R/split_bams.R

In DAMEfinder: Finds DAMEs - Differential Allelicly MEthylated regions

#' Detect allele-specific methylation from a bam file
#'
#' The function takes a bam (from bismark) and vcf file for each sample. For
#' each SNP contained in the vcfile it calculates the proportion of methylated
#' reads for each CpG site at each allele. At the end it returns (saves to
#' working directory) a GRanges list, where each GRanges contains all the CpG
#' sites overlapping the reads containing a specific SNP.
#'
#' @param bamFiles List of bam files.
#' @param vcfFiles List of vcf files.
#' @param sampleNames Names of files in the list.
#' @param referenceFile fasta file used to generate the bam files. Or 
#'   \code{DNAStringSet} with DNA sequence.
#' @param coverage Minimum number of reads covering a CpG site on each allele.
#'   Default = 2.
#' @param cores Number of cores to use. See package {parallel} for description
#'   of core. Default = 1.
#' @param verbose Default = TRUE
#'
#' @return A list of GRanges for each sample. Each list is saved in a separate
#'   .rds file.
#' @examples
#' DATA_PATH_DIR <- system.file('extdata', '.', package = 'DAMEfinder')
#' get_data_path <- function(file_name) file.path(DATA_PATH_DIR, file_name)
#' bamFiles <- get_data_path('NORM1_chr19_trim.bam')
#' vcfFiles <- get_data_path('NORM1.chr19.trim.vcf')
#' sampleNames <- 'NORM1'
#' 
#' #referenceFile 
#' suppressPackageStartupMessages({library(BSgenome.Hsapiens.UCSC.hg19)})
#' genome <- BSgenome.Hsapiens.UCSC.hg19
#' seqnames(genome) <- gsub("chr","",seqnames(genome))
#' dna <- DNAStringSet(genome[[19]], use.names = TRUE)
#' names(dna) <- 19
#'
#' GRanges_list <- extract_bams(bamFiles, vcfFiles, sampleNames, dna)
#'
#' @importFrom BiocGenerics start
#' @importFrom BiocGenerics end
#' @importFrom S4Vectors mcols
#' @importFrom S4Vectors mcols<-
#' @importFrom GenomicRanges GRanges
#' @importFrom IRanges IRanges
#' @importFrom GenomicAlignments readGAlignmentPairs
#' @importFrom GenomeInfoDb seqnames
#' @importFrom BiocGenerics rowSums
#'
#' @export
extract_bams <- function(bamFiles, vcfFiles, sampleNames, referenceFile, 
  coverage = 4, cores = 1, verbose = TRUE) {
    
    if (verbose) 
        message("Reading reference file", appendLF = TRUE)
    if (typeof(referenceFile) == "character") {
        fa <- open(Rsamtools::FaFile(referenceFile, 
            index = paste0(referenceFile, ".fai")))
        
    }
    
    # get names and indeces per sample to apply to
    sample_list <- seq(from = 1, to = length(sampleNames), by = 1)
    names(sample_list) <- sampleNames
    
    lapply(sample_list, function(samp) {
        
        message(sprintf("Running sample %s", sampleNames[samp]))
        
        if (verbose) 
            message("Reading VCF file", appendLF = TRUE)
        param <- VariantAnnotation::ScanVcfParam(fixed="ALT", info=NA, geno=NA)  
        #vcf <- VariantAnnotation::readVcf(vcfFiles[samp], genome = build,
        vcf <- VariantAnnotation::readVcf(vcfFiles[samp], 
              param = param)
        bam.file <- bamFiles[samp]
        
        # message(sprintf('Processing chromosome %s',chrom))
        if (verbose) 
            message("Extracting methylation per SNP", appendLF = TRUE)
        snp.table <- parallel::mcmapply(function(t, u, v) {
          
          #t = vcf[, 1] = CHROM 
          #u = vcf[, 2] = POS 
          #v = vcf[, 4] = REF
            
            # Applying to a GRanges takes too long so I create one each
            # time
            snp <- GRanges(seqnames = t, IRanges(start = as.integer(u), 
                width = 1))
            
            # Ignore non-standard chromosomes
            # if (length(grep(t, c(as.character(seq(from = 1, to = 22, 
            #     by = 1)), "X", "Y"))) == 0) {
            #     if (verbose) message("Bad chrom", appendLF = TRUE)
            #     return(NULL)
            # }
            chrom <- t
            
            params <- Rsamtools::ScanBamParam(tag = c("MD", "XM", 
                "XR", "XG"), which = snp)
            
            alns.pairs <- readGAlignmentPairs(bam.file, use.names = TRUE, 
                param = params)
            
            alns <- unlist(alns.pairs)  #unpaired
            split <- splitReads(alns, v, snp)
            
            alt.reads <- split$alt.reads
            ref.reads <- split$ref.reads
            
            if (length(ref.reads) <= 1 | length(alt.reads) <= 
                1) {
                if (verbose) 
                    message("0 or 1 read in one or both alleles", 
                    appendLF = TRUE)
                return(NULL)
                # next
            }
            
            #### get reference and CpG
            #### positions####-----------------------------------
            
            # Get limits for calling methylation and grabing reference
            # sequence
            left <- min(start(alns))
            right <- max(end(alns))
            window <- GRanges(seqnames(snp), IRanges(left, 
                right))
            
            if (typeof(referenceFile) == "S4") {
                dna <- referenceFile[window]
                
            } else if (typeof(referenceFile) == "character") {
                # open reference seq to get correct CpG locations within that
                # reference chunk
                dna <- Rsamtools::scanFa(fa, param = window)
            }
            
            cgsite <- stringr::str_locate_all(dna, "CG")[[1]][, 
                1]  #also look at GpCs?
            
            if (length(cgsite) < 1) {
                if (verbose) 
                  message("No CpG sites associated to this SNP", 
                      appendLF = TRUE)
                return(NULL)
                # next
            }
            mepos <- cgsite/Biostrings::nchar(dna)  #location of CpG sites
            
            ##### Use MD tag from bam to extract methylation status for each
            ##### site detected above ####----------------------------
            conversion <- vapply(alns.pairs, function(x) {
                # change C locations for G locations if reference context is
                # different
                XGcondition <- mcols(x)$XG[1]
                if (XGcondition == "GA") {
                  cgsite <- cgsite + 1
                }
                
                # fuse pair info in one row
                conversion <- matrix(NA, 1, length(cgsite))
                
                read.start <- start(x) - left + 1  #start of read
                read.end <- end(x) - left + 1  #end
                
                for (pair in c(1, 2)) {
                  something <- mcols(x[pair])$MD
                  tag <- getMD(something)
                  MDtag <- tag$MDtag
                  nucl.num <- tag$nucl.num
                  
                  # Get the locations from the specific read: Count along the
                  # numerical MDtag until you reach the actual CpG start Fill
                  # in conversion table with meth state for each read 1 is
                  # unmethylated 2 is methylated NA is not in read
                  
                  count <- 0
                  this.mepos <- cgsite - read.start[pair] + 1
                  
                  for (p in seq_along(this.mepos)) {
                    if (is.na(conversion[1, p])) {
                      if (cgsite[p] > read.end[pair] || cgsite[p] < 
                        read.start[pair]) {
                        conversion[1, p] <- NA
                        next
                      } else {
                        for (i in seq_along(nucl.num)) {
                          count <- count + nucl.num[i]
                          if (count >= this.mepos[p]) {
                            count <- 0
                            break
                          }
                        }
                        
                # if there is a mismatch, the MDtag shows what the base is in
                # the ref. So, if it shows a C or G, it means that the ref is
                # C or G, and the read has T or A, therefore it is
                # unmethylated (converted)
                        if (MDtag[i] %in% c("C", "G")) {
                          conversion[1, p] <- 1
                        } else {
                          conversion[1, p] <- 2
                        }
                      }
                    } else {
                      next
                    }
                  }
                }
                return(conversion)
            }, double(length(cgsite)))
            
            # Return methylated reads and total coverage for each allele
            
            if (is.null(dim(conversion))) {
                ref.conv <- conversion[names(conversion) %in% 
                  ref.reads]
                alt.conv <- conversion[names(conversion) %in% 
                  alt.reads]
                
                ref.cov <- sum(!is.na(ref.conv))
                alt.cov <- sum(!is.na(alt.conv))
                
                ref.meth <- sum(!is.na(ref.conv) & ref.conv == 
                  2)
                alt.meth <- sum(!is.na(alt.conv) & alt.conv == 
                  2)
                
            } else {
                ref.conv <- conversion[, colnames(conversion) %in% 
                  ref.reads]
                alt.conv <- conversion[, colnames(conversion) %in% 
                  alt.reads]
                
                ref.cov <- rowSums(!is.na(ref.conv))
                alt.cov <- rowSums(!is.na(alt.conv))
                
                ref.meth <- rowSums(!is.na(ref.conv) & 
                  ref.conv == 2)
                alt.meth <- rowSums(!is.na(alt.conv) & 
                  alt.conv == 2)
            }
            
            # Build GRanges
            GR <- GRanges(gsub("chr", "", chrom), IRanges(start = left + 
                cgsite, width = 1))
            mcols(GR)$cov.ref <- ref.cov
            mcols(GR)$cov.alt <- alt.cov
            mcols(GR)$meth.ref <- ref.meth
            mcols(GR)$meth.alt <- alt.meth
            mcols(GR)$snp <- paste0(seqnames(snp), 
                ".", start(snp))
            
            # Keep CpG sites where each allele has at least 'coverage'
            # reads.
            filt <- rowSums(cbind(GR$cov.ref, GR$cov.alt) >= 
                coverage) >= 2
            if (sum(filt) < 2) {
                if (verbose) 
                  message("No CpG sites sufficiently covered at this SNP", 
                    appendLF = TRUE)
                return(NULL)
            }
            
            gr <- GR[filt]
            return(gr)
            
        #}, t = vcf[, 1], u = vcf[, 2], v = vcf[, 4], SIMPLIFY = FALSE, 
        }, t = as.character(seqnames(vcf)), 
           u = start(vcf), 
           v = as.character(VariantAnnotation::ref(vcf)), SIMPLIFY = FALSE, 
            USE.NAMES = FALSE, mc.cores = cores)
        
        message(sprintf("Done with sample %s", sampleNames[samp]))
        return(snp.table)
    })
}