Created: 20 February, 2013 Last Modified: May 01, 2024
Whole genome Variant Call Format (VCF) files are very large, typically containing millions of called variants, one call per line of text. The actual number of variants relevant to a particular study (of a disease such as breast cancer, for instance) will often be far fewer. Thus the first task one faces when analyzing a whole genome VCF file is to identify and extract the relatively few variants which may be of interest, excluding all others. This vignette illustrates several techniques for doing this.
We demonstrate three methods: filtering by genomic region, filtering on attributes of each specific variant call, and intersecting with known regions of interest (exons, splice sites, regulatory regions, etc.). We are primarily concerned with the latter two. However, in order to create the small VCF data file we use here for demonstration purposes, we employed genomic region filtering, reducing a very large whole genome two-sample breast cancer VCF file of fourteen million calls, to a file containing fewer than ten thousand calls in a one million base pair region of chromosome 7. For the sake of reproducibility, and for completeness of exposition, we will illustrate this first step also.
Complete Genomics Inc. states:
To provide the scientific community with public access to data generated from two paired tumor/normal cancer samples, Complete Genomics sequenced and analyzed cell-line samples of patients with breast cancer (invasive ductal carcinomas). The cell line-derived DNA are housed at ATCC. Samples have been sequenced to an average genome-wide coverage of 123X for three of the samples, and 92X for for the fourth sample.1 Data generated withversion 2.0.0.32 of the Complete Genomics assembly software, on high molecular weight genomic DNA isolated from the HCC1187 breast carcinoma cell line ATCC CRL 2322. Sequencing methods documented in (Drmanac et al., 2010)
A small (1M base) subset of this data is included in the current package, and used in the code presented below.
We identified this subset in a prior exploration of the full data set (work not shown), learning that variants of biological interest suitable for our purpose are found in a 1M base pair region of chromosome seven. The appendix to this document (see below) shows the few lines of code required to extract variant calls in that small region, from the very large file obtained from Complete Genomics (somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz) and write them to a new, small VCF file.
filterVcf MethodThe filterVcf method reads (by chunks, about which more below) through a
possibly very large VCF file to write a new, smaller VCF file which includes
only those variant calling rows which meet the criteria specified in
prefilters and filters.
Reading “by chunks” is accomplished using a tabix (Li, 2011) file. The a
yieldSize argument specifies how many variant lines are read into memory at
each iteration.
tabix.file <- TabixFile(file.gz, yieldSize=10000)
filterVcf(tabix.file, genome, destination.file,
prefilters=prefilters, filters=filters)in which
Prefilters are conceptually very simple. They are functions which will be called
with a single argument, a vector of character strings, each of which is an
unparsed variant call line, as read from the input VCF file. We use grepl to
return a logical vector equal in length to the incoming vector of unparsed VCF
lines. Each prefilter and filter is called repeatedly, with lines supplied on
each invocation. filterVcf calls these functions repeatedly until the input
file is exhausted.
Notice how the logic of these prefilters is very simple, using grepl to do
fast, simple, fixed pattern matching:
isGermlinePrefilter <- function(x) {
grepl("Germline", x, fixed=TRUE)
}
notInDbsnpPrefilter <- function(x) {
!(grepl("dbsnp", x, fixed=TRUE))
}Filters are more sophisticated than prefilters in that they assess parsed
variant call lines for possible inclusion. Such parsing is intrinsically
expensive but will be performed only on those lines which passed the prefilters.
Therefore it pays to eliminate as many lines as possible using prefilters.
Filters are useful when there exists detailed criteria for inclusion and
exclusion. This can be seen below, especially in the allelicDepth function.
Each filter must be written to return a logical vector as long as the number of
rows in the input VCF argument; be sure your filter works with 0-row VCF
instances.
## We will use isSNV() to filter only SNVs
allelicDepth <- function(x) {
## ratio of AD of the "alternate allele" for the tumor sample
## OR "reference allele" for normal samples to total reads for
## the sample should be greater than some threshold (say 0.1,
## that is: at least 10% of the sample should have the allele
## of interest)
ad <- geno(x)$AD
tumorPct <- ad[,1,2,drop=FALSE] / rowSums(ad[,1,,drop=FALSE])
normPct <- ad[,2,1, drop=FALSE] / rowSums(ad[,2,,drop=FALSE])
test <- (tumorPct > 0.1) | (normPct > 0.1)
as.vector(!is.na(test) & test)
}FilterRules allow you to combine a list of filters, or of prefilters so that
they may be passed as parameters to filterVcf. We use them here to combine the
isGermlinePrefilter with the notInDbsnpPrefilter, and the isSNV with the
AD filter.
library(VariantAnnotation)
prefilters <- FilterRules(list(germline=isGermlinePrefilter,
dbsnp=notInDbsnpPrefilter))
filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth))file.gz <- system.file("extdata", "chr7-sub.vcf.gz",
package="VariantAnnotation")
file.gz.tbi <- system.file("extdata", "chr7-sub.vcf.gz.tbi",
package="VariantAnnotation")
destination.file <- tempfile()
tabix.file <- TabixFile(file.gz, yieldSize=10000)
filterVcf(tabix.file, "hg19", destination.file,
prefilters=prefilters, filters=filters, verbose=TRUE)We have now created a file containing 29 novel, Germline, SNP variant calls, each with a reasonable allelic depth, extracted from the 3808 calls in chr7-sub.vcf. We examine those variants for overlap with regulatory regions, turning to the ENCODE project and using Bioconductor’s AnnotationHub.
The ENCODE project is a large, long-term effort to build a “parts list” of all the functional elements in the human genome. They have recently focused on regulatory elements. We use the Bioconductor AnnotationHub to download regulatory regions reported for a breast cancer cell line, with which to identify possibly functional, and possibly clinically relevant SNVs in the breast cancer tumor/normal genome we have been examining.
The AnnotationHub is a recent addition to Bioconductor that facilitates access to genome-scale resources like ENCODE.
The MCF-7 Breast Cancer Cell line was established forty years ago, and has since played a dominant role in breast cancer cell line studies. The University of Washington reports transcription factor binding sites (TFBS) for the CTCF protein, which often acts as a negative regulator of transcription via chromatin structure modifications Though the MCF-7 cell line is an imperfect match to the HCC1187 cell line sequenced by Complete Genomics, we combine these two breast-cancer related data sets here, didactically, in this exercise, to highlight the importance of cell-type-specific regulatory regions, and of the availability of such data from ENCODE. We shall see a SNP in the intronic binding region of the cancer-related gene, EGFR.
library(AnnotationHub)
hub <- AnnotationHub()
id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak"))
mcf7.gr <- hub[[tail(id, 1)]]vcf <- readVcf(destination.file, "hg19")
seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="")
ov.mcf7 <- findOverlaps(vcf, mcf7.gr)There is just one SNV which overlaps with the MCF-7 regulatory regions. Find out where, if anywhere, it fits within a gene model.
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
locateVariants(vcf[6,], txdb, AllVariants())## GRanges object with 10 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART LOCEND
## <Rle> <IRanges> <Rle> | <factor> <integer> <integer>
## 7:55212133_C/T chr7 55212133 + | intron 168915 168915
## 7:55212133_C/T chr7 55212133 + | intron 162109 162109
## 7:55212133_C/T chr7 55212133 + | intron 136465 136465
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 124739 124739
## 7:55212133_C/T chr7 55212133 + | intron 34146 34146
## QUERYID TXID CDSID GENEID
## <integer> <character> <IntegerList> <character>
## 7:55212133_C/T 1 25129 2898
## 7:55212133_C/T 1 25130 2898
## 7:55212133_C/T 1 25131 2898
## 7:55212133_C/T 1 25132 389421
## 7:55212133_C/T 1 25133 389421
## 7:55212133_C/T 1 25134 154442
## 7:55212133_C/T 1 25135 639
## 7:55212133_C/T 1 25136 639
## 7:55212133_C/T 1 25138 202
## 7:55212133_C/T 1 25139 202
## PRECEDEID FOLLOWID
## <CharacterList> <CharacterList>
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## 7:55212133_C/T
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThis case study begins, somewhat artificially, with a very short region of chromosome seven, a section which we knew, from previous exploration, held an intronic regulatory SNV for EGFR, a receptor tryosine kinase implicated in some cancers. Though artificial, the case study illustrates all of the steps needed for broader, realistic surveys of whole genome variation data:
The most basic form of VCF file filtering is by genomic region. We demonstrate that here, extracting variant calls in a 1M base region of chromosome 7, writing them to a new file, compressing and then indexing that file. These steps created the small VCF file which accompanies this vignette, and is used in the code shown above.
Note that this code is NOT executed during the creation of this vignette: we do not supply the very large VCF file that it operates on. This code is here for tutorial purposes only, showing how you can filter by genomic region with a possibly large VCF file of your own.
library(VariantAnnotation)
file.gz <- "somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz"
stopifnot(file.exists(file.gz))
file.gz.tbi <- paste(file.gz, ".tbi", sep="")
if(!(file.exists(file.gz.tbi)))
indexTabix(file.gz, format="vcf")
start.loc <- 55000000
end.loc <- 56000000
chr7.gr <- GRanges("7", IRanges(start.loc, end.loc))
params <- ScanVcfParam(which=chr7.gr)
vcf <- readVcf(TabixFile(file.gz), "hg19", params)
writeVcf(vcf, "chr7-sub.vcf")
bgzip("chr7-sub.vcf", overwrite=TRUE)
indexTabix("chr7-sub.vcf.gz", format="vcf")This code creates the small gzipped vcf and index files used in the examples above.
Drmanac,R. et al. (2010) Human genome sequencing using unchained base reads on self-assembling dna nanoarrays. Science, 327, 78–81.
Li,H. (2011) Tabix: Fast retrieval of sequence features from generic tab-delimited files. Bioinformatics, 27, 718–719.