tigger: Infers Novel Immunoglobulin Alleles from Sequencing Data

Description

Infers the V genotype of an individual from immunoglobulin (Ig) repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences. Citations: Gadala-Maria, et al (2015) doi:10.1073/pnas.1417683112, Gadala-Maria, et al (2019) doi:10.3389/fimmu.2019.00129.

Author(s)

Maintainer: Susanna Marquez susanna.marquez@yale.edu

Authors:

• Daniel Gadala-Maria daniel.gadala-maria@yale.edu

• Moriah Cohen moriah.cohen@biu.ac.il

• Jason Vander Heiden jason.vanderheiden@gmail.com

• Gur Yaari gur.yaari@biu.ac.il

• Steven Kleinstein steven.kleinstein@yale.edu [copyright holder]

See Also

Useful links:

• http://tigger.readthedocs.io

• Report bugs at https://bitbucket.org/kleinstein/tigger/issues

Example human immune repertoire data

Description

A data.frame of example V(D)J immunoglobulin sequences derived from a single individual (PGP1), sequenced on the Roche 454 platform, and assigned by IMGT/HighV-QUEST to IGHV1 family alleles.

Format

A data.frame where rows correspond to unique V(D)J sequences and columns include:

• "sequence_alignment": IMGT-gapped V(D)J nucleotide sequence.

• "v_call": IMGT/HighV-QUEST V segment allele calls.

• "d_call": IMGT/HighV-QUEST D segment allele calls.

• "j_call": IMGT/HighV-QUEST J segment allele calls.

• "junction_length": Junction region length.

References

1. Gadala-Maria, et al. (2015) Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. PNAS. 112(8):E862-70.

See Also

See SampleDb for Change-O formatted version of AIRRDb.

Human IGHV germlines

Description

A character vector of all human IGHV germline gene segment alleles in IMGT/GENE-DB (2019-06-01, 372 alleles). See IMGT data updates: https://www.imgt.org/IMGTgenedbdoc/dataupdates.html.

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by ".") while names correspond to stripped-down IMGT allele names (e.g. "IGHV1-18*01").

References

1. Xochelli, et al. (2014) Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia. Immunogenetics. 67(1):61-6.

Example human immune repertoire data

Description

A data.frame of example V(D)J immunoglobulin sequences derived from a single individual (PGP1), sequenced on the Roche 454 platform, and assigned by IMGT/HighV-QUEST to IGHV1 family alleles.

Format

A data.frame where rows correspond to unique V(D)J sequences and columns include:

• "SEQUENCE_IMGT": IMGT-gapped V(D)J nucleotide sequence.

• "V_CALL": IMGT/HighV-QUEST V segment allele calls.

• "D_CALL": IMGT/HighV-QUEST D segment allele calls.

• "J_CALL": IMGT/HighV-QUEST J segment allele calls.

• "JUNCTION_LENGTH": Junction region length.

References

1. Gadala-Maria, et al. (2015) Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. PNAS. 112(8):E862-70.

See Also

See AIRRDb for an AIRR formatted version of SampleDb.

Example genotype inferrence results

Description

A data.frame of genotype inference results from inferGenotype after novel allele detection via findNovelAlleles. Source data was a collection of V(D)J immunoglobulin sequences derived from a single individual (PGP1), sequenced on the Roche 454 platform, and assigned by IMGT/HighV-QUEST to IGHV1 family alleles.

Format

A data.frame where rows correspond to genes carried by an individual and columns lists the alleles of those genes and their counts.

References

1. Gadala-Maria, et al. (2015) Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. PNAS. 112(8):E862-70.

See Also

See inferGenotype for detailed column descriptions.

Example Human IGHV germlines

Description

A character vector of all 344 human IGHV germline gene segment alleles in IMGT/GENE-DB release 2014-08-4.

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by ".") while names correspond to stripped-down IMGT allele names (e.g. "IGHV1-18*01").

References

1. Xochelli, et al. (2014) Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia. Immunogenetics. 67(1):61-6.

Example novel allele detection results

Description

A data.frame of novel allele detection results from findNovelAlleles. Source data was a collection of V(D)J immunoglobulin sequences derived from a single individual (PGP1), sequenced on the Roche 454 platform, and assigned by IMGT/HighV-QUEST to IGHV1 family alleles.

Format

A data.frame where rows correspond to alleles checked for polymorphisms and columns give results as well as paramaters used to run the test.

References

1. Gadala-Maria, et al. (2015) Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. PNAS. 112(8):E862-70.

See Also

See findNovelAlleles for detailed column descriptions.

Clean up nucleotide sequences

Description

cleanSeqs capitalizes nucleotides and replaces all characters besides c("A", "C", "G", "T", "-", ".") with "N".

Usage

cleanSeqs(seqs)

Arguments

Value

A modified vector of nucleotide sequences.

See Also

sortAlleles and updateAlleleNames can help format a list of allele names.

Examples

# Clean messy nucleotide sequences
seqs <- c("AGAT.taa-GAG...ATA", "GATACAGTXXZZAGNNPPACA")
cleanSeqs(seqs)

Find novel alleles from repertoire sequencing data

Description

findNovelAlleles analyzes mutation patterns in sequences thought to align to each germline allele in order to determine which positions might be polymorphic.

Usage

findNovelAlleles(
  data,
  germline_db,
  v_call = "v_call",
  j_call = "j_call",
  seq = "sequence_alignment",
  junction = "junction",
  junction_length = "junction_length",
  germline_min = 200,
  min_seqs = 50,
  auto_mutrange = TRUE,
  mut_range = 1:10,
  pos_range = 1:312,
  pos_range_max = NULL,
  y_intercept = 0.125,
  alpha = 0.05,
  j_max = 0.15,
  min_frac = 0.75,
  nproc = 1
)

Arguments

Details

The TIgGER allele-finding algorithm, briefly, works as follows: Mutations are determined through comparison to the provided germline. Mutation frequency at each *position* is determined as a function of *sequence-wide* mutation counts. Polymorphic positions exhibit a high mutation frequency despite sequence-wide mutation count. False positive of potential novel alleles resulting from clonally-related sequences are guarded against by ensuring that sequences perfectly matching the potential novel allele utilize a wide range of combinations of J gene and junction length.

Value

A data.frame with a row for each known allele analyzed. Besides metadata on the the parameters used in the search, each row will have either a note as to where the polymorphism-finding algorithm exited or a nucleotide sequence for the predicted novel allele, along with columns providing additional evidence.

The output contains the following columns:

• germline_call: The input (uncorrected) V call.

• note: Comments regarding the inferrence.

• polymorphism_call: The novel allele call.

• nt_substitutions: Mutations identified in the novel allele, relative to the reference germline (germline_call)

• novel_imgt: The novel allele sequence.

• novel_imgt_count: The number of times the sequence novel_imgt is found in the input data. Considers the subsequence of novel_imgt in the pos_range.

• novel_imgt_unique_j: Number of distinct J calls associated to novel_imgt in the input data. Considers the subsequence of novel_imgt in the pos_range.

• novel_imgt_unique_cdr3: Number of distinct CDR3 sequences associated with novel_imgt in the input data. Considers the subsequence of novel_imgt in the pos_range.

• perfect_match_count: Final number of sequences retained to call the new allele. These are unique sequences that have V segments that perfectly match the predicted germline in the pos_range.

• perfect_match_freq: perfect_match_count / germline_call_count

• germline_call_count: The number of sequences with the germline_call in the input data that were initially considered for the analysis.

• germline_call_freq: The fraction of sequences with the germline_call in the input data initially considered for the analysis.

• germline_imgt: Germline sequence for germline_call.

• germline_imgt_count: The number of times the germline_imgt sequence is found in the input data.

• mut_min: Minimum mutation considered by the algorithm.

• mut_max: Maximum mutation considered by the algorithm.

• mut_pass_count: Number of sequences in the mutation range.

• pos_min: First position of the sequence considered by the algorithm (IMGT numbering).

• pos_max: Last position of the sequence considered by the algorithm (IMGT numbering).

• y_intercept: The y-intercept above which positions were considered potentially polymorphic.

• y_intercept_pass: Number of positions that pass the y_intercept threshold.

• snp_pass: Number of sequences that pass the y_intercept threshold and are within the desired nucleotide range (min_seqs).

• unmutated_count: Number of unmutated sequences.

• unmutated_freq: Number of unmutated sequences over germline_imgt_count.

• unmutated_snp_j_gene_length_count: Number of distinct combinations of SNP, J gene, and junction length.

• snp_min_seqs_j_max_pass: Number of SNPs that pass both the min_seqs and j_max thresholds.

• alpha: Significance threshold to be used when constructing the confidence interval for the y-intercept.

• min_seqs: Input min_seqs. The minimum number of total sequences (within the desired mutational range and nucleotide range) required for the samples to be considered.

• j_max: Input j_max. The maximum fraction of sequences perfectly aligning to a potential novel allele that are allowed to utilize to a particular combination of junction length and J gene.

• min_frac: Input min_frac. The minimum fraction of sequences that must have usable nucleotides in a given position for that position to be considered.

The following comments can appear in the note column:

• Novel allele found: A novel allele was detected.

• Same as:: The same novel allele sequence has been identified multiple times. If this happens, the function will also throw the message 'Duplicated polymorphism(s) found'.

• Plurality sequence too rare: No sequence is frequent enough to pass the J test (j_max).

• A J-junction combination is too prevalent: Not enough J diversity (j_max).

• No positions pass y-intercept test: No positions above y_intercept.

• Insufficient sequences in desired mutational range: mut_range and pos_range.

• Not enough sequences: Not enough sequences in the desired mutational range and nucleotide range (min_seqs).

• No unmutated versions of novel allele found: All observed variants of the allele are mutated.

See Also

selectNovel to filter the results to show only novel alleles. plotNovel to visualize the data supporting any novel alleles hypothesized to be present in the data and inferGenotype and inferGenotypeBayesian to determine if the novel alleles are frequent enought to be included in the subject's genotype.

Examples

# Note: In this example, with SampleGermlineIGHV,
# which contains reference germlines retrieved on August 2014,
# TIgGER finds the allele IGHV1-8*02_G234T. This allele
# was added to IMGT as IGHV1-8*03 on March 28, 2018.

# Find novel alleles and return relevant data
novel <- findNovelAlleles(AIRRDb, SampleGermlineIGHV)
selectNovel(novel)

Determine which calls represent an unmutated allele

Description

findUnmutatedCalls determines which allele calls would represent a perfect match with the germline sequence, given a vector of allele calls and mutation counts. In the case of multiple alleles being assigned to a sequence, only the subset that would represent a perfect match is returned.

Usage

findUnmutatedCalls(allele_calls, sample_seqs, germline_db)

Arguments

Value

A vector of strings containing the members of allele_calls that represent unmutated sequences.

Examples

# Find which of the sample alleles are unmutated
calls <- findUnmutatedCalls(AIRRDb$v_call, AIRRDb$sequence_alignment, 
                            germline_db=SampleGermlineIGHV)

Generate evidence

Description

generateEvidence builds a table of evidence metrics for the final novel V allele detection and genotyping inferrences.

Usage

generateEvidence(
  data,
  novel,
  genotype,
  genotype_db,
  germline_db,
  j_call = "j_call",
  junction = "junction",
  fields = NULL
)

Arguments

Value

Returns the genotype input data.frame with the following additional columns providing supporting evidence for each inferred allele:

• field_id: Data subset identifier, defined with the input paramter fields.

• A variable number of columns, specified with the input parameter fields.

• polymorphism_call: The novel allele call.

• novel_imgt: The novel allele sequence.

• closest_reference: The closest reference gene and allele in the germline_db database.

• closest_reference_imgt: Sequence of the closest reference gene and allele in the germline_db database.

• germline_call: The input (uncorrected) V call.

• germline_imgt: Germline sequence for germline_call.

• nt_diff: Number of nucleotides that differ between the new allele and the closest reference (closest_reference) in the germline_db database.

• nt_substitutions: A comma separated list of specific nucleotide differences (e.g. 112G>A) in the novel allele.

• aa_diff: Number of amino acids that differ between the new allele and the closest reference (closest_reference) in the germline_db database.

• aa_substitutions: A comma separated list with specific amino acid differences (e.g. 96A>N) in the novel allele.

• sequences: Number of sequences unambiguosly assigned to this allele.

• unmutated_sequences: Number of records with the unmutated novel allele sequence.

• unmutated_frequency: Proportion of records with the unmutated novel allele sequence (unmutated_sequences / sequences).

• allelic_percentage: Percentage at which the (unmutated) allele is observed in the sequence dataset compared to other (unmutated) alleles.

• unique_js: Number of unique J sequences found associated with the novel allele. The sequences are those who have been unambiguously assigned to the novel allelle (polymorphism_call).

• unique_cdr3s: Number of unique CDR3s associated with the inferred allele. The sequences are those who have been unambiguously assigned to the novel allelle (polymorphism_call).

• mut_min: Minimum mutation considered by the algorithm.

• mut_max: Maximum mutation considered by the algorithm.

• pos_min: First position of the sequence considered by the algorithm (IMGT numbering).

• pos_max: Last position of the sequence considered by the algorithm (IMGT numbering).

• y_intercept: The y-intercept above which positions were considered potentially polymorphic.

• alpha: Significance threshold to be used when constructing the confidence interval for the y-intercept.

• min_seqs: Input min_seqs. The minimum number of total sequences (within the desired mutational range and nucleotide range) required for the samples to be considered.

• j_max: Input j_max. The maximum fraction of sequences perfectly aligning to a potential novel allele that are allowed to utilize to a particular combination of junction length and J gene.

• min_frac: Input min_frac. The minimum fraction of sequences that must have usable nucleotides in a given position for that position to be considered.

• note: Comments regarding the novel allele inferrence.

See Also

See findNovelAlleles, inferGenotype and genotypeFasta for generating the required input.

Examples

# Generate input data
novel <- findNovelAlleles(AIRRDb, SampleGermlineIGHV,
    v_call="v_call", j_call="j_call", junction="junction", 
    junction_length="junction_length", seq="sequence_alignment")
genotype <- inferGenotype(AIRRDb, find_unmutated=TRUE, 
                          germline_db=SampleGermlineIGHV,
                          novel=novel,
                          v_call="v_call", seq="sequence_alignment")
genotype_db <- genotypeFasta(genotype, SampleGermlineIGHV, novel)
data_db <- reassignAlleles(AIRRDb, genotype_db, 
v_call="v_call", seq="sequence_alignment")

# Assemble evidence table
evidence <- generateEvidence(data_db, novel, genotype, 
                             genotype_db, SampleGermlineIGHV,
                             j_call = "j_call", 
                             junction = "junction")

Return the nucleotide sequences of a genotype

Description

genotypeFasta converts a genotype table into a vector of nucleotide sequences.

Usage

genotypeFasta(genotype, germline_db, novel = NA)

Arguments

Value

A named vector of strings containing the germline nucleotide sequences of the alleles in the provided genotype.

See Also

inferGenotype

Examples

# Find the sequences that correspond to the genotype
genotype_db <- genotypeFasta(SampleGenotype, SampleGermlineIGHV, SampleNovel)

Determine the mutation counts from allele calls

Description

getMutCount takes a set of nucleotide sequences and their allele calls and determines the distance between that seqeunce and any germline alleles contained within the call

Usage

getMutCount(samples, allele_calls, germline_db)

Arguments

Value

A list equal in length to samples, containing the Hamming distance to each germline allele contained within each call within each element of samples

Examples

# Insert a mutation into a germline sequence
s2 <- s3 <- SampleGermlineIGHV[1]
stringi::stri_sub(s2, 103, 103) <- "G"
stringi::stri_sub(s3, 107, 107) <- "C"

sample_seqs <- c(SampleGermlineIGHV[2], s2, s3)

# Pretend that one sample sequence has received an ambiguous allele call
sample_alleles <- c(paste(names(SampleGermlineIGHV[1:2]), collapse=","),
                    names(SampleGermlineIGHV[2]),
                    names(SampleGermlineIGHV[1]))

# Compare each sequence to its assigned germline(s) to determine the distance
getMutCount(sample_seqs, sample_alleles, SampleGermlineIGHV)

Find the location of mutations in a sequence

Description

getMutatedPositions takes two vectors of aligned sequences and compares pairs of sequences. It returns a list of the nucleotide positions of any differences.

Usage

getMutatedPositions(
  samples,
  germlines,
  ignored_regex = "[\\.N-]",
  match_instead = FALSE
)

Arguments

Value

A list of the nucleotide positions of any differences between the input vectors.

Examples

# Create strings to act as a sample sequences and a reference sequence
seqs <- c("----GATA", "GAGAGAGA", "TANA")
ref <- "GATAGATA"

# Find the differences between the two
getMutatedPositions(seqs, ref)

Find mutation counts for frequency sequences

Description

getPopularMutationCount determines which sequences occur frequently for each V gene and returns the mutation count of those sequences.

Usage

getPopularMutationCount(
  data,
  germline_db,
  v_call = "v_call",
  seq = "sequence_alignment",
  gene_min = 0.001,
  seq_min = 50,
  seq_p_of_max = 1/8,
  full_return = FALSE
)

Arguments

Value

A data frame of genes that have a frequent sequence mutation count above 1.

See Also

getMutatedPositions can be used to find which positions of a set of sequences are mutated.

Examples

getPopularMutationCount(AIRRDb, SampleGermlineIGHV)

Infer a subject-specific genotype using a frequency method

Description

inferGenotype infers an subject's genotype using a frequency method. The genotype is inferred by finding the minimum number set of alleles that can explain the majority of each gene's calls. The most common allele of each gene is included in the genotype first, and the next most common allele is added until the desired fraction of alleles can be explained. In this way, mistaken allele calls (resulting from sequences which by chance have been mutated to look like another allele) can be removed.

Usage

inferGenotype(
  data,
  germline_db = NA,
  novel = NA,
  v_call = "v_call",
  seq = "sequence_alignment",
  fraction_to_explain = 0.875,
  gene_cutoff = 1e-04,
  find_unmutated = TRUE
)

Arguments

Details

Allele calls representing cases where multiple alleles have been assigned to a single sample sequence are rare among unmutated sequences but may result if nucleotides for certain positions are not available. Calls containing multiple alleles are treated as belonging to all groups. If novel is provided, all sequences that are assigned to the same starting allele as any novel germline allele will have the novel germline allele appended to their assignent prior to searching for unmutated sequences.

Value

A data.frame of alleles denoting the genotype of the subject containing the following columns:

• gene: The gene name without allele.

• alleles: Comma separated list of alleles for the given gene.

• counts: Comma separated list of observed sequences for each corresponding allele in the alleles list.

• total: The total count of observed sequences for the given gene.

• note: Any comments on the inferrence.

Note

This method works best with data derived from blood, where a large portion of sequences are expected to be unmutated. Ideally, there should be hundreds of allele calls per gene in the input.

See Also

plotGenotype for a colorful visualization and genotypeFasta to convert the genotype to nucleotide sequences. See inferGenotypeBayesian to infer a subject-specific genotype using a Bayesian approach.

Examples

# Infer IGHV genotype, using only unmutated sequences, including novel alleles
inferGenotype(AIRRDb, germline_db=SampleGermlineIGHV, novel=SampleNovel,
              find_unmutated=TRUE)

Infer a subject-specific genotype using a Bayesian approach

Description

inferGenotypeBayesian infers an subject's genotype by applying a Bayesian framework with a Dirichlet prior for the multinomial distribution. Up to four distinct alleles are allowed in an individual’s genotype. Four likelihood distributions were generated by empirically fitting three high coverage genotypes from three individuals (Laserson and Vigneault et al, 2014). A posterior probability is calculated for the four most common alleles. The certainty of the highest probability model was calculated using a Bayes factor (the most likely model divided by second-most likely model). The larger the Bayes factor (K), the greater the certainty in the model.

Usage

inferGenotypeBayesian(
  data,
  germline_db = NA,
  novel = NA,
  v_call = "v_call",
  seq = "sequence_alignment",
  find_unmutated = TRUE,
  priors = c(0.6, 0.4, 0.4, 0.35, 0.25, 0.25, 0.25, 0.25, 0.25)
)

Arguments

Details

Allele calls representing cases where multiple alleles have been assigned to a single sample sequence are rare among unmutated sequences but may result if nucleotides for certain positions are not available. Calls containing multiple alleles are treated as belonging to all groups. If novel is provided, all sequences that are assigned to the same starting allele as any novel germline allele will have the novel germline allele appended to their assignent prior to searching for unmutated sequences.

Value

A data.frame of alleles denoting the genotype of the subject with the log10 of the likelihood of each model and the log10 of the Bayes factor. The output contains the following columns:

• gene: The gene name without allele.

• alleles: Comma separated list of alleles for the given gene.

• counts: Comma separated list of observed sequences for each corresponding allele in the alleles list.

• total: The total count of observed sequences for the given gene.

• note: Any comments on the inferrence.

• kh: log10 likelihood that the gene is homozygous.

• kd: log10 likelihood that the gene is heterozygous.

• kt: log10 likelihood that the gene is trizygous

• kq: log10 likelihood that the gene is quadrozygous.

• k_diff: log10 ratio of the highest to second-highest zygosity likelihoods.

Note

This method works best with data derived from blood, where a large portion of sequences are expected to be unmutated. Ideally, there should be hundreds of allele calls per gene in the input.

References

1. Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. PNAS. 2014 111(13):4928-33.

See Also

plotGenotype for a colorful visualization and genotypeFasta to convert the genotype to nucleotide sequences. See inferGenotype to infer a subject-specific genotype using a frequency method

Examples

# Infer IGHV genotype, using only unmutated sequences, including novel alleles
inferGenotypeBayesian(AIRRDb, germline_db=SampleGermlineIGHV, novel=SampleNovel, 
                      find_unmutated=TRUE, v_call="v_call", seq="sequence_alignment")

Insert polymorphisms into a nucleotide sequence

Description

insertPolymorphisms replaces nucleotides in the desired locations of a provided sequence.

Usage

insertPolymorphisms(sequence, positions, nucleotides)

Arguments

Value

A sequence with the desired nucleotides in the provided locations.

Examples

insertPolymorphisms("HUGGED", c(1, 6, 2), c("T", "R", "I")) 

Show a colorful representation of a genotype

Description

plotGenotype plots a genotype table.

Usage

plotGenotype(
  genotype,
  facet_by = NULL,
  gene_sort = c("name", "position"),
  text_size = 12,
  silent = FALSE,
  ...
)

Arguments

Value

A ggplot object defining the plot.

See Also

inferGenotype

Examples

# Plot genotype
plotGenotype(SampleGenotype)

# Facet by subject
genotype_a <- genotype_b <- SampleGenotype
genotype_a$SUBJECT <- "A"
genotype_b$SUBJECT <- "B"
geno_sub <- rbind(genotype_a, genotype_b)
plotGenotype(geno_sub, facet_by="SUBJECT", gene_sort="pos")

Visualize evidence of novel V alleles

Description

plotNovel is be used to visualize the evidence of any novel V alleles found using findNovelAlleles. It can also be used to visualize the results for alleles that did

Usage

plotNovel(
  data,
  novel_row,
  v_call = "v_call",
  j_call = "j_call",
  seq = "sequence_alignment",
  junction = "junction",
  junction_length = "junction_length",
  pos_range_max = NULL,
  ncol = 1,
  multiplot = TRUE
)

Arguments

Details

The first panel in the plot shows, for all sequences which align to a particular germline allele, the mutation frequency at each postion along the aligned sequence as a function of the sequence-wide mutation count. Each line is a position. Positions that contain polymorphisms (rather than somatic hypermutations) will exhibit a high apparent mutation frequency for a range of sequence-wide mutation counts. The positions are color coded as follows:

• red: the position(s) pass(ess) the novel allele test

• yellow: the position(s) pass(ess) the y-intercept test but not other tests

• blue: the position(s) didn't pass the y-intercept test and was(were) not further considered

The second panel shows the nucleotide usage at each of the polymorphic positions as a function of sequence-wide mutation count. If no polymorphisms were identified, the panel will show the mutation count.

To avoid cases where a clonal expansion might lead to a false positive, TIgGER examines the combinations of J gene and junction length among sequences which perfectly match the proposed germline allele. Clonally related sequences usually share the same V gene, J gene and junction length. Requiring the novel allele to be found in different combinations of J gene and junction lengths is a proxy for requiring it to be found in different clonal lineages.

Examples

# Plot the evidence for the first (and only) novel allele in the example data
novel <- selectNovel(SampleNovel)
plotNovel(AIRRDb, novel[1, ], v_call="v_call", j_call="j_call", 
          seq="sequence_alignment", junction="junction", junction_length="junction_length", 
          multiplot=TRUE)

Read immunoglobulin sequences

Description

readIgFasta reads a fasta-formatted file of immunoglobulin (Ig) sequences and returns a named vector of those sequences.

Usage

readIgFasta(fasta_file, strip_down_name = TRUE, force_caps = TRUE)

Arguments

Value

Named vector of strings respresenting Ig alleles.

See Also

writeFasta to do the inverse.

Examples

## Not run: 
# germlines <- readIgFasta("ighv.fasta")

## End(Not run)

Correct allele calls based on a personalized genotype

Description

reassignAlleles uses a subject-specific genotype to correct correct preliminary allele assignments of a set of sequences derived from a single subject.

Usage

reassignAlleles(
  data,
  genotype_db,
  v_call = "v_call",
  seq = "sequence_alignment",
  method = "hamming",
  path = NA,
  keep_gene = c("gene", "family", "repertoire")
)

Arguments

Details

In order to save time, initial gene assignments are preserved and the allele calls are chosen from among those provided in genotype_db, based on a simple alignment to the sample sequence.

Value

A modifed input data.frame containing the best allele call from among the sequences listed in genotype_db in the v_call_genotyped column.

Examples

# Extract the database sequences that correspond to the genotype
genotype_db <- genotypeFasta(SampleGenotype, SampleGermlineIGHV, novel=SampleNovel)

# Use the personlized genotype to determine corrected allele assignments
output_db <- reassignAlleles(AIRRDb, genotype_db, v_call="v_call",
                             seq="sequence_alignment")

Select rows containing novel alleles

Description

selectNovel takes the result from findNovelAlleles and selects only the rows containing unique, novel alleles.

Usage

selectNovel(novel, keep_alleles = FALSE)

Arguments

Details

If, for instance, subject has in his genome IGHV1-2*02 and a novel allele equally close to IGHV1-2*02 and IGHV1-2*05, the novel allele may be detected by analyzing sequences that best align to either of these alleles. If keep_alleles is TRUE, both polymorphic allele calls will be retained. In the case that multiple mutation ranges are checked for the same allele, only one mutation range will be kept in the output.

Value

A data.frame containing only unique, novel alleles (if any) that were in the input.

Examples

novel <- selectNovel(SampleNovel)

Sort allele names

Description

sortAlleles returns a sorted vector of strings respresenting Ig allele names. Names are first sorted by gene family, then by gene, then by allele. Duplicated genes have their alleles are sorted as if they were part of their non-duplicated counterparts (e.g. IGHV1-69D*01 comes after IGHV1-69*01 but before IGHV1-69*02), and non-localized genes (e.g. IGHV1-NL1*01) come last within their gene family.

Usage

sortAlleles(allele_calls, method = c("name", "position"))

Arguments

Value

A sorted vector of strings respresenting Ig allele names.

See Also

Like sortAlleles, updateAlleleNames can help format a list of allele names.

Examples

# Create a list of allele names
alleles <- c("IGHV1-69D*01","IGHV1-69*01","IGHV1-2*01","IGHV1-69-2*01",
             "IGHV2-5*01","IGHV1-NL1*01", "IGHV1-2*01,IGHV1-2*05", 
             "IGHV1-2", "IGHV1-2*02", "IGHV1-69*02")

# Sort the alleles by name
sortAlleles(alleles)

# Sort the alleles by position in the locus
sortAlleles(alleles, method="pos")

Subsample repertoire

Description

subsampleDb will sample the same number of sequences for each gene, family or allele (specified with mode) in data. Samples or subjects can be subsampled indepently by setting group.

Usage

subsampleDb(
  data,
  gene = "v_call",
  mode = c("gene", "allele", "family"),
  min_n = 1,
  max_n = NULL,
  group = NULL
)

Arguments

Details

data will be split into gene, allele or family subsets (mode) from which the same number of sequences will be subsampled. If mode=gene, for each gene in the field gene from data, a maximum of max_n sequences will be subsampled. Input sequences that have multiple gene calls (ties), can be subsampled from any of their calls, but these duplicated samplings will be removed, and the final subsampled data will contain unique rows.

Value

Subsampled version of the input data.

See Also

selectNovel

Examples

subsampleDb(AIRRDb)

tigger

Description

Here we provide a Tool for Immunoglobulin Genotype Elucidation via Rep-Seq (TIgGER). TIgGER inferrs the set of Ig alleles carried by an individual (including any novel alleles) and then uses this set of alleles to correct the initial assignments given to sample sequences by existing tools.

Details

Immunoglobulin repertoire sequencing (AIRR-Seq, Rep-Seq) data is currently the subject of much study. A key step in analyzing these data involves assigning the closest known V(D)J germline alleles to the (often somatically mutated) sample sequences using a tool such as IMGT/HighV-QUEST. However, if the sample utilizes alleles not in the germline database used for alignment, this step will fail. Additionally, this alignment has an associated error rate of ~5 mutations. The purpose of TIgGER is to address these issues.

Allele detection and genotyping

• findNovelAlleles: Detect novel alleles.

• plotNovel: Plot evidence of novel alleles.

• inferGenotype: Infer an Ig genotype using a frequency approach.

• inferGenotypeBayesian: Infer an Ig genotype using a Bayesian approach.

• plotGenotype: A colorful genotype visualization.

• genotypeFasta: Convert a genotype to sequences.

• reassignAlleles: Correct allele calls.

• generateEvidence: Generate evidence for the genotype and allele detection inferrence.

Mutation handling

• getMutatedPositions: Find mutation locations.

• getMutCount: Find distance from germline.

• findUnmutatedCalls: Subset unmutated sequences.

• getPopularMutationCount: Find most common sequence's mutation count.

• insertPolymorphisms: Insert SNPs into a sequence.

Input, output and formatting

• readIgFasta: Read a fasta file of Ig sequences.

• updateAlleleNames: Correct outdated allele names.

• sortAlleles: Sort allele names intelligently.

• cleanSeqs: Standardize sequence format.

References

1. Gadala-Maria, et al. (2015) Automated analysis of high-throughput B cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. PNAS. 112(8):E862-70.

Update IGHV allele names

Description

updateAlleleNames takes a set of IGHV allele calls and replaces any outdated names (e.g. IGHV1-f) with the new IMGT names.

Usage

updateAlleleNames(allele_calls)

Arguments

Value

Vector of strings respresenting updated IGHV allele names.

Note

IGMT has removed IGHV2-5*10 and IGHV2-5*07 as it has determined they are actually alleles 02 and 04, respectively. The updated allele names are based on IMGT release 2014-08-4.

References

1. Xochelli et al. (2014) Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia. Immunogenetics. 67(1):61-6

See Also

Like updateAlleleNames, sortAlleles can help format a list of allele names.

Examples

# Create a vector that uses old gene/allele names.
alleles <- c("IGHV1-c*01", "IGHV1-f*02", "IGHV2-5*07")

# Update the alleles to the new names
updateAlleleNames(alleles)

Write to a fasta file

Description

writeFasta writes a named vector of sequences to a file in fasta format.

Usage

writeFasta(named_sequences, file, width = 60, append = FALSE)

Arguments

Value

A named vector of strings respresenting Ig alleles.

See Also

readIgFasta to do the inverse.

Examples

## Not run: 
# writeFasta(germlines, "ighv.fasta")

## End(Not run)