r-bioc-variantannotation 1.24.2-1 / usr / lib / R / site-library / VariantAnnotation / doc / filterVcf.Rnw

%\VignetteIndexEntry{2. Using filterVcf to Select Variants from VCF Files}
%\VignetteDepends{VariantAnnotation}
%\VignettePackage{VariantAnntation}

\documentclass{article}

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@ 

\title{2. Using \texttt{filterVcf()} to Select Variants from VCF Files}
\author{Paul Shannon}
\date{Created: 20 February, 2013 \\ Last modified: 22 October, 2015}

\begin{document}

\maketitle
\tableofcontents

\section{Introduction}

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.

\section{The Data: Paired Tumor/Normal Breast Cancer Variants}

Complete Genomics
Inc.\footnote{\url{http://www.completegenomics.com/public-data/cancer-data}}
states:
%% two whole genome VCF files[\cite{drmanac2010human}], accompanied by this description:

\begin{quote}
  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.\footnote{Data generated with version 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].}
\end{quote}

A small (1M base) subset of this data is included in the current
package, and used in the code presented below.

\section{Filter by Genomic Region}

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
(\emph{"somaticVcfBeta-HCC1187-H-200-37-ASM-T1-N1.vcf.gz"}) and write
them to a new, small VCF file.

\section{Introducing the \Rfunction{filterVcf} Method}

The \Rfunction{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 \Robject{prefilters} and 
\Robject{filters}.  

Reading ``by chunks'' is accomplished using a tabix
[\cite{li2011tabix}] file.  The \Rcode{yieldSize} argument specifies
how many variant lines are read into memory at each iteration.

\begin{verbatim}
tabix.file <- TabixFile(file.gz, yieldSize=10000)
filterVcf(tabix.file, genome, destination.file,
          prefilters=prefilters, filters=filters)
\end{verbatim}

in which 
\begin{enumerate}
\item \emph{file.gz}: a gzipped vcf file with an accompanying Tabix
  index file.
\item \emph{yieldSize}: the number of text (call variant) lines to
  read at a time.
\item \emph{genome}: a string indicating the genome assembly, e.g.,
  ``hg19''.
\item \emph{prefilters}: one or more simple string-based filtering
  functions, each of which returns a logical vector corresponding to
  the vcf rows it will be passed (as simple character strings).
\item \emph{filters}: one or more filtering function, each of which
  returns a logical vector, corresponding to the list of parsed vcf
  structures it will be passed.
\end{enumerate}

\subsection{Prefilters}

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 \emph{unparsed} variant call line, as read from
the input VCF file.  We use \Rfunction{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 \Rcode{yieldSize}
lines supplied on each invocation.  \Rfunction{filterVcf} calls these
functions repeatedly until the input file is exhausted.

Notice how the logic of these prefilters is very simple, using
\Rfunction{grepl} to do fast, simple, fixed pattern matching:

<<prefilters>>=
isGermlinePrefilter <- function(x) {
    grepl("Germline", x, fixed=TRUE)
}

notInDbsnpPrefilter <- function(x) {
    !(grepl("dbsnp", x, fixed=TRUE))
}
@ 

\subsection{Filters}

Filters are more sophisticated than prefilters in that they assess
\emph{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 \Rfunction{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.

<<filters>>=
## 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)
}
@ 

\subsection{FilterRules}

\Robject{FilterRules} allow you to combine a list of filters, or of
prefilters so that they may be passed as parameters to
\emph{filterVcf}.  We use them here to combine the
\emph{isGermlinePrefilter} with the \emph{notInDbsnpPrefilter}, and
the \emph{isSNV} with the \emph{AD} filter.

<<createFilterRules>>=
library(VariantAnnotation)
prefilters <- FilterRules(list(germline=isGermlinePrefilter, 
                               dbsnp=notInDbsnpPrefilter))
filters <- FilterRules(list(isSNV=isSNV, AD=allelicDepth))
@ 

\subsection{Create the Filtered file}

<<createFilteredFile>>=
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)
@ 

\section{Look for SNPs in Regulatory Regions}

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 \emph{chr7-sub.vcf}.  We examine those variants for overlap
with regulatory regions, turning to the ENCODE project and using
\Bioconductor's \Biocpkg{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
\Biocpkg{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 \Biocpkg{AnnotationHub} is a recent addition to \Bioconductor{}
that facilitates access to genome-scale resources like ENCODE.

\subsection{Load CTCF Transcription Factor Binding Regions Identified
  in MCF-7 Breast Cancer Cell Line}

The \emph{MCF-7} Breast Cancer Cell line
(\url{http://en.wikipedia.org/wiki/MCF-7}) 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.

<<mcf7regulatoryRegions>>=
library(AnnotationHub)
hub <- AnnotationHub()
id <- names(query(hub, "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak"))
mcf7.gr <- hub[[tail(id, 1)]]
@ 

\subsection{Find SNPs in CTCF Binding Regions}

<<findOverlaps>>=
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.

<<locateVariant>>=
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene 
locateVariants(vcf[6,], txdb, AllVariants())
@ 

\section{Conclusion}

This 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:

\begin{enumerate}
\item Filter by genomic coordinates.
\item Filter to extract only those variant calls which meet these
  criteria: Germline, novel (not in dbSnp), consisting of a single
  nucleotide, of sufficient allelic depth.
\item Intersect these variants with recognized DNA elements.  In our
  case, we used short TFBS regulatory regions, but the same method can
  be used with exons, splice sites, DNaseI footprints, methylation
  sites, etc.
\end{enumerate}

\section{Appendix: Filter by Genomic Region}

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 \emph{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.

\begin{verbatim}
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")
\end{verbatim}  

This code creates the small gzipped vcf and index files used in the
examples above.

\nocite{*}

\end{document}