotides. We have provided users with the ability to choose among the 12 properties in the tri-nucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-APkNUCTri_DNA(seqs=fileLNC,l=3,threshold=1)

Amphiphilic Pseudo-k Nucleotide Composition-di(series) (APkNUCdi_DNA)

Description

This function calculates the amphiphilic pseudo k nucleotide composition(Di) (Series) for each sequence.

Usage

APkNUCdi_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of dinucleotides. We have provided users with the ability to choose among the 148 properties in the di-nucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-APkNUCdi_DNA(seqs=fileLNC,ORF=TRUE,threshold=1)

Amphiphilic Pseudo-k riboNucleotide Composition-di(series) (APkNUCdi_RNA)

Description

This function calculates the amphiphilic pseudo k ribonucleotide composition(Di) (Series) for each sequence.

Usage

APkNUCdi_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo ribonucleotide composition for each physicochemical property of di-ribonucleotides. We have provided users with the ability to choose among the 22 properties in the di-ribonucleotide index database.

Value

It is a feature matrix. The number of columns is 4^l+(number of the chosen indices*lambda) and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-APkNUCdi_RNA(seqs=fileLNC,ORF=TRUE,threshold=0.8)

Accessible Solvent Accessibility (ASA)

Description

ASA represents an amino acid by a numeric value. This function extracts the ASA from the output of SPINE-X software which predicts ASA for each amino acid in a peptide or protein sequence. The output of SPINE-X is a tab-delimited file. ASAs are in the 11th column of the file.

Usage

ASA(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same lengths such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/asa.txt")


PredASAdir<-system.file("testForder",package="ftrCOOL")
PredASAdir<-paste0(PredASAdir,"/ASAdir/")
ASA(PredASAdir,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Adaptive skip dipeptide composition (ASDC)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent residues but also between intervening residues. This function calculates frequency of pair amino acids omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC(seqs, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 400 (all posible amino acid pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ASDC(seqs=filePrs)

Adaptive skip dinucleotide composition_DNA) (ASDC_DNA)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent nucleotides but also between intervening nucleotides This function calculates frequency of pair nucleotides omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16 (All posible nucleotide pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
fileLNC<-fa.read(file=fileLNC,alphabet="dna")[1:5]
mat1<-ASDC_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Adaptive skip di-ribonucleotide composition) (ASDC_RNA)

Description

This descriptor sufficiently considers the correlation information present not only between adjacent ribo ribonucleotides but also between intervening nucleotides This function calculates frequency of pair ribonucleotides omitting gaps between them. Then this function normalizes each value through dividing each frequency by summition(frequencies).

Usage

ASDC_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16 (All posible ribonucleotide pairs).

References

Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
fileLNC<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")
mat1<-ASDC_RNA(seqs=fileLNC)

Di Nucleotide Autocorrelation-Autocovariance (AutoCorDiNUC_DNA)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorDiNUC_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")

mat2<-AutoCorDiNUC_DNA(seqs=fileLNC,selectedIdx=list(10,c(1,3),6:13,c(2:7))
,maxlag=15,type="CC")

Di riboNucleotide Autocorrelation-Autocovariance (AutoCorDiNUC_RNA)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorDiNUC_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
fileLNC<-fa.read(fileLNC,alphabet="rna")
fileLNC<-fileLNC[1:20]
mat1<-AutoCorDiNUC_RNA(seqs=fileLNC,maxlag=20,type=c("Moran"))

Tri Nucleotide Autocorrelation-Autocovariance (AutoCorTriNUC_DNA)

Description

It creates the feature matrix for each function in autocorelation (i.e., Moran, Greay, NormalizeMBorto) or autocovariance (i.e., AC, CC,ACC). The user can select any combination of the functions too. In this case, the final matrix will contain features of each selected function.

Usage

AutoCorTriNUC_DNA(
  seqs,
  selectedNucIdx = c("Dnase I", "Bendability (DNAse)"),
  maxlag = 3,
  threshold = 1,
  type = c("Moran", "Geary", "NormalizeMBorto", "AC", "CC", "ACC"),
  label = c()
)

Arguments

Details

For CC and AAC autocovriance functions, which consider the covariance of the two physicochemical properties, we have provided users with the ability to categorize their selected properties in a list. The binary combination of each group will be taken into account. Note: If all the features are in a group or selectedAAidx parameter is a vector, the binary combination will be calculated for all the physicochemical properties.

Value

This function returns a feature matrix. The number of columns in the matrix changes depending on the chosen autocorrelation or autocovariance types and nlag parameter. The output is a matrix. The number of rows shows the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat1<-AutoCorTriNUC_DNA(seqs=fileLNC,selectedNucIdx=c(1:7),maxlag=20,type=c("Moran","Geary"))

mat2<-AutoCorTriNUC_DNA(seqs=fileLNC,selectedNucIdx=list(c(1,3),6:10,c(2:7)),
maxlag=15,type=c("AC","CC"))

Blosum62 (BLOSUM62)

Description

This function creates a 20-dimentional numeric vector for each amino acid of a sequence. Each entry of the vector contains the similarity score of the amino acid with other amino acids including itself. The score is extracted from the Blosum62 matrix.

Usage

BLOSUM62(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*20 and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
filePr<-system.file("extdata/protein.fasta",package="ftrCOOL")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

ad<-paste0(dir,"/blosum62.txt")
vect<-BLOSUM62(seqs = filePr,outFormat="mat")
BLOSUM62(seqs = filePrs,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Composition_Transition_Distribution (CTD)

Description

This function calculates the composition, transition, and distribution for each sequence.

Usage

CTD(seqs, normalized = FALSE, label = c())

Arguments

Value

Output is a combination of three different matrices: Composition, Transition, and Distribution. You can obtain any of the three matrices by executing the corresponding function, i.e., CTDC, CTDT, and CTDD.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTDtotal<-CTD(seqs=filePrs,normalized=FALSE)

CTD Composition (CTDC)

Description

This function computes the composition part of CTD. Thirteen properties are defined in this function. Each property categorizes the amino acids of the sequences into three groups. The grouped amino acid composition is calculated for each property. For more information, please check the references.

Usage

CTDC(seqs, normalized = FALSE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 3*7, where three is the number of groups and thirteen is the number of properties.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_C<-CTDC(seqs=filePrs,normalized=FALSE,label=c())

CTD Distribution (CTDD)

Description

This function computes the distribution part of CTD. It calculates fifteen values for each property. For more information, please check the references.

Usage

CTDD(seqs, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 15*7.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_D<-CTDD(seqs=filePrs)

CTD Transition (CTDT)

Description

This function computes the transition part of CTD. Thirteen properties are defined in this function. Each property categorizes the amino acids of a sequence into three groups. For each property, the grouped amino acid transition (i.e., transitions 1-2, 1-3, and 2-3) is calculated. For more information, please check the references.

Usage

CTDT(seqs, normalized = FALSE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 3*7, where three is the number of transition types (i.e., 1-2, 1-3, and 2-3) and thirteen is the number of properties.

References

Dubchak, Inna, et al. "Prediction of protein folding class using global description of amino acid sequence." Proceedings of the National Academy of Sciences 92.19 (1995): 8700-8704.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
CTD_T<-CTDT(seqs=filePrs,normalized=FALSE)

Composition of k-Spaced Amino Acids pairs (CkSAApair)

Description

This function calculates the composition of k-spaced amino acid pairs. In other words, it computes the frequency of all amino acid pairs with k spaces.

Usage

CkSAApair(seqs, rng = 3, upto = FALSE, normalized = TRUE, label = c())

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 400*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSAApair(seqs=filePrs,rng=2,upto=TRUE,normalized=TRUE)

mat2<-CkSAApair(seqs=filePrs,rng=c(1,3,5))

Composition of k-Spaced Grouped Amino Acids pairs (CkSGAApair)

Description

In this function, amino acids are first grouped into a category which is defined by the user. Later, the composition of the k-spaced grouped amino acids is computed. Please note that this function differs from CkSAApair which works on individual amino acids.

Usage

CkSGAApair(
  seqs,
  rng = 3,
  upto = FALSE,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Details

Column names in the feature matrix follow G(?ss?). For example, G(1ss2) means Group1**Group2, where '*' is a wild character.

Value

This function returns a feature matrix. Row length is equal to the number of sequences and the number of columns is ((number of categorizes)^2)*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSGAApair(seqs=filePrs,rng=2,upto=TRUE,Grp="aromatic")

mat2<-CkSGAApair(seqs=filePrs,rng=c(1,3,5),upto=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Composition of k-Spaced Nucleotides Pairs (CkSNUCpair_DNA)

Description

This function calculates the composition of k-spaced nucleotide pairs. In other words, it computes the frequency of all nucleotide pairs with k spaces.

Usage

CkSNUCpair_DNA(
  seqs,
  rng = 3,
  upto = FALSE,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat1<-CkSNUCpair_DNA(seqs=fileLNC,rng=2,upto=TRUE,ORF=TRUE,reverseORF=FALSE)
mat2<-CkSNUCpair_DNA(seqs=fileLNC,rng=c(1,3,5))

Composition of k-Spaced riboNucleotides Pairs (CkSNUCpair_RNA)

Description

This function calculates the composition of k-spaced ribonucleotide pairs. In other words, it computes the frequency of all ribonucleotide pairs with k spaces.

Usage

CkSNUCpair_RNA(
  seqs,
  rng = 3,
  upto = FALSE,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 16*(length of rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat1<-CkSNUCpair_RNA(seqs=fileLNC,rng=2,upto=TRUE,ORF=TRUE,reverseORF=FALSE)
mat2<-CkSNUCpair_RNA(seqs=fileLNC,rng=c(1,3,5))

Codon Fraction (CodonFraction)

Description

This function calculates the codon fraction for each sequence.

Usage

CodonFraction(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-CodonFraction(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Codon Usage in DNA (CodonUsage_DNA)

Description

This function calculates the codon usage for each sequence.

Usage

CodonUsage_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-CodonUsage_DNA(fileLNC,ORF=TRUE,reverseORF=FALSE)

Codon Usage in RNA (CodonUsage_RNA)

Description

This function calculates the codon usage for each sequence.

Usage

CodonUsage_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

A feature matrix such that the number of columns is 4^3 and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-CodonUsage_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Dipeptide Deviation from Expected Mean value (DDE)

Description

This function computes the dipeptide deviation from the expected mean value.

Usage

DDE(seqs, label = c())

Arguments

Value

A feature matrix with 20^2=400 number of columns. The number of rows is equal to the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-DDE(seqs=filePrs)

Dinucleotide physicochemical properties (DPCP_DNA)

Description

This function replaces dinucleotides in a sequence with their physicochemical properties which is multiplied by normalized frequency of that di-nucleotide.

Usage

DPCP_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 148 physicochemical indexes in the dinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-nucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-DPCP_DNA(seqs = fileLNC,outFormat="mat")

Di-ribonucleotide physicochemical properties (DPCP_RNA)

Description

This function replaces di-ribonucleotides in a sequence with their physicochemical properties which is multiplied by normalized frequency of that di-ribonucleotide.

Usage

DPCP_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 22 physicochemical indexes in the di-ribonucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-ribonucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-DPCP_RNA(seqs = fileLNC,outFormat="mat")

Dinucleotide To Binary DNA (DiNUC2Binary_DNA)

Description

This function transforms a dinucleotide to a binary number with four bits which is enough to represent all the possible types of dinucleotides. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

DiNUC2Binary_DNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the (length of the sequences-1). Otherwise, it is equal to (length of the sequences-1)*4. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-DiNUC2Binary_DNA(seqs = LNC50Nuc, binaryType="numBin",outFormat="mat")

Di riboNucleotide To Binary RNA (DiNUC2Binary_RNA)

Description

This function transforms a di-ribonucleotide to a binary number with four bits which is enough to represent all the possible types of di-ribonucleotides. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

DiNUC2Binary_RNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is (length of the sequences-1). Otherwise, it is equal to (length of the sequences-1)*4. If outFormat is 'txt', all binary values will be written to a 'txt' file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-DiNUC2Binary_RNA(seqs = fileLNC, binaryType="numBin",outFormat="mat")

Di Nucleotide Index (DiNUCindex_DNA)

Description

This function replaces dinucleotides in a sequence with their physicochemical properties in the dinucleotide index file.

Usage

DiNUCindex_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 148 physicochemical indexes in the dinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-nucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-DiNUCindex_DNA(seqs = fileLNC,outFormat="mat")

Di riboNucleotide Index (DiNUCindex_RNA)

Description

This function replaces di-ribonucleotides in a sequence with their physicochemical properties in the di-ribonucleotide index file.

Usage

DiNUCindex_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 22 physicochemical indexes in the di-ribonucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-1)*(number of selected di-ribonucleotide indexes) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-DiNUCindex_RNA(seqs = fileLNC,outFormat="mat")

disorder Binary (DisorderB)

Description

This function extracts the ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. Also, the function converts order amino acids to '10' and disorder amino acids to '01'.

Usage

DisorderB(
  dirPath,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*2. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()

PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")
ad1<-paste0(dir,"/disorderB.txt")

DisorderB(PredDisdir,binaryType="numBin",outFormat="txt",outputFileDist=ad1)

unlink("dir", recursive = TRUE)

disorder Content (DisorderC)

Description

This function extracts ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. Also, the function returns number of order and disorder amino acids in the sequence.

Usage

DisorderC(dirPath)

Arguments

Value

The output is a feature matrix with 2 columns. The number of rows is equal to the number of sequences.

Examples

dir = tempdir()
PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")

mat<-DisorderC(PredDisdir)

disorder Simple (DisorderS)

Description

This function extracts ordered and disordered amino acids in protein or peptide sequences. The input to the function is provided by VSL2 software. The function represent order amino acids by 'O' and disorder amino acids by 'D'.

Usage

DisorderS(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same lengths such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()

PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")
ad1<-paste0(dir,"/disorderS.txt")

DisorderS(PredDisdir, outFormat="txt",outputFileDist=ad1)

unlink("dir", recursive = TRUE)

PseAAC of distance-pairs and reduced alphabet (DistancePair)

Description

In this function, first amino acids are grouped into a category which is one of 'cp13', 'cp14', 'cp19', 'cp20'. Users choose one of these terms to categorize amino acids. Then DistancePair function computes frequencies of all grouped residues and also all grouped-paired residues with [0,rng] distance. 'rng' is a parameter which already was set by the user.

Usage

DistancePair(seqs, rng = 3, normalized = TRUE, Grp = "cp14", label = c())

Arguments

Value

This function returns a feature matrix. Row length is equal to the number of sequences and the number of columns is (number of categorizes)+((number of categorizes)^2)*(rng+1).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-DistancePair(seqs=filePrs,rng=2,Grp="cp14")

Enhanced Amino Acid Composition (EAAComposition)

Description

This function slides a window over the input sequence(s). Also, it computes the composition of amino acids that appears within the limits of the window.

Usage

EAAComposition(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

Column names in the output matrix are Wi(aa), where aa shows an amino acid type ("A", "C", "D",..., "Y") and i indicates the number of times that the window has moved over the sequence(s).

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (20 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

When overlap is FALSE, the last partition represented by the window may have a different length with other parts.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-EAAComposition(seqs = ptmSeqsVect,winSize=50, overLap=FALSE,outFormat='mat')

ad<-paste0(dir,"/EaaCompos.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
EAAComposition(seqs = filePrs,winSize=50, overLap=FALSE,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Enhanced Grouped Amino Acid Composition (EGAAComposition)

Description

In this function, amino acids are first grouped into user-defined categories. Then, enhanced grouped amino acid composition is computed. For details about the enhanced feature, please refer to function EAAComposition. Please note that this function differs from function EAAComposition which works on individual amino acids.

Usage

EGAAComposition(
  seqs,
  winSize = 50,
  overLap = TRUE,
  Grp = "locFus",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is ((number of categorizes) * (number of windows)) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat1<-EGAAComposition(seqs = ptmSeqsVect,winSize=20,overLap=FALSE,Grp="locFus")

mat2<-EGAAComposition(seqs = ptmSeqsVect,winSize=30,overLap=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")),outFormat="mat")

ad<-paste0(dir,"/EGrpaaCompos.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
EGAAComposition(seqs = filePrs,winSize=20,Grp="cTriad",outFormat="txt"
,outputFileDist=ad)


unlink("dir", recursive = TRUE)

Electron-Ion Interaction Pseudopotentials (EIIP)

Description

This function replaces each nucleotide in the input sequence with its electron-ion interaction value. The resulting sequence is represented by a feature vector whose length is equal to the length of the sequence. Please check the references for more information.

Usage

EIIP(seqs, outFormat = "mat", outputFileDist = "", label = c())

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-EIIP(seqs = LNC50Nuc,outFormat="mat")

Enhanced Nucleotide Composition (ENUComposition_DNA)

Description

This function slides a window over the input sequence(s). Also, it computes the composition of nucleotides that appears within the limits of the window.

Usage

ENUComposition_DNA(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (4 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-ENUComposition_DNA(seqs = LNC50Nuc, winSize=20,outFormat="mat")

ad<-paste0(dir,"/ENUCcompos.txt")
fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
ENUComposition_DNA(seqs = fileLNC,outFormat="txt",winSize=20
,outputFileDist=ad,overLap=FALSE)


unlink("dir", recursive = TRUE)

Enhanced riboNucleotide Composition (ENUComposition_RNA)

Description

This function slides a window over the input sequence(s). Also, it computes the composition of ribonucleotides that appears within the limits of the window.

Usage

ENUComposition_RNA(
  seqs,
  winSize = 50,
  overLap = TRUE,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (4 * number of partitions displayed by the window) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ENUComposition_RNA(seqs = fileLNC, winSize=20,outFormat="mat")

ad<-paste0(dir,"/ENUCcompos.txt")
ENUComposition_RNA(seqs = fileLNC,outFormat="txt",winSize=20
,outputFileDist=ad,overLap=FALSE)


unlink("dir", recursive = TRUE)

Effective Number of Codon (EffectiveNumberCodon)

Description

This function calculates the effective number of codon for each sequence.

Usage

EffectiveNumberCodon(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the effective number of codon.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-EffectiveNumberCodon(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Expected Value for K-mer Nucleotide (ExpectedValKmerNUC_DNA)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / ( freq(nucleotide1) * freq(nucleotide2) * ... * freq(nucleotidek) )

Usage

ExpectedValKmerNUC_DNA(
  seqs,
  k = 4,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (4^k).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-ExpectedValKmerNUC_DNA(seqs=fileLNC,k=4,ORF=TRUE,reverseORF=FALSE)

Expected Value for K-mer riboNucleotide (ExpectedValKmerNUC_RNA)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / ( freq(ribonucleotide1) * freq(ribonucleotide2) * ... * freq(ribonucleotidek) )

Usage

ExpectedValKmerNUC_RNA(
  seqs,
  k = 4,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (4^k).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-ExpectedValKmerNUC_RNA(seqs=fileLNC,k=4,ORF=TRUE,reverseORF=FALSE)

Expected Value for each Amino Acid (ExpectedValueAA)

Description

This function is introduced by this package for the first time. It computes the expected value for each k-mer in a sequence. ExpectedValue(k-mer) = freq(k-mer) / (c_1 * c_2 * ... * c_k), where c_i is the number of codons that encrypt the i'th amino acid in the k-mer.

Usage

ExpectedValueAA(seqs, k = 2, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 20^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ExpectedValueAA(seqs=filePrs,k=2,normalized=FALSE)

Expected Value for Grouped Amino Acid (ExpectedValueGAA)

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, the expected value of grouped amino acids is computed. Please note that this function differs from Function ExpectedValueAA which works on individual amino acids.

Usage

ExpectedValueGAA(seqs, k = 3, Grp = "locFus", normalized = TRUE, label = c())

Arguments

Details

for more information about ExpectedValueGAA, please refer to function ExpectedValueKmer.

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (number of categories)^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-ExpectedValueGAA(seqs=filePrs,k=2,Grp="locFus")

mat2<-ExpectedValueGAA(seqs=filePrs,k=1,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Expected Value for Grouped K-mer Amino Acid(ExpectedValueGKmerAA)

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, the expected value of grouped k-mer is computed. Please note that this function differs from Function ExpectedValueKmerAA which works on individual amino acids.

Usage

ExpectedValueGKmerAA(
  seqs,
  k = 2,
  Grp = "locFus",
  normalized = TRUE,
  label = c()
)

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (number of categorizes)^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-ExpectedValueGKmerAA(seqs=filePrs,k=2,Grp="locFus")

mat2<-ExpectedValueGKmerAA(seqs=filePrs,k=1,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

Expected Value for K-mer Amino Acid (ExpectedValueKmerAA)

Description

This function computes the expected value of each k-mer by dividing the frequency of the kmer to multiplying frequency of each amino acid of the k-mer in the sequence.

Usage

ExpectedValueKmerAA(seqs, k = 2, normalized = TRUE, label = c())

Arguments

Details

ExpectedValue(k-mer) = freq(k-mer) / ( freq(aminoacid1) * freq(aminoacid2) * ... * freq(aminoacidk) )

Value

This function returns a feature matrix. The number of rows equals the number of sequences and the number of columns if upto set false, is 20^k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-ExpectedValueKmerAA(filePrs,k=2,normalized=FALSE)

Grouped Amino Acid K Part Composition (GAAKpartComposition)

Description

In this function, amino acids are first grouped into user-defined categories. Later, the composition of the grouped amino acid k part is computed. Please note that this function differs from AAKpartComposition which works on individual amino acids.

Usage

GAAKpartComposition(
  seqs,
  k = 5,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Value

a feature matrix with k*(number of categorizes) number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-GAAKpartComposition(seqs=filePrs,k=5,Grp="aromatic")

mat2<-GAAKpartComposition(seqs=filePrs,k=3,normalized=FALSE,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

G_C content in DNA (G_Ccontent_DNA)

Description

This function calculates G-C content of each sequence.

Usage

G_Ccontent_DNA(
  seqs,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains G-C content of a sequence.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-G_Ccontent_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

G_C content in RNA (G_Ccontent_RNA)

Description

This function calculates G-C content of each sequence.

Usage

G_Ccontent_RNA(
  seqs,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains G-C content of a sequence.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-G_Ccontent_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Group Dipeptide Deviation from Expected Mean (GrpDDE)

Description

This function is introduced by this package for the first time. In this function, amino acids are first grouped into user-defined categories. Later, DDE is applied to grouped amino acids. Please note that this function differs from DDE which works on individual amino acids.

Usage

GrpDDE(seqs, Grp = "locFus", label = c())

Arguments

Value

A feature matrix with (number of categorizes)^2 number of columns. The number of rows is equal to the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-GrpDDE(seqs=filePrs,Grp="aromatic")

mat2<-GrpDDE(seqs=filePrs,Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

K-Nearest Neighbor for Peptides (KNNPeptide)

Description

This function needs an extra training data set and a label. We compute the similarity score of each input sequence with all sequences in the training data set. We use the BLOSUM62 matrix to compute the similarity score. The label shows the class of each sequence in the training data set. KNNPeptide finds the label of 1 It reports the frequency of each class for each k

Usage

KNNPeptide(seqs, trainSeq, percent = 30, label = c(), labeltr = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

Note

This function is usable for amino acid sequences with the same length in both training data set and the set of sequences.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])

posSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/poSeqPTM101.csv"))[,2])
negSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/negSeqPTM101.csv"))[,2])

posSeqs<-posSeqs[1:10]
negSeqs<-negSeqs[1:10]

trainSeq<-c(posSeqs,negSeqs)

labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNNPeptide(seqs=ptmSeqsVect,trainSeq=trainSeq,percent=10,labeltr=labeltr)

K-Nearest Neighbor for Protein (KNNProtein)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNNProtein(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
ptmSeqsVect<-ptmSeqsVect[1:2]
ptmSeqsVect<-sapply(ptmSeqsVect,function(seq){substr(seq,1,31)})

posSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/poSeqPTM101.csv"))[,2])
negSeqs<-as.vector(read.csv(paste0(ptmSeqsADR,"/negSeqPTM101.csv"))[,2])

posSeqs<-posSeqs[1:3]
negSeqs<-negSeqs[1:3]

posSeqs<-sapply(posSeqs,function(seq){substr(seq,1,31)})
negSeqs<-sapply(negSeqs,function(seq){substr(seq,1,31)})

trainSeq<-c(posSeqs,negSeqs)

labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

mat<-KNNProtein(seqs=ptmSeqsVect,trainSeq=trainSeq,percent=5,labeltr=labeltr)

K-Nearest Neighbor_DNA (KNN_DNA)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNN_DNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Chen, Zhen, et al. "iFeature: a python package and web server for features extraction and selection from protein and peptide sequences." Bioinformatics 34.14 (2018): 2499-2502.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testData51.txt"),alphabet="dna")

posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/posData51.txt"),alphabet="dna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/negData51.txt"),alphabet="dna")


trainSeq<-c(posSeqs,negSeqs)

labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_DNA(seqs=seqs,trainSeq=trainSeq,percent=5,labeltr=labeltr)

K-Nearest Neighbor_RNA (KNN_RNA)

Description

This function is like KNNPeptide with the difference that similarity score is computed by Needleman-Wunsch algorithm.

Usage

KNN_RNA(seqs, trainSeq, percent = 30, labeltr = c(), label = c())

Arguments

Value

This function returns a feature matrix such that number of columns is number of classes multiplied by percent and number of rows is equal to the number of the sequences.

References

Wei,L., Su,R., Luan,S., Liao,Z., Manavalan,B., Zou,Q. and Shi,X. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics, (2019).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/pos2RNA51.txt"),alphabet="rna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/neg2RNA51.txt"),alphabet="rna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA51.txt"),alphabet="rna")


trainSeq<-c(posSeqs,negSeqs)


labelPos<-rep(1,length(posSeqs))
labelNeg<-rep(0,length(negSeqs))

labeltr<-c(labelPos,labelNeg)

KNN_RNA(seqs=seqs,trainSeq=trainSeq,percent=10,labeltr=labeltr)

Local Position Specific k Amino Acids Frequency (LocalPoSpKAAF)

Description

For each sequence, this function creates a feature vector denoted as (f1,f2, f3, …, fN), where fi = freq(i'th k-mer of the sequence) / i. It should be applied to sequences with the same length.

Usage

LocalPoSpKAAF(seqs, k = 2, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-k+1) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-LocalPoSpKAAF(seqs = ptmSeqsVect, k=2,outFormat="mat")

ad<-paste0(dir,"/LocalPoSpKaaF.txt")
filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
LocalPoSpKAAF(seqs = filePrs, k=1,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Local Position Specific k Nucleotide Frequency (LocalPoSpKNUCF_DNA)

Description

For each sequence, this function creates a feature vector denoted as (f1,f2, f3, …, fN), where fi = freq(i'th k-mer of the sequence) / i. It should be applied to sequences with the same length.

Usage

LocalPoSpKNUCF_DNA(
  seqs,
  k = 2,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-k+1) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-LocalPoSpKNUCF_DNA(seqs = LNC50Nuc, k=2,outFormat="mat")

ad<-paste0(dir,"/LocalPoSpKnucF.txt")
fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
LocalPoSpKNUCF_DNA(seqs = fileLNC,k=1,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Local Position Specific k riboNucleotide Frequency (LocalPoSpKNUCF_RNA)

Description

For each sequence, this function creates a feature vector denoted as (f1,f2, f3, …, fN), where fi = freq(i'th k-mer of the sequence) / i. It should be applied to sequences with the same length.

Usage

LocalPoSpKNUCF_RNA(
  seqs,
  k = 2,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-k+1) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-LocalPoSpKNUCF_RNA(seqs = fileLNC, k=2,outFormat="mat")

ad<-paste0(dir,"/LocalPoSpKnucF.txt")
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
LocalPoSpKNUCF_RNA(seqs = fileLNC,k=1,outFormat="txt"
,outputFileDist=ad)

unlink("dir", recursive = TRUE)

Multivariate Mutual Information_DNA (MMI_DNA)

Description

MMI computes mutual information based on 2-mers T2 = AA, AC, AG, AT, CC, CG, CT, GG, GT, TT and 3-mers T3 = AAA, AAC, AAG, AAT, ACC, ACG, ACT, AGG, AGT, ATT, CCC, CCG, CCT, CGG, CGT, CTT, GGG, GGT, GTT and TTT for more information please check the reference part.

Usage

MMI_DNA(seqs, label = c())

Arguments

Value

It is a feature matrix. The number of columns is 30 and the number of rows is equal to the number of sequences.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song. iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research (2021).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-MMI_DNA(seqs=fileLNC)

Multivariate Mutual Information_RNA (MMI_RNA)

Description

MMI computes mutual information based on 2-mers T2 = AA, AC, AG, AU, CC, CG, CU, GG, GU, U and 3-mers T3 = AAA, AAC, AAG, AAU, ACC, ACG, ACU, AGG, AGU, AUU, CCC, CCG, CCU, CGG, CGU, CUU, GGG, GGU, GUU and UUU for more information please check the reference part.

Usage

MMI_RNA(seqs, label = c())

Arguments

Value

It is a feature matrix. The number of columns is 30 and the number of rows is equal to the number of sequences.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song. iLearnPlus: a comprehensive and automated machine-learning platform for ribonucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research (2021).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-MMI_RNA(seqs=fileLNC)

Mismatch_DNA (Mismatch_DNA)

Description

This function also calculates the frequencies of all k-mers in the sequence but alows maximum m mismatch. m<k.

Usage

Mismatch_DNA(seqs, k = 3, m = 2, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on the rng vector. For each value k in the vector, (4)^k columns are created in the matrix.

References

Liu, B., Gao, X. and Zhang, H. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Res (2019).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Mismatch_DNA(seqs=fileLNC)

Mismatch_RNA (Mismatch_RNA)

Description

This function also calculates the frequencies of all k-mers in the sequence but alows maximum m mismatch. m<k.

Usage

Mismatch_RNA(seqs, k = 3, m = 2, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on the rng vector. For each value k in the vector, (4)^k columns are created in the matrix.

References

Liu, B., Gao, X. and Zhang, H. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Res (2019).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Mismatch_RNA(seqs=fileLNC)

Nucleotide Chemical Property (NCP_DNA)

Description

This function replaces nucleotides with a three-length vector. The vector represent the nucleotides such that 'A' will be replaced with c(1, 1, 1), 'C' with c(0, 1, 0),'G' with c(1, 0, 0), and 'T' with c(0, 0, 1).

Usage

NCP_DNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-NCP_DNA(seqs = LNC50Nuc,binaryType="strBin",outFormat="mat")

ad<-paste0(dir,"/NCP.txt")
fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
NCP_DNA(seqs = fileLNC,binaryType="numBin",outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

riboNucleotide Chemical Property (NCP_RNA)

Description

This function replaces ribonucleotides with a three-length vector. The vector represent the ribonucleotides such that 'A' will be replaced with c(1, 1, 1), 'C' with c(0, 1, 0),'G' with c(1, 0, 0), and 'U' with c(0, 0, 1).

Usage

NCP_RNA(
  seqs,
  binaryType = "numBin",
  outFormat = "mat",
  outputFileDist = "",
  label = c()
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

dir = tempdir()
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-NCP_RNA(seqs = fileLNC,binaryType="strBin",outFormat="mat")

ad<-paste0(dir,"/NCP.txt")
NCP_RNA(seqs = fileLNC,binaryType="numBin",outFormat="txt",outputFileDist=ad)
unlink("dir", recursive = TRUE)

Nucleotide To Binary (NUC2Binary_DNA)

Description

This function transforms a nucleotide to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

NUC2Binary_DNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*4. If outFormat is 'txt', all binary values will be written to a 'txt' file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

dir = tempdir()
LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-NUC2Binary_DNA(seqs = LNC50Nuc,outFormat="mat")

ad<-paste0(dir,"/NUC2Binary.txt")
fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
NUC2Binary_DNA(seqs = fileLNC,binaryType="numBin",outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

riboNucleotide To Binary (NUC2Binary_RNA)

Description

This function transforms a ribonucleotide to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

NUC2Binary_RNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*4. If outFormat is 'txt', all binary values will be written to a 'txt' file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

dir = tempdir()
fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-NUC2Binary_RNA(seqs = fileLNC,outFormat="mat")

ad<-paste0(dir,"/NUC2Binary.txt")
NUC2Binary_RNA(seqs = fileLNC,binaryType="numBin",outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Nucleotide to K Part Composition (NUCKpartComposition_DNA)

Description

In this function, each sequence is divided into k equal partitions. The length of each part is equal to ceiling(l(lenght of the sequence)/k). The last part can have a different length containing the residual nucleotides. The nucleotide composition is calculated for each part.

Usage

NUCKpartComposition_DNA(
  seqs,
  k = 5,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

a feature matrix with k*4 number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-NUCKpartComposition_DNA(seqs=fileLNC,k=5,ORF=TRUE,reverseORF=FALSE,normalized=FALSE)

riboNucleotide to K Part Composition (NUCKpartComposition_RNA)

Description

In this function, each sequence is divided into k equal partitions. The length of each part is equal to ceiling(l(lenght of the sequence)/k). The last part can have a different length containing the residual ribonucleotides. The ribonucleotide composition is calculated for each part.

Usage

NUCKpartComposition_RNA(
  seqs,
  k = 5,
  ORF = FALSE,
  reverseORF = TRUE,
  normalized = TRUE,
  label = c()
)

Arguments

Value

a feature matrix with k*4 number of columns. The number of rows is equal to the number of sequences.

Note

Warning: The length of all sequences should be greater than k.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-NUCKpartComposition_RNA(seqs=fileLNC,k=5,ORF=TRUE,reverseORF=FALSE,normalized=FALSE)

Overlapping Property Features_10bit (OPF_10bit)

Description

This group of functions (OPF Group) categorize amino acids in different groups based on the type. This function includes 10 amino acid properties. OPF_10bit substitutes each amino acid with a 10-dimensional vector. Each element of the vector shows if that amino acid locates in a special property category or not. '0' means that amino acid is not located in that property group and '1' means it is located.

Usage

OPF_10bit(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. Number of columns for this feature matrix is equal to (length of the sequences)*10 and number of rows is equal to the number of sequences. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Wei,L., Zhou,C., Chen,H., Song,J. and Su,R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-OPF_10bit(seqs = ptmSeqsVect,outFormat="mat")

Overlapping property features_7bit_T1 (OPF_7bit_T1)

Description

This group of functions (OPF Group) categorize amino acids in different groups based on the type. This function includes 7 amino acid properties. OPF_7bit_T1 substitutes each amino acid with a 7-dimensional vector. Each element of the vector shows if that amino acid locates in a special property category or not. '0' means that amino acid is not located in that property group and '1' means it is located. The only difference between OPF_7bit type1, type2, and type3 is in localization of amino acids in the properties groups.

Usage

OPF_7bit_T1(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. Number of columns for this feature matrix is equal to (length of the sequences)*7 and number of rows is equal to the number of sequences. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Wei,L., Zhou,C., Chen,H., Song,J. and Su,R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-OPF_7bit_T1(seqs = ptmSeqsVect,outFormat="mat")

Overlapping property features_7bit_T2 (OPF_7bit_T2)

Description

This group of functions (OPF Group) categorize amino acids in different groups based on the type. This function includes 7 amino acid properties. OPF_7bit_T2 substitutes each amino acid with a 7-dimensional vector. Each element of the vector shows if that amino acid locates in a special property category or not. '0' means that amino acid is not located in that property group and '1' means it is located. The only difference between OPF_7bit type1, type2, and type3 is in localization of amino acids in the properties groups.

Usage

OPF_7bit_T2(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. Number of columns for this feature matrix is equal to (length of the sequences)*7 and number of rows is equal to the number of sequences. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Wei,L., Zhou,C., Chen,H., Song,J. and Su,R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-OPF_7bit_T2(seqs = ptmSeqsVect,outFormat="mat")

Overlapping property features_7bit_T3 (OPF_7bit_T3)

Description

This group of functions (OPF Group) categorize amino acids in different groups based on the type. This function includes 7 amino acid properties. OPF_7bit_T3 substitutes each amino acid with a 7-dimensional vector. Each element of the vector shows if that amino acid locates in a special property category or not. '0' means that amino acid is not located in that property group and '1' means it is located. The only difference between OPF_7bit type1, type2, and type3 is in localization of amino acids in the properties groups.

Usage

OPF_7bit_T3(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. Number of columns for this feature matrix is equal to (length of the sequences)*7 and number of rows is equal to the number of sequences. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Wei,L., Zhou,C., Chen,H., Song,J. and Su,R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics (2018).

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-OPF_7bit_T3(seqs = ptmSeqsVect,outFormat="mat")

Parallel Correlation Pseudo Dinucleotide Composition (PCPseDNC)

Description

This function works like PSEkNUCdi_DNA except that the default value of selectedIdx parameter is different.

Usage

PCPseDNC(
  seqs,
  selectedIdx = c("Base stacking", "Protein induced deformability", "B-DNA twist",
    "A-philicity", "Propeller twist", "Duplex stability:(freeenergy)",
    "DNA denaturation", "Bending stiffness", "Protein DNA twist", "Aida_BA_transition",
    "Breslauer_dG", "Breslauer_dH", "Electron_interaction", "Hartman_trans_free_energy",
    "Helix-Coil_transition", "Lisser_BZ_transition", "Polar_interaction",
    "SantaLucia_dG", "SantaLucia_dS", "Sarai_flexibility", "Stability", "Sugimoto_dG",
    "Sugimoto_dH", "Sugimoto_dS", "Duplex tability(disruptenergy)",     
    "Stabilising energy of Z-DNA", "Breslauer_dS", "Ivanov_BA_transition",
    "SantaLucia_dH", "Stacking_energy", "Watson-Crick_interaction",
    "Dinucleotide GC Content", "Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of di-nucleotides. We have provided users with the ability to choose among the 148 properties in the di-nucleotide index database.

Value

a feature matrix such that the number of columns is 4^l+lambda and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-PSEkNUCdi_DNA(seqs=fileLNC,l=2,ORF=TRUE,threshold=0.8)

Position-specific of two nucleotide_DNA (PS2_DNA)

Description

This function transforms each di-nucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS2_DNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-1)*16. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-PS2_DNA(seqs = LNC50Nuc,outFormat="mat")

Position-specific of two nucleotide_RNA (PS2_RNA)

Description

This function transforms each di-ribonucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS2_RNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-1)*16. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-PS2_RNA(seqs = fileLNC, binaryType="numBin",outFormat="mat")

Position-specific of three nucleotide_DNA (PS3_DNA)

Description

This function transforms each tri-nucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS3_DNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-2)*64. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-PS3_DNA(seqs = LNC50Nuc,outFormat="mat")

Position-specific of three ribonucleotide_RNA (PS3_RNA)

Description

This function transforms each tri-ribonucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS3_RNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-2)*64. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-PS3_RNA(seqs = fileLNC, binaryType="numBin",outFormat="mat")

Position-specific of four nucleotide_DNA (PS4_DNA)

Description

This function transforms each 4-nucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS4_DNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-3)*256. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-PS4_DNA(seqs = LNC50Nuc,outFormat="mat")

Position-specific of four ribonucleotide (PS4_RNA)

Description

This function transforms each 4-ribonucleotide of the sequence to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

PS4_RNA(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences-3)*256. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

References

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, Nucleic Acids Research, (2021).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-PS4_RNA(seqs = fileLNC, binaryType="numBin",outFormat="mat")

Pseudo-Amino Acid Composition (Parallel) (PSEAAC)

Description

This function calculates the pseudo amino acid composition (parallel) for each sequence.

Usage

PSEAAC(
  seqs,
  aaIDX = c("ARGP820101", "HOPT810101", "Mass"),
  lambda = 30,
  w = 0.05,
  l = 1,
  threshold = 1,
  label = c()
)

Arguments

Value

A feature matrix such that the number of columns is 20^l+(lambda) and the number of rows is equal to the number of sequences.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-PSEAAC(seqs=filePrs,l=2)

Pseudo k Nucleotide Composition-Tri(Parallel) (PSEkNUCTri_RNA)

Description

This function calculates pseudo-k nucleotide composition(Tri) (Parallel) for each sequence.

Usage

PSEkNUCTri_DNA(
  seqs,
  selectedIdx = c("Dnase I", "Bendability (DNAse)"),
  lambda = 3,
  w = 0.05,
  l = 3,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of trinucleotides. We have provided users with the ability to choose among the 12 properties in the tri-nucleotide index database.

Value

a feature matrix such that the number of columns is 4^l+lambda and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-PSEkNUCTri_DNA(seqs=fileLNC, l=2,ORF=TRUE,threshold=0.8)

Pseudo k Nucleotide Composition-Di(Parallel) (PSEkNUCdi_DNA)

Description

This function calculates the pseudo-k nucleotide composition(Di) (Parallel) for each sequence.

Usage

PSEkNUCdi_DNA(
  seqs,
  selectedIdx = c("Rise", "Roll", "Shift", "Slide", "Tilt", "Twist"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo nucleotide composition for each physicochemical property of di-nucleotides. We have provided users with the ability to choose among the 148 properties in the di-nucleotide index database.

Value

a feature matrix such that the number of columns is 4^l+lambda and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-PSEkNUCdi_DNA(seqs=fileLNC,l=2,ORF=TRUE,threshold=0.8)

Pseudo k riboNucleotide Composition-Di(Parallel) (PSEkNUCdi_RNA)

Description

This function calculates the pseudo-k ribonucleotide composition(Di) (Parallel) for each sequence.

Usage

PSEkNUCdi_RNA(
  seqs,
  selectedIdx = c("Rise (RNA)", "Roll (RNA)", "Shift (RNA)", "Slide (RNA)",
    "Tilt (RNA)", "Twist (RNA)"),
  lambda = 3,
  w = 0.05,
  l = 2,
  ORF = FALSE,
  reverseORF = TRUE,
  threshold = 1,
  label = c()
)

Arguments

Details

This function computes the pseudo ribonucleotide composition for each physicochemical property of di-ribonucleotides. We have provided users with the ability to choose among the 22 properties in the di-ribonucleotide index database.

Value

a feature matrix such that the number of columns is 4^l+lambda and the number of rows is equal to the number of sequences.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-PSEkNUCdi_RNA(seqs=fileLNC,l=2,ORF=TRUE,threshold=0.8)

Position-Specific Scoring Matrix (PSSM)

Description

This functions receives as input PSSM matrices (which are created by PSI-BLAST software) and converts them into feature vectors.

Usage

PSSM(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(20) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/pssm.txt")

PSSMdir<-system.file("testForder",package="ftrCOOL")
PSSMdir<-paste0(PSSMdir,"/PSSMdir/")
mat<-PSSM(PSSMdir,outFormat="txt",outputFileDist=ad)


unlink("dir", recursive = TRUE)

Position-Specific Trinucleotide Propensity based on double-strand (PSTNPds)

Description

This function works like PSTNPss_DNA except that it considers T as A and G as C. So it converts Ts in the sequence to A and Gs to C. Then, it works with 2 alphabets A and C. For more details refer to PSTNPss_DNA.

Usage

PSTNPds(seqs, pos, neg, label = c())

Arguments

Value

It returns a feature matrix. The number of columns is equal to the length of sequences minus two and the number of rows is equal to the number of sequences.

Note

The length of the sequences in positive and negative data sets and the input sets should be equal.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")

posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/posData.txt"),alphabet="dna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/negData.txt"),alphabet="dna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testData.txt"),alphabet="dna")


PSTNPds(seqs=seqs,pos=posSeqs[1],neg=negSeqs[1])

Position-Specific Trinucleotide Propensity based on single-strand DNA (PSTNPss_DNA)

Description

The inputs to this function are positive and negative data sets and a set of sequences. The output of the function is a matrix of feature vectors. The number of rows of the output matrix is equal to the number of sequences. The feature vector for an input sequence with length L is [u(1),u(2),...u(L-2)]. For each input sequence, u(1) is calculated by subtracting the frequency of sequences (which start with the same trinucleotides as the input sequence) in the positive set with those starting with the same trinucleotide in the negative set. We compute u(i) like u(1) with the exception that instead of the first trinucleotide, the ith trinucletide is considered.

Usage

PSTNPss_DNA(seqs, pos, neg, label = c())

Arguments

Value

It returns a feature matrix. The number of columns is equal to the length of sequences minus two and the number of rows is equal to the number of sequences.

Note

The length of the sequences in positive and negative data sets and the input sets should be equal.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")

posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/posDNA.txt"),alphabet="dna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/negDNA.txt"),alphabet="dna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/DNA_testing.txt"),alphabet="dna")


mat=PSTNPss_DNA(seqs=seqs,pos=posSeqs,neg=negSeqs)

Position-Specific Tri-ribonucleotide Propensity based on single-strand RNA (PSTNPss_RNA)

Description

The inputs to this function are positive and negative data sets and a set of sequences. The output of the function is a matrix of feature vectors. The number of rows of the output matrix is equal to the number of sequences. The feature vector for an input sequence with length L is [u(1),u(2),...u(L-2)]. For each input sequence, u(1) is calculated by subtracting the frequency of sequences (which start with the same tri-ribonucleotides as the input sequence) in the positive set with those starting with the same tri-ribonucleotide in the negative set. We compute u(i) like u(1) with the exception that instead of the first tri-ribonucleotide, the ith tri-ribonucletide is considered.

Usage

PSTNPss_RNA(seqs, pos, neg, label = c())

Arguments

Value

It returns a feature matrix. The number of columns is equal to the length of sequences minus two and the number of rows is equal to the number of sequences.

Note

The length of the sequences in positive and negative data sets and the input sets should be equal.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")


posSeqs<-fa.read(file=paste0(ptmSeqsADR,"/pos2RNA.txt"),alphabet="rna")
negSeqs<-fa.read(file=paste0(ptmSeqsADR,"/neg2RNA.txt"),alphabet="rna")
seqs<-fa.read(file=paste0(ptmSeqsADR,"/testSeq2RNA.txt"),alphabet="rna")


PSTNPss_RNA(seqs=seqs,pos=posSeqs,neg=negSeqs)

Pseudo Electron-Ion Interaction Pseudopotentials of Trinucleotide (PseEIIP)

Description

This function calculates the pseudo electron-ion interaction for each sequence. It creates a feature vector for each sequence. The vector contains a value for each for each tri-nucleotide. The value is computed by multiplying the aggregate value of electron-ion interaction of each nucleotide

Usage

PseEIIP(seqs, label = c())

Arguments

Value

This function returns a feature matrix which the number of rows is equal to the number of sequences and the number of columns is 4^3=64.

References

Chen, Zhen, et al. "iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data." Briefings in bioinformatics 21.3 (2020): 1047-1057.

Examples

LNCSeqsADR<-system.file("extdata/",package="ftrCOOL")
LNC50Nuc<-as.vector(read.csv(paste0(LNCSeqsADR,"/LNC50Nuc.csv"))[,2])
mat<-PseEIIP(seqs = LNC50Nuc)

Pseudo K_tuple Reduced Amino Acid Composition Type-1 (PseKRAAC_T1)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type1(PseKRAAC_T1) contains Grp 2 to 20.

Usage

PseKRAAC_T1(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 2,
  label = c()
)

Arguments

Details

Groups: 2=c("CMFILVWY", "AGTSNQDEHRKP"), 3=c("CMFILVWY", "AGTSP", "NQDEHRK"), 4=c("CMFWY", "ILV", "AGTS", "NQDEHRKP"), 5=c("WFYH", "MILV", "CATSP", "G", "NQDERK"), 6=c("WFYH", "MILV", "CATS", "P", "G", "NQDERK"), 7=c("WFYH", "MILV", "CATS", "P", "G", "NQDE", "RK"), 8=c("WFYH", "MILV", "CA", "NTS", "P", "G", "DE", "QRK"), 9=c("WFYH", "MI", "LV", "CA", "NTS", "P", "G", "DE", "QRK"), 10=c("WFY", "ML", "IV", "CA", "TS", "NH", "P", "G", "DE", "QRK"), 11=c("WFY", "ML", "IV", "CA", "TS", "NH", "P", "G", "D", "QE", "RK"), 12=c("WFY", "ML", "IV", "C", "A", "TS", "NH", "P", "G", "D", "QE", "RK"), 13=c("WFY", "ML", "IV", "C", "A", "T", "S", "NH", "P", "G", "D", "QE", "RK"), 14=c("WFY", "ML", "IV", "C", "A", "T", "S", "NH", "P", "G", "D", "QE", "R", "K"), 15=c("WFY", "ML", "IV", "C", "A", "T", "S", "N", "H", "P", "G", "D", "QE", "R", "K"), 16=c("W", "FY", "ML", "IV", "C", "A", "T", "S", "N", "H", "P", "G", "D", "QE", "R", "K"), 17=c("W", "FY", "ML", "IV", "C", "A", "T", "S", "N", "H", "P", "G", "D", "Q", "E", "R", "K"), 18=c("W", "FY", "M", "L", "IV", "C", "A", "T", "S", "N", "H", "P", "G", "D", "Q", "E", "R", "K"), 19=c("W", "F", "Y", "M", "L", "IV", "C", "A", "T", "S", "N", "H", "P", "G", "D", "Q", "E", "R", "K"), 20=c("W", "F", "Y", "M", "L", "I", "V", "C", "A", "T", "S", "N", "H", "P", "G", "D", "Q", "E", "R", "K")

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T1(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T1(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-10 (PseKRAAC_T10)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type10(PseKRAAC_T10) contains Grp 2-20.

Usage

PseKRAAC_T10(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 2=c('CMFILVWY', 'AGTSNQDEHRKP'), 3=c('CMFILVWY', 'AGTSP', 'NQDEHRK'), 4=c('CMFWY', 'ILV', 'AGTS', 'NQDEHRKP'), 5=c('FWYH', 'MILV', 'CATSP', 'G', 'NQDERK'), 6=c('FWYH', 'MILV', 'CATS', 'P', 'G', 'NQDERK'), 7=c('FWYH', 'MILV', 'CATS', 'P', 'G', 'NQDE', 'RK'), 8=c('FWYH', 'MILV', 'CA', 'NTS', 'P', 'G', 'DE', 'QRK'), 9=c('FWYH', 'ML', 'IV', 'CA', 'NTS', 'P', 'G', 'DE', 'QRK'), 10=c('FWY', 'ML', 'IV', 'CA', 'TS', 'NH', 'P', 'G', 'DE', 'QRK'), 11=c('FWY', 'ML', 'IV', 'CA', 'TS', 'NH', 'P', 'G', 'D', 'QE', 'RK'), 12=c('FWY', 'ML', 'IV', 'C', 'A', 'TS', 'NH', 'P', 'G', 'D', 'QE', 'RK'), 13=c('FWY', 'ML', 'IV', 'C', 'A', 'T', 'S', 'NH', 'P', 'G', 'D', 'QE', 'RK'), 14=c('FWY', 'ML', 'IV', 'C', 'A', 'T', 'S', 'NH', 'P', 'G', 'D', 'QE', 'R', 'K'), 15=c('FWY', 'ML', 'IV', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'QE', 'R', 'K'), 16=c('W', 'FY', 'ML', 'IV', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'QE', 'R', 'K'), 17=c('W', 'FY', 'ML', 'IV', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'Q', 'E', 'R', 'K'), 18=c('W', 'FY', 'M', 'L', 'IV', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'Q', 'E', 'R', 'K'), 19=c('W', 'F', 'Y', 'M', 'L', 'IV', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'Q', 'E', 'R', 'K'), 20=c('W', 'F', 'Y', 'M', 'L', 'I', 'V', 'C', 'A', 'T', 'S', 'N', 'H', 'P', 'G', 'D', 'Q', 'E', 'R', 'K')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T10(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T10(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-11 (PseKRAAC_T11)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type11(PseKRAAC_T11) contains Grp 2-20.

Usage

PseKRAAC_T11(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 2=c('CFYWMLIV', 'GPATSNHQEDRK'), 3=c('CFYWMLIV', 'GPATS', 'NHQEDRK'), 4=c('CFYW', 'MLIV', 'GPATS', 'NHQEDRK'), 5=c('CFYW', 'MLIV', 'G', 'PATS', 'NHQEDRK'), 6=c('CFYW', 'MLIV', 'G', 'P', 'ATS', 'NHQEDRK'), 7=c('CFYW', 'MLIV', 'G', 'P', 'ATS', 'NHQED', 'RK'), 8=c('CFYW', 'MLIV', 'G', 'P', 'ATS', 'NH', 'QED', 'RK'), 9=c('CFYW', 'ML', 'IV', 'G', 'P', 'ATS', 'NH', 'QED', 'RK'), 10=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'ATS', 'NH', 'QED', 'RK'), 11=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'A', 'TS', 'NH', 'QED', 'RK'), 12=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'A', 'TS', 'NH', 'QE', 'D', 'RK'), 13=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'A', 'T', 'S', 'NH', 'QE', 'D', 'RK'), 14=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'QE', 'D', 'RK'), 15=c('C', 'FYW', 'ML', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'QE', 'D', 'R', 'K'), 16=c('C', 'FY', 'W', 'ML', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'QE', 'D', 'R', 'K'), 17=c('C', 'FY', 'W', 'ML', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'Q', 'E', 'D', 'R', 'K'), 18=c('C', 'FY', 'W', 'M', 'L', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'Q', 'E', 'D', 'R', 'K'), 19=c('C', 'F', 'Y', 'W', 'M', 'L', 'IV', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'Q', 'E', 'D', 'R', 'K'), 20=c('C', 'F', 'Y', 'W', 'M', 'L', 'I', 'V', 'G', 'P', 'A', 'T', 'S', 'N', 'H', 'Q', 'E', 'D', 'R', 'K')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T11(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T11(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-12 (PseKRAAC_T12)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type12(PseKRAAC_T12) contains Grp 2-18,20.

Usage

PseKRAAC_T12(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 2=c('IVMLFWYC', 'ARNDQEGHKPST'), 3=c('IVLMFWC', 'YA', 'RNDQEGHKPST'), 4=c('IVLMFW', 'C', 'YA', 'RNDQEGHKPST'), 5=c('IVLMFW', 'C', 'YA', 'G', 'RNDQEHKPST'), 6=c('IVLMF', 'WY', 'C', 'AH', 'G', 'RNDQEKPST'), 7=c('IVLMF', 'WY', 'C', 'AH', 'GP', 'R', 'NDQEKST'), 8=c('IVLMF', 'WY', 'C', 'A', 'G', 'R', 'Q', 'NDEHKPST'), 9=c('IVLMF', 'WY', 'C', 'A', 'G', 'P', 'H', 'K', 'RNDQEST'), 10=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'RN', 'DQEKPST'), 11=c('IVLMF', 'W', 'Y', 'C', 'A', 'H', 'G', 'R', 'N', 'Q', 'DEKPST'), 12=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'T', 'RDEKPS'), 13=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'DEKST'), 14=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'DEST'), 15=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'D', 'EST'), 16=c('IVLM', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'S', 'T', 'DE'), 17=c('IVL', 'M', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'S', 'T', 'DE'), 18=c('IVL', 'M', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'S', 'T', 'D', 'E'), 20=c('I', 'V', 'L', 'M', 'F', 'W', 'Y', 'C', 'A', 'H', 'G', 'N', 'Q', 'P', 'R', 'K', 'S', 'T', 'D', 'E')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T12(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T12(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type_13 (PseKRAAC_T13)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type13(PseKRAAC_T13) contains Grp 4,12,17,20.

Usage

PseKRAAC_T13(
  seqs,
  type = "gap",
  Grp = 4,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 4=c('ADKERNTSQ', 'YFLIVMCWH', 'G', 'P'), 12=c('A', 'D', 'KER', 'N', 'TSQ', 'YF', 'LIVM', 'C', 'W', 'H', 'G', 'P'), 17=c('A', 'D', 'KE', 'R', 'N', 'T', 'S', 'Q', 'Y', 'F', 'LIV', 'M', 'C', 'W', 'H', 'G', 'P'), 20=c('A', 'D', 'K', 'E', 'R', 'N', 'T', 'S', 'Q', 'Y', 'F', 'L', 'I', 'V', 'M', 'C', 'W', 'H', 'G', 'P')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T13(seqs=filePrs,type="gap",Grp=17,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T13(seqs=filePrs,type="lambda",Grp=17,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-14 (PseKRAAC_T14)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type14(PseKRAAC_T14) contains Grp 2-20.

Usage

PseKRAAC_T14(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 2=c('ARNDCQEGHKPST', 'ILMFWYV'), 3=c('ARNDQEGHKPST', 'C', 'ILMFWYV'), 4=c('ARNDQEGHKPST', 'C', 'ILMFYV', 'W'), 5=c('AGPST', 'RNDQEHK', 'C', 'ILMFYV', 'W'), 6=c('AGPST', 'RNDQEK', 'C', 'H', 'ILMFYV', 'W'), 7=c('ANDGST', 'RQEK', 'C', 'H', 'ILMFYV', 'P', 'W'), 8=c('ANDGST', 'RQEK', 'C', 'H', 'ILMV', 'FY', 'P', 'W'), 9=c('AGST', 'RQEK', 'ND', 'C', 'H', 'ILMV', 'FY', 'P', 'W'), 10=c('AGST', 'RK', 'ND', 'C', 'QE', 'H', 'ILMV', 'FY', 'P', 'W'), 11=c('AST', 'RK', 'ND', 'C', 'QE', 'G', 'H', 'ILMV', 'FY', 'P', 'W'), 12=c('AST', 'RK', 'ND', 'C', 'QE', 'G', 'H', 'IV', 'LM', 'FY', 'P', 'W'), 13=c('AST', 'RK', 'N', 'D', 'C', 'QE', 'G', 'H', 'IV', 'LM', 'FY', 'P', 'W'), 14=c('AST', 'RK', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'LM', 'FY', 'P', 'W'), 15=c('A', 'RK', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'LM', 'FY', 'P', 'ST', 'W'), 16=c('A', 'RK', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'LM', 'F', 'P', 'ST', 'W', 'Y'), 17=c('A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'LM', 'K', 'F', 'P', 'ST', 'W', 'Y'), 18=c('A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'LM', 'K', 'F', 'P', 'S', 'T', 'W', 'Y'), 19=c('A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'IV', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y'), 20=c('A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'V', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T14(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T14(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-15 (PseKRAAC_T15)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type15(PseKRAAC_T15) contains Grp 2-16,20.

Usage

PseKRAAC_T15(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups:

Grp2=c('MFILVAW', 'CYQHPGTSNRKDE'), Grp3=c('MFILVAW', 'CYQHPGTSNRK', 'DE'), Grp4=c('MFILV', 'ACW', 'YQHPGTSNRK', 'DE'), Grp5=c('MFILV', 'ACW', 'YQHPGTSN', 'RK', 'DE'), Grp6=c('MFILV', 'A', 'C', 'WYQHPGTSN', 'RK', 'DE'), Grp7=c('MFILV', 'A', 'C', 'WYQHP', 'GTSN', 'RK', 'DE'), Grp8=c('MFILV', 'A', 'C', 'WYQHP', 'G', 'TSN', 'RK', 'DE'), Grp9=c('MF', 'ILV', 'A', 'C', 'WYQHP', 'G', 'TSN', 'RK', 'DE'), Grp10=c('MF', 'ILV', 'A', 'C', 'WYQHP', 'G', 'TSN', 'RK', 'D', 'E'), Grp11=c('MF', 'IL', 'V', 'A', 'C', 'WYQHP', 'G', 'TSN', 'RK', 'D', 'E'), Grp12=c('MF', 'IL', 'V', 'A', 'C', 'WYQHP', 'G', 'TS', 'N', 'RK', 'D', 'E'), Grp13=c('MF', 'IL', 'V', 'A', 'C', 'WYQHP', 'G', 'T', 'S', 'N', 'RK', 'D', 'E'), Grp14=c('MF', 'I', 'L', 'V', 'A', 'C', 'WYQHP', 'G', 'T', 'S', 'N', 'RK', 'D', 'E'), Grp15=c('MF', 'IL', 'V', 'A', 'C', 'WYQ', 'H', 'P', 'G', 'T', 'S', 'N', 'RK', 'D', 'E'), Grp16=c('MF', 'I', 'L', 'V', 'A', 'C', 'WYQ', 'H', 'P', 'G', 'T', 'S', 'N', 'RK', 'D', 'E'), Grp20=c('M', 'F', 'I', 'L', 'V', 'A', 'C', 'W', 'Y', 'Q', 'H', 'P', 'G', 'T', 'S', 'N', 'R', 'K', 'D', 'E')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T15(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T15(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-16 (PseKRAAC_T16)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type16(PseKRAAC_T16) contains Grp 2-16,20.

Usage

PseKRAAC_T16(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups:

2=c('IMVLFWY', 'GPCASTNHQEDRK'), 3=c('IMVLFWY', 'GPCAST', 'NHQEDRK'), 4=c('IMVLFWY', 'G', 'PCAST', 'NHQEDRK'), 5=c('IMVL', 'FWY', 'G', 'PCAST', 'NHQEDRK'), 6=c('IMVL', 'FWY', 'G', 'P', 'CAST', 'NHQEDRK'), 7=c('IMVL', 'FWY', 'G', 'P', 'CAST', 'NHQED', 'RK'), 8=c('IMV', 'L', 'FWY', 'G', 'P', 'CAST', 'NHQED', 'RK'), 9=c('IMV', 'L', 'FWY', 'G', 'P', 'C', 'AST', 'NHQED', 'RK'), 10=c('IMV', 'L', 'FWY', 'G', 'P', 'C', 'A', 'STNH', 'RKQE', 'D'), 11=c('IMV', 'L', 'FWY', 'G', 'P', 'C', 'A', 'STNH', 'RKQ', 'E', 'D'), 12=c('IMV', 'L', 'FWY', 'G', 'P', 'C', 'A', 'ST', 'N', 'HRKQ', 'E', 'D'), 13=c('IMV', 'L', 'F', 'WY', 'G', 'P', 'C', 'A', 'ST', 'N', 'HRKQ', 'E', 'D'), 14=c('IMV', 'L', 'F', 'WY', 'G', 'P', 'C', 'A', 'S', 'T', 'N', 'HRKQ', 'E', 'D'), 15=c('IMV', 'L', 'F', 'WY', 'G', 'P', 'C', 'A', 'S', 'T', 'N', 'H', 'RKQ', 'E', 'D'), 16=c('IMV', 'L', 'F', 'W', 'Y', 'G', 'P', 'C', 'A', 'S', 'T', 'N', 'H', 'RKQ', 'E', 'D'), 20=c('I', 'M', 'V', 'L', 'F', 'W', 'Y', 'G', 'P', 'C', 'A', 'S', 'T', 'N', 'H', 'R', 'K', 'Q', 'E', 'D')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T16(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T16(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-2 (PseKRAAC_T2)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type2(PseKRAAC_T2) contains Grp 2-6,8,15,20.

Usage

PseKRAAC_T2(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups:

2=c('LVIMCAGSTPFYW', 'EDNQKRH'), 3=c('LVIMCAGSTP', 'FYW', 'EDNQKRH'), 4=c('LVIMC', 'AGSTP', 'FYW', 'EDNQKRH'), 5=c('LVIMC', 'AGSTP', 'FYW', 'EDNQ', 'KRH'), 6=c('LVIM', 'AGST', 'PHC', 'FYW', 'EDNQ', 'KR'), 8=c('LVIMC', 'AG', 'ST', 'P', 'FYW', 'EDNQ', 'KR', 'H'), 15=c('LVIM', 'C', 'A', 'G', 'S', 'T', 'P', 'FY', 'W', 'E', 'D', 'N', 'Q', 'KR', 'H'), 20=c('L', 'V', 'I', 'M', 'C', 'A', 'G', 'S', 'T', 'P', 'F', 'Y', 'W', 'E', 'D', 'N', 'Q', 'K', 'R', 'H')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T2(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T2(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-3A (PseKRAAC_T3A)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type3 contain two type: type3A and type3B. 'PseKRAAC_T3A' contains Grp 2-20.

Usage

PseKRAAC_T3A(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: Grp2=c('AGSPDEQNHTKRMILFYVC', 'W'), Grp3=c('AGSPDEQNHTKRMILFYV', 'W', 'C'), Grp4=c('AGSPDEQNHTKRMIV', 'W', 'YFL', 'C'), Grp5=c('AGSPDEQNHTKR', 'W', 'YF', 'MIVL', 'C'), Grp6=c('AGSP', 'DEQNHTKR', 'W', 'YF', 'MIL', 'VC'), Grp7=c('AGP', 'DEQNH', 'TKRMIV', 'W', 'YF', 'L', 'CS'), Grp8=c('AG', 'DEQN', 'TKRMIV', 'HY', 'W', 'L', 'FP', 'CS'), Grp9=c('AG', 'P', 'DEQN', 'TKRMI', 'HY', 'W', 'F', 'L', 'VCS'), Grp10=c('AG', 'P', 'DEQN', 'TKRM', 'HY', 'W', 'F', 'I', 'L', 'VCS'), Grp11=c('AG', 'P', 'DEQN', 'TK', 'RI', 'H', 'Y', 'W', 'F', 'ML', 'VCS'), Grp12=c('FAS', 'P', 'G', 'DEQ', 'NL', 'TK', 'R', 'H', 'W', 'Y', 'IM', 'VC'), Grp13=c('FAS', 'P', 'G', 'DEQ', 'NL', 'T', 'K', 'R', 'H', 'W', 'Y', 'IM', 'VC'), Grp14=c('FA', 'P', 'G', 'T', 'DE', 'QM', 'NL', 'K', 'R', 'H', 'W', 'Y', 'IV', 'CS'), Grp15=c('FAS', 'P', 'G', 'T', 'DE', 'Q', 'NL', 'K', 'R', 'H', 'W', 'Y', 'M', 'I', 'VC'), Grp16=c('FA', 'P', 'G', 'ST', 'DE', 'Q', 'N', 'K', 'R', 'H', 'W', 'Y', 'M', 'L', 'I', 'VC'), Grp17=c('FA', 'P', 'G', 'S', 'T', 'DE', 'Q', 'N', 'K', 'R', 'H', 'W', 'Y', 'M', 'L', 'I', 'VC'), Grp18=c('FA', 'P', 'G', 'S', 'T', 'DE', 'Q', 'N', 'K', 'R', 'H', 'W', 'Y', 'M', 'L', 'I', 'V', 'C'), Grp19=c('FA', 'P', 'G', 'S', 'T', 'D', 'E', 'Q', 'N', 'K', 'R', 'H', 'W', 'Y', 'M', 'L', 'I', 'V', 'C'), Grp20=c('F', 'A', 'P', 'G', 'S', 'T', 'D', 'E', 'Q', 'N', 'K', 'R', 'H', 'W', 'Y', 'M', 'L', 'I', 'V', 'C')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T3A(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T3A(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type_3B (PseKRAAC_T3B)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type3 contain two type: type3A and type3B. 'PseKRAAC_T3B' contains Grp 2-20.

Usage

PseKRAAC_T3B(
  seqs,
  type = "gap",
  Grp = 2,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 2=c('HRKQNEDSTGPACVIM', 'LFYW'), 3=c('HRKQNEDSTGPACVIM', 'LFY', 'W'), 4=c('HRKQNEDSTGPA', 'CIV', 'MLFY', 'W'), 5=c('HRKQNEDSTGPA', 'CV', 'IML', 'FY', 'W'), 6=c('HRKQNEDSTPA', 'G', 'CV', 'IML', 'FY', 'W'), 7=c('HRKQNEDSTA', 'G', 'P', 'CV', 'IML', 'FY', 'W'), 8=c('HRKQSTA', 'NED', 'G', 'P', 'CV', 'IML', 'FY', 'W'), 9=c('HRKQ', 'NED', 'ASTG', 'P', 'C', 'IV', 'MLF', 'Y', 'W'), 10=c('RKHSA', 'Q', 'NED', 'G', 'P', 'C', 'TIV', 'MLF', 'Y', 'W'), 11=c('RKQ', 'NG', 'ED', 'AST', 'P', 'C', 'IV', 'HML', 'F', 'Y', 'W'), 12=c('RKQ', 'ED', 'NAST', 'G', 'P', 'C', 'IV', 'H', 'ML', 'F', 'Y', 'W'), 13=c('RK', 'QE', 'D', 'NG', 'HA', 'ST', 'P', 'C', 'IV', 'ML', 'F', 'Y', 'W'), 14=c('R', 'K', 'QE', 'D', 'NG', 'HA', 'ST', 'P', 'C', 'IV', 'ML', 'F', 'Y', 'W'), 15=c('R', 'K', 'QE', 'D', 'NG', 'HA', 'ST', 'P', 'C', 'IV', 'M', 'L', 'F', 'Y', 'W'), 16=c('R', 'K', 'Q', 'E', 'D', 'NG', 'HA', 'ST', 'P', 'C', 'IV', 'M', 'L', 'F', 'Y', 'W'), 17=c('R', 'K', 'Q', 'E', 'D', 'NG', 'HA', 'S', 'T', 'P', 'C', 'IV', 'M', 'L', 'F', 'Y', 'W'), 18=c('R', 'K', 'Q', 'E', 'D', 'NG', 'HA', 'S', 'T', 'P', 'C', 'I', 'V', 'M', 'L', 'F', 'Y', 'W'), 19=c('R', 'K', 'Q', 'E', 'D', 'NG', 'H', 'A', 'S', 'T', 'P', 'C', 'I', 'V', 'M', 'L', 'F', 'Y', 'W'), 20=c('R', 'K', 'Q', 'E', 'D', 'N', 'G', 'H', 'A', 'S', 'T', 'P', 'C', 'I', 'V', 'M', 'L', 'F', 'Y', 'W')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T3B(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T3B(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-4 (PseKRAAC_T4)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type4(PseKRAAC_T4) contains Grp 5,8,9,11,13,20.

Usage

PseKRAAC_T4(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 5=c('G', 'IVFYW', 'ALMEQRK', 'P', 'NDHSTC'), 8=c('G', 'IV', 'FYW', 'ALM', 'EQRK', 'P', 'ND', 'HSTC'), 9=c('G', 'IV', 'FYW', 'ALM', 'EQRK', 'P', 'ND', 'HS', 'TC'), 11=c('G', 'IV', 'FYW', 'A', 'LM', 'EQRK', 'P', 'ND', 'HS', 'T', 'C'), 13=c('G', 'IV', 'FYW', 'A', 'L', 'M', 'E', 'QRK', 'P', 'ND', 'HS', 'T', 'C'), 20=c('G', 'I', 'V', 'F', 'Y', 'W', 'A', 'L', 'M', 'E', 'Q', 'R', 'K', 'P', 'N', 'D', 'H', 'S', 'T', 'C')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T4(seqs=filePrs,type="gap",Grp=8,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T4(seqs=filePrs,type="lambda",Grp=8,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-5 (PseKRAAC_T5)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type5(PseKRAAC_T5) contains Grp 3,4,8,10,15,20.

Usage

PseKRAAC_T5(
  seqs,
  type = "gap",
  Grp = 4,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 3=c('FWYCILMVAGSTPHNQ', 'DE', 'KR'), 4=c('FWY', 'CILMV', 'AGSTP', 'EQNDHKR'), 8=c('FWY', 'CILMV', 'GA', 'ST', 'P', 'EQND', 'H', 'KR'), 10=c('G', 'FYW', 'A', 'ILMV', 'RK', 'P', 'EQND', 'H', 'ST', 'C'), 15=c('G', 'FY', 'W', 'A', 'ILMV', 'E', 'Q', 'RK', 'P', 'N', 'D', 'H', 'S', 'T', 'C'), 20=c('G', 'I', 'V', 'F', 'Y', 'W', 'A', 'L', 'M', 'E', 'Q', 'R', 'K', 'P', 'N', 'D', 'H', 'S', 'T', 'C')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T5(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T5(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-6A (PseKRAAC_T6A)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type6 contain two type: type6A and type6B. 'PseKRAAC_T6A' contains Grp 4,5,20.

Usage

PseKRAAC_T6A(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 4=c('AGPST', 'CILMV', 'DEHKNQR', 'FYW'), 5=c('AHT', 'CFILMVWY', 'DE', 'GP', 'KNQRS'), 20=c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T6A(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T6A(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-6B (PseKRAAC_T6B)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type6 contain two type: type6A and type6B. 'PseKRAAC_T6B' contains Grp 5.

Usage

PseKRAAC_T6B(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: 5=c('AEHKQRST', 'CFILMVWY', 'DN', 'G', 'P')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T6B(seqs=filePrs,type="gap",Grp=5,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T6B(seqs=filePrs,type="lambda",Grp=5,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-7 (PseKRAAC_T7)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type7(PseKRAAC_T7) contains Grp 2-20.

Usage

PseKRAAC_T7(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: Grp2=c('C', 'MFILVWYAGTSNQDEHRKP'), Grp3=c('C', 'MFILVWYAKR', 'GTSNQDEHP'), Grp4=c('C', 'KR', 'MFILVWYA', 'GTSNQDEHP'), Grp5=c('C', 'KR', 'MFILVWYA', 'DE', 'GTSNQHP'), Grp6=c('C', 'KR', 'WYA', 'MFILV', 'DE', 'GTSNQHP'), Grp7=c('C', 'KR', 'WYA', 'MFILV', 'DE', 'QH', 'GTSNP'), Grp8=c('C', 'KR', 'WYA', 'MFILV', 'D', 'E', 'QH', 'GTSNP'), Grp9=c('C', 'KR', 'WYA', 'MFILV', 'D', 'E', 'QH', 'TP', 'GSN'), Grp10=c('C', 'KR', 'WY', 'A', 'MFILV', 'D', 'E', 'QH', 'TP', 'GSN'), Grp11=c('C', 'K', 'R', 'WY', 'A', 'MFILV', 'D', 'E', 'QH', 'TP', 'GSN'), Grp12=c('C', 'K', 'R', 'WY', 'A', 'MFILV', 'D', 'E', 'QH', 'TP', 'GS', 'N'), Grp13=c('C', 'K', 'R', 'W', 'Y', 'A', 'MFILV', 'D', 'E', 'QH', 'TP', 'GS', 'N'), Grp14=c('C', 'K', 'R', 'W', 'Y', 'A', 'FILV', 'M', 'D', 'E', 'QH', 'TP', 'GS', 'N'), Grp15=c('C', 'K', 'R', 'W', 'Y', 'A', 'FILV', 'M', 'D', 'E', 'Q', 'H', 'TP', 'GS', 'N'), Grp16=c('C', 'K', 'R', 'W', 'Y', 'A', 'FILV', 'M', 'D', 'E', 'Q', 'H', 'TP', 'G', 'S', 'N'), Grp17=c('C', 'K', 'R', 'W', 'Y', 'A', 'FI', 'LV', 'M', 'D', 'E', 'Q', 'H', 'TP', 'G', 'S', 'N'), Grp18=c('C', 'K', 'R', 'W', 'Y', 'A', 'FI', 'LV', 'M', 'D', 'E', 'Q', 'H', 'T', 'P', 'G', 'S', 'N'), Grp19=c('C', 'K', 'R', 'W', 'Y', 'A', 'F', 'I', 'LV', 'M', 'D', 'E', 'Q', 'H', 'T', 'P', 'G', 'S', 'N'), Grp20=c('C', 'K', 'R', 'W', 'Y', 'A', 'F', 'I', 'L', 'V', 'M', 'D', 'E', 'Q', 'H', 'T', 'P', 'G', 'S', 'N')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T7(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T7(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-8 (PseKRAAC_T8)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type8(PseKRAAC_T8) contains Grp 2-20.

Usage

PseKRAAC_T8(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: Grp2=c('ADEGKNPQRST', 'CFHILMVWY'), Grp3=c('ADEGNPST', 'CHKQRW', 'FILMVY'), Grp4=c('AGNPST', 'CHWY', 'DEKQR', 'FILMV'), Grp5=c('AGPST', 'CFWY', 'DEN', 'HKQR', 'ILMV'), Grp6=c('APST', 'CW', 'DEGN', 'FHY', 'ILMV', 'KQR'), Grp7=c('AGST', 'CW', 'DEN', 'FY', 'HP', 'ILMV', 'KQR'), Grp8=c('AST', 'CG', 'DEN', 'FY', 'HP', 'ILV', 'KQR', 'MW'), Grp9=c('AST', 'CW', 'DE', 'FY', 'GN', 'HQ', 'ILV', 'KR', 'MP'), Grp10=c('AST', 'CW', 'DE', 'FY', 'GN', 'HQ', 'IV', 'KR', 'LM', 'P'), Grp11=c('AST', 'C', 'DE', 'FY', 'GN', 'HQ', 'IV', 'KR', 'LM', 'P', 'W'), Grp12=c('AST', 'C', 'DE', 'FY', 'G', 'HQ', 'IV', 'KR', 'LM', 'N', 'P', 'W'), Grp13=c('AST', 'C', 'DE', 'FY', 'G', 'H', 'IV', 'KR', 'LM', 'N', 'P', 'Q', 'W'), Grp14=c('AST', 'C', 'DE', 'FL', 'G', 'H', 'IV', 'KR', 'M', 'N', 'P', 'Q', 'W', 'Y'), Grp15=c('AST', 'C', 'DE', 'F', 'G', 'H', 'IV', 'KR', 'L', 'M', 'N', 'P', 'Q', 'W', 'Y'), Grp16=c('AT', 'C', 'DE', 'F', 'G', 'H', 'IV', 'KR', 'L', 'M', 'N', 'P', 'Q', 'S', 'W', 'Y'), Grp17=c('AT', 'C', 'DE', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'W', 'Y'), Grp18=c('A', 'C', 'DE', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y'), Grp19=c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y'), Grp20=c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'V', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T8(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T8(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Pseudo K_tuple Reduced Amino Acid Composition Type-9 (PseKRAAC_T9)

Description

There are 16 types of PseKRAAC function. In the functions, a (user-selected) grouping of the amino acids might be used to reduce the amino acid alphabet. Also, the functions have a type parameter. The parameter determines the protein sequence analyses which can be either gap or lambda-correlation. PseKRAAC_type9(PseKRAAC_T9) contains Grp 2-20.

Usage

PseKRAAC_T9(
  seqs,
  type = "gap",
  Grp = 5,
  GapOrLambdaValue = 2,
  k = 4,
  label = c()
)

Arguments

Details

Groups: Grp2=c('ADEGKNPQRST', 'CFHILMVWY'), Grp3=c('ADEGNPST', 'CHKQRW', 'FILMVY'), Grp4=c('AGNPST', 'CHWY', 'DEKQR', 'FILMV'), Grp5=c('AGPST', 'CFWY', 'DEN', 'HKQR', 'ILMV'), Grp6=c('APST', 'CW', 'DEGN', 'FHY', 'ILMV', 'KQR'), Grp7=c('AGST', 'CW', 'DEN', 'FY', 'HP', 'ILMV', 'KQR'), Grp8=c('AST', 'CG', 'DEN', 'FY', 'HP', 'ILV', 'KQR', 'MW'), Grp9=c('AST', 'CW', 'DE', 'FY', 'GN', 'HQ', 'ILV', 'KR', 'MP'), Grp10=c('AST', 'CW', 'DE', 'FY', 'GN', 'HQ', 'IV', 'KR', 'LM', 'P'), Grp11=c('AST', 'C', 'DE', 'FY', 'GN', 'HQ', 'IV', 'KR', 'LM', 'P', 'W'), Grp12=c('AST', 'C', 'DE', 'FY', 'G', 'HQ', 'IV', 'KR', 'LM', 'N', 'P', 'W'), Grp13=c('AST', 'C', 'DE', 'FY', 'G', 'H', 'IV', 'KR', 'LM', 'N', 'P', 'Q', 'W'), Grp14=c('AST', 'C', 'DE', 'FL', 'G', 'H', 'IV', 'KR', 'M', 'N', 'P', 'Q', 'W', 'Y'), Grp15=c('AST', 'C', 'DE', 'F', 'G', 'H', 'IV', 'KR', 'L', 'M', 'N', 'P', 'Q', 'W', 'Y'), Grp16=c('AT', 'C', 'DE', 'F', 'G', 'H', 'IV', 'KR', 'L', 'M', 'N', 'P', 'Q', 'S', 'W', 'Y'), Grp17=c('AT', 'C', 'DE', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'W', 'Y'), Grp18=c('A', 'C', 'DE', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y'), Grp19=c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'IV', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y'), Grp20=c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'V', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'W', 'Y')

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (Grp)^k.

References

Zuo, Yongchun, et al. "PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition." Bioinformatics 33.1 (2017): 122-124.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-PseKRAAC_T9(seqs=filePrs,type="gap",Grp=4,GapOrLambdaValue=3,k=2)

mat2<-PseKRAAC_T9(seqs=filePrs,type="lambda",Grp=4,GapOrLambdaValue=3,k=2)

Quasi Sequence Order (QSOrder)

Description

This function computes the quasi-sequence-order for sequences. It is for amino acid pairs with d distances (d can be any number between 1 and 20). First, it calculates the frequencies of each amino acid ("A", "C",..., "Y"). Then, it normalizes the frequencies by dividing the frequency of an amino acid to the frequency of all amino acids plus the sum of tau values which is multiplied by W. tau values are given by function SOCNumber. For d bigger than 20, it computes tau for d in the range "1 to (nlag-20) * W" and normalizes them like before.

Usage

QSOrder(seqs, nlag = 25, W = 0.1, label = c())

Arguments

Details

Please find details about tau in function SOCNumber.

Value

It returns a feature matrix which the number of rows equals to the number of sequences and the number of columns is (nlag*2). For each distance d, there are two values. One value for Granthman and another one for Schneider distance.

Note

For d between 21 to nlag, the function calculates tau values for (d-20) to (nlag-20).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat<-QSOrder(seqs=filePrs,nlag=25)

Splitted Amino Acid Composition (SAAC)

Description

This function splits the input sequence into three parts. The first part is N-terminal and the third part is C-terminal and middle part contains all amino acids between these two part. N-terminal will be determined by the first numNterm amino acid in the sequences and C-terminal is determined by numCterm of the last amino acids in the sequence. Users should enter numNterm and numCterm parameters. Their default value is 25. The function calculates kAAComposition for each of the three parts.

Usage

SAAC(seqs, k = 1, numNterm = 5, numCterm = 5, normalized = TRUE, label = c())

Arguments

Value

It returns a feature matrix. The number of rows is equal to the number of sequences. The number of columns is (3*(20^k)).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-SAAC(seqs=filePrs,k=1,numNterm=15,numCterm=15)

Splitted Group Amino Acid Composition (SGAAC)

Description

In this function, amino acids are first grouped into a user-defined category. Later, the splitted amino Acid composition is computed. Please note that this function differs from SAAC which works on individual amino acids.

Usage

SGAAC(
  seqs,
  k = 1,
  numNterm = 25,
  numCterm = 25,
  Grp = "locFus",
  normalized = TRUE,
  label = c()
)

Arguments

Value

It returns a feature matrix. The number of rows is equal to the number of sequences. The number of columns is 3*((number of groups)^k).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat<-SGAAC(seqs=filePrs,k=1,numNterm=15,numCterm=15,Grp="aromatic")

Sequence Order Coupling Number (SOCNumber)

Description

This function uses dissimilarity matrices Grantham and Schneider to compute the dissimilarity between amino acid pairs. The distance between amino acid pairs is determined by d which varies between 1 to nlag. For each d, it computes the sum of the dissimilarities of all amino acid pairs. The sum shows the value of tau for a value d. The feature vector contains the values of taus for both matrices. Thus, the length of the feature vector is equal to nlag*2.

Usage

SOCNumber(seqs, nlag = 30, label = c())

Arguments

Value

It returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (nlag*2). For each distance d, there are two values. One value for Granthman and another one for Schneider distance.

Note

When d=1, the pairs of amino acids have no gap and when d=2, there is one gap between the amino acid pairs in the sequence. It will repeat likewise for other values of d.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat<-SOCNumber(seqs=filePrs,nlag=25)

Secondary Structure Elements Binary (SSEB)

Description

This function works based on the output of PSIPRED which predicts the secondary structure of the amino acids in a sequence. The output of the PSIPRED is a tab-delimited file which contains the secondary structure in the third column. SSEB gives a binary number (i.e., '001'='H','010'=E','100'='C') for each amino acid.

Usage

SSEB(dirPath, binaryType = "numBin", outFormat = "mat", outputFileDist = "")

Arguments

Details

This function converts each amino acid to a 3-bit value, such that 2 bits are 0 and 1 bit is 1. The position of 1 shows the type of the secondary structure of the amino acids in the protein/peptide. In this function, '001' is used to show Helix structure, '010' to show Extended structure and '100' to show coil structure.

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in the outFormat parameter for sequences with different lengths. Warning: If the outFormat is set to 'mat' for sequences with different lengths, it returns an error. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/SSEB.txt")

Predss2dir<-system.file("testForder",package="ftrCOOL")
Predss2dir<-paste0(Predss2dir,"/ss2Dir/")
mat<-SSEB(Predss2dir,binaryType="numBin",outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Secondary Structure Elements Composition (SSEC)

Description

This function works based on the output of PSIPRED which predicts the secondary structure of the amino acids in a sequence. The output of the PSIPRED is a tab-delimited file which contains the secondary structure in the third column. SSEC returns the frequency of the secondary structures (i.e., Helix, Extended, Coil) of the sequences.

Usage

SSEC(dirPath)

Arguments

Value

It returns a feature matrix which the number of rows is the number of sequences and the number of columns is 3. The first column shows the number of amino acids which participate in the coil structure. The second column shows the number of amino acids in the extended structure and the last column shows the number of amino acids in the helix structure.

Examples

Predss2dir<-system.file("testForder",package="ftrCOOL")
Predss2dir<-paste0(Predss2dir,"/ss2Dir/")
mat<-SSEC(Predss2dir)

Secondary Structure Elements Simple (SSES)

Description

This function works based on the output of PSIPRED which predicts the secondary structure of the amino acids in a sequence. The output of the PSIPRED is a tab-delimited file which contains the secondary structure in the third column. The function represent amino acids in the helix structure by 'H', amino acids in the extended structure by 'E', and amino acids in the coil structure by 'C'.

Usage

SSES(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same lengths such that the number of columns is equal to the length of the sequences and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for the sequences with the same lengths. However, the users can use 'txt' option in the outFormat parameter for sequences with different lengths. Warning: If the outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when the output format is 'txt', the label information is not displayed in the text file. It is noteworthy that, 'txt' format is not usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/simpleSSE.txt")

Predss2dir<-system.file("testForder",package="ftrCOOL")
Predss2dir<-paste0(Predss2dir,"/ss2Dir/")
mat<-SSES(Predss2dir,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Trinucleotide physicochemical properties (TPCP_DNA)

Description

This function replaces trinucleotides in a sequence with their physicochemical properties which is multiplied by normalized frequency of that tri-nucleotide.

Usage

TPCP_DNA(
  seqs,
  selectedIdx = c("Dnase I", "Bendability (DNAse)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 12 physicochemical indexes in the trinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-2)*(number of selected trinucleotide properties) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-TPCP_DNA(seqs = fileLNC,threshold=1,outFormat="mat")

Torsion Angle (TorsionAngle)

Description

The inputs to this function are phi and psi angles of each amino acid in the sequence. We use the output of SPINE-X software to obtain the angles. Further, the TA function replaces each amino acid of the sequence with a vector. The vector contain two elements: The phi and psi angles.

Usage

TorsionAngle(dirPath, outFormat = "mat", outputFileDist = "")

Arguments

Value

The output is differnet depending on the outFormat parameter ('mat' or 'txt'). If the outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and the number of columns is (length of the sequence)*2. If the outFormat is set to 'txt', all binary values will be writen in a 'txt' file. Each row belongs to a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If the outFormat is set to 'mat' for sequences with different lengths, it returns an error. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

dir = tempdir()
ad<-paste0(dir,"/ta.txt")

PredTorsioNdir<-system.file("testForder",package="ftrCOOL")
PredTorsioNdir<-paste0(PredTorsioNdir,"/TorsioNdir/")
mat<-TorsionAngle(PredTorsioNdir,outFormat="txt",outputFileDist=ad)

unlink("dir", recursive = TRUE)

Tri Nucleotide Index (TriNucIndex)

Description

This function replaces trinucleotides in a sequence with their physicochemical properties in the trinucleotide index file.

Usage

TriNUCindex_DNA(
  seqs,
  selectedNucIdx = c("Dnase I", "Bendability (DNAse)"),
  threshold = 1,
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

There are 12 physicochemical indexes in the trinucleotide database.

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length-2)*(number of selected trinucleotide properties) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

fileLNC<-system.file("extdata/Athaliana1.fa",package="ftrCOOL")
vect<-TriNUCindex_DNA(seqs = fileLNC,threshold=1,outFormat="mat")

Z_curve_12bit_DNA (Zcurve12bit_DNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of nucleotides, di-nucleotides, or tri-nucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve12bit_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 12.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Zcurve12bit_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_12bit_RNA (Zcurve12bit_RNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of ribonucleotides, di-ribonucleotides, or tri-ribonucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve12bit_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 12.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Zcurve12bit_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_144bit_DNA (Zcurve144bit_DNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of nucleotides, di-nucleotides, or tri-nucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve144bit_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 144.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Zcurve144bit_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_144bit_RNA (Zcurve144bit_RNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of ribonucleotides, di-ribonucleotides, or tri-ribonucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve144bit_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 144.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Zcurve144bit_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_36bit_DNA (Zcurve36bit_DNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of nucleotides, di-nucleotides, or tri-nucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve36bit_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 36.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Zcurve36bit_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_36bit_RNA (Zcurve36bit_RNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of ribonucleotides, di-ribonucleotides, or tri-ribonucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve36bit_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 36.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Zcurve36bit_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_48bit_DNA (Zcurve48bit_DNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of nucleotides, di-nucleotides, or tri-nucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve48bit_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 48.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Zcurve48bit_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_48bit_RNA (Zcurve48bit_RNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of ribo ribonucleotides, di-ribonucleotides, or tri-ribonucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve48bit_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 48.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Zcurve48bit_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_9bit_DNA (Zcurve9bit_DNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of nucleotides, di-nucleotides, or tri-nucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve9bit_DNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 9.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-Zcurve9bit_DNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Z_curve_9bit_RNA (Zcurve9bit_RNA)

Description

These group of functions (Zcurve (9, 12, 36, 48, 144)_bit) function calculates the Z-curves. Z-curves are based on freqiencies of ribo ribonucleotides, di-ribonucleotides, or tri-ribonucleotides and their positions on the sequences. For more information about the methods please refer to reference part.

Usage

Zcurve9bit_RNA(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is 9.

References

Gao,F. and Zhang,C.T. Comparison of various algorithms for recognizing short coding sequences of human genes. Bioinformatics, (2004).

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-Zcurve9bit_RNA(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

AlphabetCheck

Description

This function checks the alphabets in a sequence. If one of the following conditions hold, the sequence will be deleted: 1. A peptide sequence containing non-standard amino acids, 2. A DNA sequence with an alphabet other than A, C, G, or T, 3. An RNA sequence having an alphabet other than A, C, G, or U.

Usage

alphabetCheck(sequences, alphabet = "aa", label = c())

Arguments

Value

'alphabetCheck' returns a list with two elements. The first element is a vector which contains valid sequences. The second element is a vector which contains the labels of the sequences (if any exists).

Note

This function receives a sequence vector and the label of sequences (if any). It deletes sequences (and their labels) containing non-standard alphabets.

Examples

seq<-alphabetCheck(sequences=c("AGDFLIAACNMLKIVYT","ADXVGAJK"),alphabet="aa")

Binary - 3bit - Type1 (binary_3bit_T1)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T1(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T1(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type2 (binary_3bit_T2)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T2(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T2(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type3 (binary_3bit_T3)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T3(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T3(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type4 (binary_3bit_T4)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T4(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T4(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type5 (binary_3bit_T5)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T5(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T5(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type6 (binary_3bit_T6)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T6(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T6(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 3bit - Type7 (binary_3bit_T7)

Description

This group of functions(binary_3bit_T1-T7) categorizes amino acids in 3 groups based on the type. Then represent group of amino acids by a three dimentional vector. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_3bit_T7(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*3. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_3bit_T7(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 5bit - Type1 (binary_5bit_T1)

Description

This function categorizes amino acids in 5 groups. Then represent group of amino acids by a 5 dimentional vector i.e.e1, e2, e3, e4, e5. e1=G, A, V, L, M, I, e2=F, Y, W, e3=K, R, H, e4=D, E, e5=S, T, C, P, N, Q. e1 is ecoded by 10000 e2 is encoded by 01000 and ... and e5 is encoded by 00001.

Usage

binary_5bit_T1(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Details

The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*5. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_5bit_T1(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 5bit - Type2 (binary_5bit_T2)

Description

The idea behind this function is: We have 20 amino acids and we can show them with at least 5 bits. A is encoded by (00011), C (00101), D (00110), E (00111), F(01001), G (01010), H (01011), I (01100), K (01101), L (01110), M (10001), N (10010), P (10011), Q (10100), R (10101), S (10110), T (11000), V (11001), W (11010), Y (11100). This function transforms an amino acid to a binary format. The type of the binary format is determined by the binaryType parameter. For details about each format, please refer to the description of the binaryType parameter.

Usage

binary_5bit_T2(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*5. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_5bit_T2(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Binary - 6bit (binary_6bit)

Description

This function categorizes amino acids in 6 groups. Then represent group of amino acids by a 6 dimentional vector i.e.e1, e2, e3, e4, e5, e6. e1=H, R, K, e2=D, E, N, D, e3=C, e4=S, T, P, A, G, e5=M, I, L, V, e6=F, Y, W. e1 is ecoded by 100000 e2 is encoded by 010000 and ... and e6 is encoded by 000001.

Usage

binary_6bit(
  seqs,
  binaryType = "numBin",
  label = c(),
  outFormat = "mat",
  outputFileDist = ""
)

Arguments

Value

The output is different depending on the outFormat parameter ('mat' or 'txt'). If outFormat is set to 'mat', it returns a feature matrix for sequences with the same lengths. The number of rows is equal to the number of sequences and if binaryType is 'strBin', the number of columns is the length of the sequences. Otherwise, it is equal to (length of the sequences)*6. If outFormat is 'txt', all binary values will be written to a the output is written to a tab-delimited file. Each line in the file shows the binary format of a sequence.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes if sequences have different sizes. Otherwise 'txt' format is also usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-binary_6bit(seqs = ptmSeqsVect, binaryType="numBin",outFormat="mat")

Codon Adaption Index (codonAdaptionIndex)

Description

This function calculates the codon adaption index for each sequence.

Usage

codonAdaptionIndex(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the value of the codon adaption index.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-codonAdaptionIndex(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

Conjoint Triad (conjointTriad)

Description

This function calculates the grouped tripeptide composition with the conjoint triad grouping type.

Usage

conjointTriad(seqs, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows equals to the number of sequences and the number of columns is 7^3.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-conjointTriad(seqs=filePrs)

k-Spaced Conjoint Triad (conjointTriadKS)

Description

This function calculates the grouped tripeptide composition with conjoint triad grouping type. For each k, it creates a 7^3 feature vector. K is the space between the first and the second amino acids and the second and the third amino acids of the tripeptide.

Usage

conjointTriadKS(seqs, rng = 3, upto = FALSE, normalized = FALSE, label = c())

Arguments

Details

A tripeptide with k spaces looks like AA1(ss..s)AA2(ss..s)AA3. AA stands for amino acids and s means space.

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is (7^3)*(length rng vector).

Note

'upto' is enabled only when rng is a number and not a vector.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-conjointTriadKS(filePrs,rng=2,upto=TRUE,normalized=TRUE)

mat2<-conjointTriadKS(filePrs,rng=c(1,3,5))

Fasta File Reader (fa.read)

Description

This function reads a FASTA file. Each sequence starts with '>' in the file. This is a general function which can be applied to all types of sequences (i.e., protein/peptide, dna, and rna).

Usage

fa.read(file, legacy.mode = TRUE, seqonly = FALSE, alphabet = "aa")

Arguments

Value

a string vector such that each element is a sequence.

References

https://cran.r-project.org/web/packages/rDNAse/index.html

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
sequenceVectLNC<-fa.read(file=fileLNC,alphabet="dna")

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
sequenceVectPRO<-fa.read(file=filePrs,alphabet="aa")

Fickett Score (fickettScore)

Description

This function calculates the ficket score of each sequence.

Usage

fickettScore(seqs, ORF = FALSE, reverseORF = TRUE, label = c())

Arguments

Value

The function returns a feature vector. The length of the vector is equal to the number of sequences. Each entry in the vector contains the value of the fickett score.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-fickettScore(seqs=fileLNC,ORF=TRUE,reverseORF=FALSE)

k Amino Acid Composition (kAAComposition)

Description

This function calculates the frequency of all k-mers in the sequence(s).

Usage

kAAComposition(seqs, rng = 3, upto = FALSE, normalized = TRUE, label = c())

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on rng vector. For each value k in the vector, (20)^k columns are created in the matrix.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")

mat1<-kAAComposition(seqs=filePrs,rng=3,upto=TRUE)
mat2<-kAAComposition(seqs=filePrs,rng=c(1,3),upto=TRUE)

k Grouped Amino Acid Composition (kGAAComposition)

Description

In this function, amino acids are first grouped into user-defined categories. Later, the composition of the k grouped amino acids is computed. Please note that this function differs from kAAComposition which works on individual amino acids.

Usage

kGAAComposition(
  seqs,
  rng = 3,
  upto = FALSE,
  normalized = TRUE,
  Grp = "locFus",
  label = c()
)

Arguments

Details

for more details, please refer to kAAComposition

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns is ((number of categorizes)^k)*(length of rng vector).

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
mat1<-CkSGAApair(seqs=filePrs,rng=2,upto=TRUE,Grp="aromatic")

mat2<-CkSGAApair(seqs=filePrs,rng=c(1,3,5),Grp=
list(Grp1=c("G","A","V","L","M","I","F","Y","W"),Grp2=c("K","R","H","D","E")
,Grp3=c("S","T","C","P","N","Q")))

k Nucleotide Composition (kNUComposition_DNA)

Description

This function calculates the frequency of all k-mers in the sequence.

Usage

kNUComposition_DNA(
  seqs,
  rng = 3,
  reverse = FALSE,
  upto = FALSE,
  normalized = TRUE,
  ORF = FALSE,
  reverseORF = TRUE,
  label = c()
)

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on the rng vector. For each value k in the vector, (4)^k columns are created in the matrix.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
mat<-kNUComposition_DNA(seqs=fileLNC,rng=c(1,3))

k riboNucleotide Composition (kNUComposition_RNA)

Description

This function calculates the frequency of all k-mers in the sequence.

Usage

kNUComposition_RNA(
  seqs,
  rng = 3,
  reverse = FALSE,
  upto = FALSE,
  normalized = TRUE,
  ORF = FALSE,
  reverseORF = TRUE,
  label = c()
)

Arguments

Value

This function returns a feature matrix. The number of rows is equal to the number of sequences and the number of columns depends on the rng vector. For each value k in the vector, (4)^k columns are created in the matrix.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
mat<-kNUComposition_RNA(seqs=fileLNC,rng=c(1,3))

Maximum Open Reading Frame in DNA (maxORF)

Description

This function gets a sequence as the input. If reverse is true, the function extracts the max Open Reading Frame in the sequence and its reverse complement (hint: Six frames). Otherwise, only the sequence is searched (hint: Three frames).

Usage

maxORF(seqs, reverse = TRUE, label = c())

Arguments

Value

A vector containing a subsequence for each given sequences. The subsequence is the maximum ORF of the sequence.

Note

If a sequence does not contain ORF, the function deletes the sequence.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
ORF<-maxORF(seqs=fileLNC,reverse=FALSE)

Maximum Open Reading Frame in RNA (maxORF_RNA)

Description

This function gets a sequence as the input. If reverse is true, the function extracts the max Open Reading Frame in the sequence and its reverse complement (hint: Six frames). Otherwise, only the sequence is searched (hint: Three frames).

Usage

maxORF_RNA(seqs, reverse = TRUE, label = c())

Arguments

Value

A vector containing a subsequence for each given sequences. The subsequence is the maximum ORF of the sequence.

Note

If a sequence does not contain ORF, the function deletes the sequence.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
ORF<-maxORF_RNA(seqs=fileLNC,reverse=FALSE)

Maximum Open Reading Frame length in DNA (maxORFlength_DNA)

Description

This function returns the length of the maximum Open Reading Frame for each sequence. If reverse is FALSE, ORF region will be searched in a sequence. Otherwise, it will be searched both in the sequence and its reverse complement.

Usage

maxORFlength_DNA(seqs, reverse = TRUE, normalized = FALSE, label = c())

Arguments

Value

A vector containing the lengths of maximum ORFs for each sequence.

Examples

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
vect<-maxORFlength_DNA(seqs=fileLNC,reverse=TRUE,normalized=TRUE)

Maximum Open Reading Frame length in RNA (maxORFlength_RNA)

Description

This function returns the length of the maximum Open Reading Frame for each sequence. If reverse is FALSE, ORF region will be searched in a sequence. Otherwise, it will be searched both in the sequence and its reverse complement.

Usage

maxORFlength_RNA(seqs, reverse = TRUE, normalized = FALSE, label = c())

Arguments

Value

A vector containing the lengths of maximum ORFs for each sequence.

Examples

fileLNC<-system.file("extdata/Carica_papaya101RNA.txt",package="ftrCOOL")
vect<-maxORFlength_RNA(seqs=fileLNC,reverse=TRUE,normalized=TRUE)

naming Kmer (nameKmer)

Description

This function creates all possible k-combinations of the given alphabets.

Usage

nameKmer(k = 3, type = "aa", num = 0)

Arguments

Value

a string vector of length (20^k for 'aa' type), (4^k for 'dna' type), (4^k for 'rna' type), and (num^k for 'num' type).

Examples

all_kmersAA<-nameKmer(k=2,type="aa")

all_kmersDNA<-nameKmer(k=3,type="dna")

all_kmersNUM<-nameKmer(k=3,type="num",num=2)

Needleman-Wunsch (needleman)

Description

This function works based on Needleman-Wunsch algorithm which computes similarity score of two sequences.

Usage

needleman(seq1, seq2, gap = -1, mismatch = -1, match = 1)

Arguments

Value

The function returns a number which indicates the similarity between sequence1 and sequence2.

References

https://gist.github.com/juliuskittler/ed53696ac1e590b413aac2dddf0457f6

Examples

simScore<-needleman(seq1="Hello",seq2="Hello",gap=-1,mismatch=-2,match=1)

nonStandard sequence (nonStandardSeq)

Description

This function returns sequences which contain at least one non-standard alphabet.

Usage

nonStandardSeq(file, legacy.mode = TRUE, seqonly = FALSE, alphabet = "aa")

Arguments

Value

This function returns a string vector. Each element of the vector is a sequence which contains at least one non-standard alphabet.

Examples

filePrs<-system.file("extdata/proteins.fasta",package="ftrCOOL")
nonStandardPrSeq<-nonStandardSeq(file = filePrs,alphabet="aa")

fileLNC<-system.file("extdata/Athaliana_LNCRNA.fa",package="ftrCOOL")
nonStandardNUCSeq<-nonStandardSeq(file = filePrs, alphabet="dna")

Read Directory of Accessible Solvent accessibility predicted files (readASAdir)

Description

This function reads a directory that contains the output files of SPINE-X. It gets the directory path as the input and returns a list of vectors. Each vector includes the ASA predicted value for amino acids of the sequence.

Usage

readASAdir(dirPath)

Arguments

Value

a list of vectors with all the predicted ASA value for each amino acid. The length of the list is the number of files(sequences) and the length of each vector is (length of sequence(i))

Examples

PredASAdir<-system.file("testForder",package="ftrCOOL")
PredASAdir<-paste0(PredASAdir,"/ASAdir/")
PredVectASA<-readASAdir(PredASAdir)

Read disorder predicted Directory (readDisDir)

Description

This function reads a directory that contains the output VSL2 files. It gets the directory path as the input and returns a list of vectors. Each vector includes the disorder/order type for the amino acids of the sequence.

Usage

readDisDir(dirPath)

Arguments

Value

a list of vectors with all the predicted disorder/order type for each amino acid. The length of the list is equal to the number of files(sequences) and the length of each vector is the length of the sequence(i).

Examples

PredDisdir<-system.file("testForder",package="ftrCOOL")
PredDisdir<-paste0(PredDisdir,"/Disdir/")
listPredVect<-readDisDir(PredDisdir)

Read PSSM Directory (readPSSMdir)

Description

This function reads a directory that contains the output psi-blast. It gets the directory path as the input and returns a list of vectors. Each vector includes the type for the amino acids of the sequence.

Usage

readPSSMdir(dirPath)

Arguments

Value

a list of vectors with all the predicted disorder/order type for each amino acid. The length of the list is equal to the number of files(sequences) and the length of each vector is the length of the sequence(i).

Examples

pssmDir<-system.file("testForder",package="ftrCOOL")
pssmDir<-paste0(pssmDir,"/PSSMdir/")
listPredVect<-readPSSMdir(pssmDir)

Read Directory of Torsion predicted files (readTorsionDir)

Description

This function reads a directory that contains the output files of SPINE-X. It gets the directory path as the input and returns a list of vectors. Each vector includes the phi and psi angle of the amino acids of the sequence.

Usage

readTorsionDir(dirPath)

Arguments

Value

returns a list of vectors with all the predicted phi and psi angles for each amino acid. The length of the list is the number of files(sequences) and the length of each vector is (2(phi-psi)*length sequence(i)).

Examples

PredTorsioNdir<-system.file("testForder",package="ftrCOOL")
PredTorsioNdir<-paste0(PredTorsioNdir,"/TorsioNdir/")
PredVectASA<-readTorsionDir(PredTorsioNdir)

Read ss2 predicted Directory (readss2Dir)

Description

This function reads a directory that contains the output files of PSIPRED It gets the directory path as the input and returns a list of vectors. Each vector contains the secondary structure of the amino acids in a peptide/protein sequence.

Usage

readss2Dir(dirPath)

Arguments

Value

returns a list of vectors with all the predicted secondary structure for each amino acid. The length of the list is the number of files(sequences) and the length of each vector is (length sequence(i))

Examples

PredSS2dir<-system.file("testForder",package="ftrCOOL")
PredSS2dir<-paste0(PredSS2dir,"/ss2Dir/")
listPredVect<-readss2Dir(PredSS2dir)

reverseCompelement (revComp)

Description

This function returns the reverse compelement of a dna sequence.

Usage

revComp(seq, outputType = "str")

Arguments

Value

The reverse complement of the input sequence.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
Seq<-ptmSeqsVect[1]
revCompSeq<-revComp(seq=Seq,outputType="char")

Z-SCALE (zSCALE)

Description

This function converts the amino acids of a sequence to five physicochemical descriptor variables which were developed by Sandberg et al. in 1998. The Z-SCALE function can be applied to encode peptides of equal length.

Usage

zSCALE(seqs, label = c(), outFormat = "mat", outputFileDist = "")

Arguments

Value

The output depends on the outFormat parameter which can be either 'mat' or 'txt'. If outFormat is 'mat', the function returns a feature matrix for sequences with the same length such that the number of columns is (sequence length)*(5) and the number of rows is equal to the number of sequences. If the outFormat is 'txt', the output is written to a tab-delimited file.

Note

This function is provided for sequences with the same lengths. Users can use 'txt' option in outFormat parameter for sequences with different lengths. Warning: If outFormat is set to 'mat' for sequences with different lengths, it returns an error. Also, when output format is 'txt', label information is not shown in the text file. It is noteworthy that 'txt' format is not usable for machine learning purposes.

Examples

ptmSeqsADR<-system.file("extdata/",package="ftrCOOL")
ptmSeqsVect<-as.vector(read.csv(paste0(ptmSeqsADR,"/ptmVect101AA.csv"))[,2])
mat<-zSCALE(seqs = ptmSeqsVect,outFormat="mat")