Type:	Package
Title:	Simple Method to Detect Compositional Changes in Genomic Sequences
Version:	2.0.1
Date:	2024-09-27
Maintainer:	Nora M. Villanueva <nmvillanueva@uvigo.gal>
Depends:	R (≥ 2.15.1)
Description:	This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset https://www.ncbi.nlm.nih.gov/genbank/. This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'.
Imports:	seqinr
License:	GPL-2 | GPL-3 [expanded from: GPL]
LazyData:	true
NeedsCompilation:	yes
Packaged:	2024-09-30 13:01:08 UTC; sestelo
Author:	Nora M. Villanueva [aut, cre], Marta Sestelo [aut], Alan Miller [ctb] (FORTRAN code lsq.f90: weighted least-squares module)
Repository:	CRAN
Date/Publication:	2024-09-30 14:50:02 UTC

Simple method to detect compositional changes in genomic sequences

Description

seq2R is just a shortcut for "sequence to R". This software is useful for loading .fasta or .gbk files, and for recovering sequences from GenBank database. This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in "seq2R".

Author(s)

Nora M. Villanueva and Marta Sestelo.

Maintainer: Nora M. Villanueva <nmvillanueva@uvigo.es>

References

Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7:1-26.

Efron, E. and Tibshirani, R. J. (1993). An introduction to the Bootstrap. Chapman and Hall, London.

Fan, J. and Marron, J.S. (1994). Fast implementation of nonparametric curve estimators. Journal of Computational and Graphical Statistics, 3:35-56.

Hastie, T. and Tibshirani, R. (1990). Generalized additive models. Chapman and Hall, London.

Stone, C. J. (1977). Consistent nonparametric regression. Annals of Statistics, 5: 595-620.

Villanueva, N. M., Sestelo, M., Fonseca, M. M. and Roca-Pardinas, J. (2023). seq2R: An R package to detect change points in DNA sequences. Mathematics, 11 (10), 2299.

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

Critical points (maxima and minima)

Description

Function that maximizes or minimizes the first derivative of the model obtained with find.points function. Also, it is included their 95% confidence intervals.

Usage

critical(model, base.pairs = NULL)

Arguments

Value

The returned list has two component ($AT, $CG). Both of them containing a matrix with values about their critical points (maxima and minima), lower and upper 95% confidence intervals.

Author(s)

Nora M. Villanueva and Marta Sestelo.

References

N. M. Villanueva, M. Sestelo, M. M. Fonseca and J. Roca-Pardinas (2023). seq2R: An R package to detect change points in DNA sequences. Mathematics, 11 (10), 2299.

Examples

library(seq2R)

#mtDNAhum <- read.genbank("NC_012920")
data(mtDNAhum)
DNA <- transform(mtDNAhum)
seq1 <- find.points(DNA, nboot = 10)


critical(seq1,base.pairs="CG")


critical(seq1,base.pairs="AT")

Simple method to detect compositional changes in genomic sequences

Description

find is used to detect changes at genomic sequences composition. The method is based on fitting nonparametric models by using local linear kernel smoothers.

Usage

find.points(x,kbin= 300, p= 3, bandwidth=-1, weights= 1, nboot=100, kernel="gaussian",
n.bandwidths= 20, seed = NULL, ...)

Arguments

Details

For each genomic sequence the AT and CG skews profiles were calculated as A vs. T = (A-T)/(A+T) and C vs. G = (C-G)/(C+G).

Value

The function computes and returns a list of short information for a fitted change.points object.

Author(s)

Nora M. Villanueva and Marta Sestelo.

References

N. M. Villanueva, M. Sestelo, M. M. Fonseca and J. Roca-Pardinas (2023). seq2R: An R package to detect change points in DNA sequences. Mathematics, 11 (10), 2299.

Examples

library(seq2R)


#mtDNAhum <- read.genbank("NC_012920")
data(mtDNAhum)
DNA <- transform(mtDNAhum)
seq1<-find.points(DNA)
seq1

Human Mitochondrial DNA

Description

The complete sequence of the human mitochondrial genome contains 16569 base pair. The sequence presents extreme economy in that the genes have none or only a few noncoding bases between them, and in many cases the termination codons are not coded in the DNA but are created post-transcriptionally by polyadenylation of the mRNAs. The genes for the 12S and 16S rRNAs, 22tRNAs, cytochrome c oxidase subunits I, II, and III, ATPase subunit 6, cytochrome b and eight other predicted protein coding genes have been located.

Usage

data(mtDNAhum)

References

Anderson, S. and Bankier, A. T. and Barrell, B. G. and de Bruijn, M. H. L. and Coulson, A. R. and Drouin, J. and Eperon, I. C. and Nierlich, D. P. and Roe, B. A. and Sanger, F. and Schreier, P. H. and Smith, A. J. H. and Staden, R. and Young, I. G.(1981) Sequence and organization of the human mitochondrial genome. Nature, 5806(290):457:465

Examples

data(mtDNAhum)

Visualization of change.points objects

Description

Useful for drawing the estimation and first derivative of the skew profile.

Usage

## S3 method for class 'change.points'
plot(x = model, y = NULL, base.pairs = NULL, der = NULL,
xlab = "x", ylab = "y", col = "black", CIcol = "black", main = NULL, type = "l",
CItype = "l", critical = FALSE, CIcritical = FALSE,ylim=NULL,...)

Arguments

Value

Simply produce a plot.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(seq2R)


#mtDNAhum <- read.genbank("NC_012920")
data(mtDNAhum)
DNA <- transform(mtDNAhum)
seq1 <- find.points(DNA)



plot(seq1,der=0,base.pairs="CG",CIcritical=TRUE,ylim=c(0.08,0.67))
plot(seq1,der=1,base.pairs="CG",CIcritical=TRUE,ylim=c(-0.0005,0.00045))
abline(h=0)



plot(seq1,critical=TRUE, CIcritical=TRUE)

Short find.points summary

Description

find.points summary.

Usage

## S3 method for class 'change.points'
print(x=model,...)

Arguments

Value

The function computes and returns a list of short information for a fitted change.points object.

Note

See details in find.points.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(seq2R)
#mtDNAhum <- read.genbank("NC_012920")
data(mtDNAhum)
DNA <- transform(mtDNAhum)
seq1 <- find.points(DNA)
seq1

Read FASTA and GBK formatted files

Description

Read nucleic acid sequences from a file in FASTA or GBK format.

Usage

read.all(file = system.file(""), seqtype = "DNA")

Arguments

Details

Fasta is a widely used format in molecular biology. Sequence in FASTA format starts with a single-line description, distinguished by a greater-than ‘>’ symbol, followed by sequence data on the next lines.

'GenBank' format files have the extension GBK, by convention. Files contain fields with different types of information well-labeled. The header of the file has information describing the sequence, such as its type, shape, length and source. The features of the genome sequence follow the header, and include protein translations. The DNA sequence is the last element of the file, which ends with (and must include) a soluble slash. Complete genomes in this format are available at the https://ftp.ncbi.nlm.nih.gov/genbank/.

Value

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(seq2R)
data(mtDNAhum)
## Not run: 
data<-read.all("file.fasta")
data<-read.all("file.gbk")

## End(Not run)

Read DNA sequences from GenBank via internet

Description

This function connects to the GenBank database, and reads nucleotide sequences using locus code given as arguments.

Usage

read.genbank(locus)

Arguments

Details

This function uses the site https://pubmed.ncbi.nlm.nih.gov/ (E-utilities) from where the sequences are downloaded. E-utilities are a set of eight server-side programs that provide a stable interface into the Entrez query and database system at the National Center for Biotechnology Informatio (NCBI). The E-utilities use a fixed URL syntax that translates a standard set of input parameters into the values necessary for various NCBI software components to search for and retrieve the requested data. The E-utilities are therefore the structured interface to the Entrez system, which currently includes 38 databases covering a variety of biomedical data, including nucleotide and protein sequences, gene records, three-dimensional molecular structures, and the biomedical literature.

Value

Note

If the computer is not connected to the internet, this function will not work.

Author(s)

Nora M. Villanueva and Marta Sestelo.

References

Bethesda M. D. (2010) Entrez Programming Utilities Help. NCBI Help Manual. NCBI, USA

Examples

library(seq2R)
#mthumanDNA <- read.genbank("NC_012920")
#mthumanDNA

Convert biological sequences into binary code

Description

Biological sequences are categorical variables. With this function the four nucleotides are coded with two bits, 0 and 1 (binary numeral system) for being used by almost all modern computers.

Usage

transform(x)

Arguments

Value

The returned list has two component ($AT, $CG). Both of them containing a matrix with values about their critical points (maximum and minimum), and their lower and upper 95% confidence intervals.

Author(s)

Nora M. Villanueva and Marta Sestelo.

Examples

library(seq2R)

#mtDNAhum <- read.genbank("NC_012920")
data(mtDNAhum)
DNA <- transform(mtDNAhum)
DNA