variantReport {systemPipeR} | R Documentation

## Generate Variant Report

### Description

Functions for generating tabular variant reports including genomic context annotations and confidence statistics of variants. The annotations are obtained with utilities provided by the `VariantAnnotation` package and the variant statistics are retrieved from the input VCF files.

### Usage

    ## Variant report
    variantReport(args, txdb, fa, organism)

    ## Combine variant reports
    combineVarReports(args, filtercol, ncol = 15)

    ## Create summary statistics of variants
    varSummary(args)

### Arguments

`args` | Object of class `SYSargs` or `SYSargs2`. The paths of the input VCF files are specified under `infile1(args)` and the paths of the output files under `outfile1(args)` or `output(args)`.
`txdb` | Annotation data stored as `TranscriptDb` object, which can be obtained from GFF/GTF files, BioMart, Bioc Annotation packages, UCSC, etc. For details see the vignette of the `GenomicFeatures` package. It is important to use here matching versions of the `txdb` and `fa` objects. The latter is the genome sequence used for read mapping and variant calling.
`fa` | `FaFile` object pointing to the sequence file of the corresponding reference genome stored in FASTA format or a `BSgenome` instance.
`organism` | Character vector specifying the organism name of the reference genome.
`filtercol` | Named character vector containing in the name field the column titles to filter on, and in the data field the corresponding values to include in the report. For instance, the setting `filtercol=c(Consequence="nonsynonymous")` will include only nonsysynonymous variances listed in the `Consequence` column. To omit the filtering step, one can use the setting `filtercol="All"`.
`ncol` | Integer specifying the number of columns in the tabular input files. Default is set to 15.

### Value

Tabular output files.

### Author(s)

Thomas Girke

### See Also

`filterVars`

### Examples

    ## Alignment with BWA (sequentially on single machine)
    param <- system.file("extdata", "bwa.param", package="systemPipeR")
    targets <- system.file("extdata", "targets.txt", package="systemPipeR")
    args <- systemArgs(sysma=param, mytargets=targets)
    sysargs(args)[1]

    ## Not run:
    system("bwa index -a bwtsw ./data/tair10.fasta")
    bampaths <- runCommandline(args=args)

    ## Alignment with BWA (parallelized on compute cluster)
    resources <- list(walltime=120, ntasks=1, ncpus=cores(args), memory=1024)
    reg <- clusterRun(args, conffile=".batchtools.conf.R", template="batchtools.slurm.tmpl", Njobs=18, runid="01", resourceList=resources)

    ## Variant calling with GATK
    ## The following creates in the inital step a new targets file
    ## (targets_bam.txt). The first column of this file gives the paths to
    ## the BAM files created in the alignment step. The new targets file and the
    ## parameter file gatk.param are used to create a new SYSargs
    ## instance for running GATK. Since GATK involves many processing steps, it is
    ## executed by a bash script gatk_run.sh where the user can specify the
    ## detailed run parameters. All three files are expected to be located in the
    ## current working directory. Samples files for gatk.param and
    ## gatk_run.sh are available in the subdirectory ./inst/extdata/ of the
    ## source file of the systemPipeR package.
    writeTargetsout(x=args, file="targets_bam.txt")
    system("java -jar CreateSequenceDictionary.jar R=./data/tair10.fasta O=./data/tair10.dict")
    # system("java -jar /opt/picard/1.81/CreateSequenceDictionary.jar R=./data/tair10.fasta O=./data/tair10.dict")
    args <- systemArgs(sysma="gatk.param", mytargets="targets_bam.txt")
    resources <- list(walltime=120, ntasks=1, ncpus=cores(args), memory=1024)
    reg <- clusterRun(args, conffile=".batchtools.conf.R", template="batchtools.slurm.tmpl", Njobs=18, runid="01", resourceList=resources)
    writeTargetsout(x=args, file="targets_gatk.txt")

    ## Variant calling with BCFtools
    ## The following runs the variant calling with BCFtools. This step requires in
    ## the current working directory the parameter file sambcf.param and the
    ## bash script sambcf_run.sh.
    args <- systemArgs(sysma="sambcf.param", mytargets="targets_bam.txt")
    resources <- list(walltime=120, ntasks=1, ncpus=cores(args), memory=1024)
    reg <- clusterRun(args, conffile=".batchtools.conf.R", template="batchtools.slurm.tmpl", Njobs=18, runid="01", resourceList=resources)
    writeTargetsout(x=args, file="targets_sambcf.txt")

    ## Filtering of VCF files generated by GATK
    args <- systemArgs(sysma="filter_gatk.param", mytargets="targets_gatk.txt")
    filter <- "totalDepth(vr) >= 2 & (altDepth(vr) / totalDepth(vr) >= 0.8) & rowSums(softFilterMatrix(vr))==4"
    # filter <- "totalDepth(vr) >= 20 & (altDepth(vr) / totalDepth(vr) >= 0.8) & rowSums(softFilterMatrix(vr))==6"
    filterVars(args, filter, varcaller="gatk", organism="A. thaliana")
    writeTargetsout(x=args, file="targets_gatk_filtered.txt")

    ## Filtering of VCF files generated by BCFtools
    args <- systemArgs(sysma="filter_sambcf.param", mytargets="targets_sambcf.txt")
    filter <- "rowSums(vr) >= 2 & (rowSums(vr[,3:4])/rowSums(vr[,1:4]) >= 0.8)"
    # filter <- "rowSums(vr) >= 20 & (rowSums(vr[,3:4])/rowSums(vr[,1:4]) >= 0.8)"
    filterVars(args, filter, varcaller="bcftools", organism="A. thaliana")
    writeTargetsout(x=args, file="targets_sambcf_filtered.txt")

    ## Annotate filtered variants from GATK
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_gatk_filtered.txt")
    txdb <- loadDb("./data/tair10.sqlite")
    fa <- FaFile(systemPipeR::reference(args))
    variantReport(args=args, txdb=txdb, fa=fa, organism="A. thaliana")

    ## Annotate filtered variants from BCFtools
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_sambcf_filtered.txt")
    txdb <- loadDb("./data/tair10.sqlite")
    fa <- FaFile(systemPipeR::reference(args))
    variantReport(args=args, txdb=txdb, fa=fa, organism="A. thaliana")

    ## Combine results from GATK
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_gatk_filtered.txt")
    combineDF <- combineVarReports(args, filtercol=c(Consequence="nonsynonymous"))
    write.table(combineDF, "./results/combineDF_nonsyn_gatk.xls", quote=FALSE, row.names=FALSE, sep="\t")

    ## Combine results from BCFtools
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_sambcf_filtered.txt")
    combineDF <- combineVarReports(args, filtercol=c(Consequence="nonsynonymous"))
    write.table(combineDF, "./results/combineDF_nonsyn_sambcf.xls", quote=FALSE, row.names=FALSE, sep="\t")

    ## Summary for GATK
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_gatk_filtered.txt")
    write.table(varSummary(args), "./results/variantStats_gatk.xls", quote=FALSE, col.names = NA, sep="\t")

    ## Summary for BCFtools
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_sambcf_filtered.txt")
    write.table(varSummary(args), "./results/variantStats_sambcf.xls", quote=FALSE, col.names = NA, sep="\t")

    ## Venn diagram of variants
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_gatk_filtered.txt")
    varlist <- sapply(names(outpaths(args))[1:4], function(x) as.character(read.delim(outpaths(args)[x])$VARID))
    vennset_gatk <- overLapper(varlist, type="vennsets")
    args <- systemArgs(sysma="annotate_vars.param", mytargets="targets_sambcf_filtered.txt")
    varlist <- sapply(names(outpaths(args))[1:4], function(x) as.character(read.delim(outpaths(args)[x])$VARID))
    vennset_bcf <- overLapper(varlist, type="vennsets")
    vennPlot(list(vennset_gatk, vennset_bcf), mymain="", mysub="GATK: red; BCFtools: blue", colmode=2, ccol=c("blue", "red"))

    ## End(Not run)

[Package systemPipeR version 1.20.0 Index]