Document the alignment script
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@ -1,6 +1,10 @@
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library(Biostrings)
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library(Biostrings)
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library(parallel)
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library(parallel)
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#' Import and process the TCR and VJ sequences
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#'
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#' @param file A file path with the sequences after applying a read simulator
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#' @return A \code{list} with the TCR sequences and VJ sequences
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parse_data <- function(file) {
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parse_data <- function(file) {
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reversed_sequences <- Biostrings::readQualityScaledDNAStringSet(file)
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reversed_sequences <- Biostrings::readQualityScaledDNAStringSet(file)
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sequences <- Biostrings::reverseComplement(reversed_sequences)
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sequences <- Biostrings::reverseComplement(reversed_sequences)
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@ -11,6 +15,10 @@ parse_data <- function(file) {
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return(list(sequences, vj_segments))
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return(list(sequences, vj_segments))
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}
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}
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#' Extracts the VJ metadata from the sequences read identifier
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#'
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#' @param metadata The read identifier of a sequence
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#' @return A \code{list} with the V and J gene identifier
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parse_metadata <- function(metadata) {
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parse_metadata <- function(metadata) {
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id_elements <- unlist(strsplit(metadata, split = " "))
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id_elements <- unlist(strsplit(metadata, split = " "))
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v_identifier <- id_elements[2]
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v_identifier <- id_elements[2]
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@ -18,12 +26,24 @@ parse_metadata <- function(metadata) {
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return(list(v_id = v_identifier, j_id = j_identifier))
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return(list(v_id = v_identifier, j_id = j_identifier))
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}
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}
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#' Fetches the sequence that matches the VJ gene identifier
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#'
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#' @param names The names of the VJ sequences
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#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
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#' @param id The read identifier of a sequence
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#' @return A \code{character} containing the gene sequence
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match_id_sequence <- function(names, vdj_segments, id) {
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match_id_sequence <- function(names, vdj_segments, id) {
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matches <- grep(names, pattern = id)
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matches <- grep(names, pattern = id)
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row <- matches[1]
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row <- matches[1]
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return(as.character(vdj_segments[row]))
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return(as.character(vdj_segments[row]))
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}
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}
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#' Gets the V and J sequences for a particular read identifier
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#'
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#' @param metadata The read identifier of a sequence
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#' @param names The names of the VJ sequences
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#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
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#' @return A \code{list} with the V and J sequences
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get_vj_sequence <- function(metadata, names, vdj_segments) {
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get_vj_sequence <- function(metadata, names, vdj_segments) {
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identifiers <- parse_metadata(metadata)
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identifiers <- parse_metadata(metadata)
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v_sequence <- match_id_sequence(names, vdj_segments, id = identifiers["v_id"])
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v_sequence <- match_id_sequence(names, vdj_segments, id = identifiers["v_id"])
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@ -31,6 +51,11 @@ get_vj_sequence <- function(metadata, names, vdj_segments) {
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return(list(v_seq = v_sequence, j_seq = j_sequence))
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return(list(v_seq = v_sequence, j_seq = j_sequence))
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}
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}
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#' Obtains the VJ sequences for all the TCR sequences
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#'
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#' @param sequences A \code{QualityScaledDNAStringSet} with the TCR sequences
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#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
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#' @return A \code{data.frame} with the V and J sequences
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fetch_vj_sequences <- function(sequences, vdj_segments) {
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fetch_vj_sequences <- function(sequences, vdj_segments) {
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vj_sequences <- sapply(names(sequences),
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vj_sequences <- sapply(names(sequences),
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names(vdj_segments),
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names(vdj_segments),
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@ -41,6 +66,11 @@ fetch_vj_sequences <- function(sequences, vdj_segments) {
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return(results)
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return(results)
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}
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}
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#' Perform a pairwise alignment of a sequence with the canonical V or J sequence
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#'
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#' @param sequence A \code{DNAString} containing the TCR sequences
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#' @param vdj_segment A \code{DNAString} containing the V or J sequence
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#' @return A \code{PairwiseAlignments}
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align_sequence <- function(sequence, vdj_segment) {
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align_sequence <- function(sequence, vdj_segment) {
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return(Biostrings::pairwiseAlignment(
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return(Biostrings::pairwiseAlignment(
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subject = sequence,
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subject = sequence,
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@ -50,6 +80,13 @@ align_sequence <- function(sequence, vdj_segment) {
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))
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))
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}
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}
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#' Computes the coordinate shift of the Cysteine due to indels
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#'
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#' @param insertion An \code{IRanges} containing the insertions
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#' @param deletion An \code{IRanges} containing the deletions
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#' @param cys A \code{list} with the Cysteine coordinates
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#' @param alignment A \code{PairwiseAlignments}
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#' @return A \code{list} with the delta of the Cysteine coordinates
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handle_indels <- function(insertion, deletion, cys, alignment) {
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handle_indels <- function(insertion, deletion, cys, alignment) {
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ins_start <- sum(Biostrings::width(deletion[start(deletion) <= cys$start]))
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ins_start <- sum(Biostrings::width(deletion[start(deletion) <= cys$start]))
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ins_end <- sum(Biostrings::width(deletion[end(deletion) <= cys$end]))
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ins_end <- sum(Biostrings::width(deletion[end(deletion) <= cys$end]))
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@ -60,6 +97,10 @@ handle_indels <- function(insertion, deletion, cys, alignment) {
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return(list("start" = ins_start - gaps, "end" = ins_end - gaps))
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return(list("start" = ins_start - gaps, "end" = ins_end - gaps))
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}
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}
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#' Find the coordinates of the first Cysteine of the HVR
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#'
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#' @param alignment A \code{PairwiseAlignments}
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#' @return A \code{list} with the Cysteine coordinates
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get_cys_coordinates <- function(alignment) {
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get_cys_coordinates <- function(alignment) {
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cys <- list("start" = 310, "end" = 312)
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cys <- list("start" = 310, "end" = 312)
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insertion <- unlist(Biostrings::insertion(alignment))
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insertion <- unlist(Biostrings::insertion(alignment))
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@ -70,6 +111,12 @@ get_cys_coordinates <- function(alignment) {
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return(list("start" = cys_start, "end" = cys_end))
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return(list("start" = cys_start, "end" = cys_end))
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}
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}
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#' Delimit the hypervariable region (HVR) for each TCR sequence
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#'
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#' @param sequences A \code{QualityScaledDNAStringSet} with the TCR sequences
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#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
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#' @param cores Number of cores to apply multiprocessing
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#' @return A \code{QualityScaledDNAStringSet} containing the HVR
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get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
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get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
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df <- fetch_vj_sequences(sequences, vdj_segments)
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df <- fetch_vj_sequences(sequences, vdj_segments)
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v_alignment <- parallel::mcmapply(sequences,
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v_alignment <- parallel::mcmapply(sequences,
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