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\title{filterVcf:  Extract Variants of Interest from a Large VCF File}
\author{Paul Shannon}

\begin{document}

\maketitle
\tableofcontents

\section{Introduction}

<<setup, echo=FALSE>>=
options(width=80, prompt = " ", continue = " ")
@ 

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[\citet{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 \Rmethod{filterVcf} Method}

The \Rmethod{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 [\citet{li2011tabix}] file.
The \Rfunarg{yieldSize} argument specifies how many variant lines are read
into memory at each iteration.

\begin{small}

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

in which 
\begin{packed_enum}
  \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{packed_enum}

\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 \Rfunarg{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:


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

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

\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.


\begin{small}
<<filters>>=
isSnp <- function(x) {
    refSnp <- nchar(ref(x)) == 1L
    a <- alt(x)
    altSnp <- elementLengths(a) == 1L
    ai <- unlist(a[altSnp])    # all length 1, so unlisting is 1:1 map
    altSnp[altSnp] <- nchar(ai) == 1L & (ai %in% c("A", "C", "G", "T"))
    refSnp & altSnp
    }

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)
    !is.na(test) & test
    }
@ 
\end{small}

\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{isSnp} with the \emph{AD} filter.


\begin{small}
<<createFilterRules>>=
library(VariantAnnotation)
prefilters <- FilterRules(list(germline=isGermlinePrefilter, 
                               dbsnp=notInDbsnpPrefilter))
filters <- FilterRules(list(isSnp=isSnp, AD=allelicDepth))
@ 
\end{small}

\subsection{Create the Filtered file}

\begin{small}

<<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)

@ 
\end{small}

\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.  See here [TODO] for more information.

\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.

%% FIXME: delete next line when AnnotationHub in manifest
\SweaveOpts{eval=FALSE}

\begin{small}

<<mcf7regulatoryRegions>>=
library(AnnotationHub)
hub <- AnnotationHub()
    # paste operation is only for display purposes on a narrow page
ctcf.regs <- paste("goldenpath.hg19.encodeDCC.wgEncodeUwTfbs",
                   "wgEncodeUwTfbsMcf7CtcfStdPkRep1.narrowPeak_0.0.1.RData",
                  sep=".")
mcf7.gr <- hub[[ctcf.regs]]
@ 
\end{small}

\subsection{Find SNPs in CTCF Binding Regions}

\begin{small}

<<findOverlaps>>=
vcf <- readVcf(destination.file, "hg19")
seqlevels(vcf) <- paste("chr", seqlevels(vcf), sep="")
ov.mcf7 <- findOverlaps(vcf, mcf7.gr)
@ 
\end{small}

There is just one SNV which overlaps with the MCF-7 regulatory regions.  Find
out where, if anywhere, it fits within a gene model.

\begin{small}

<<locateVariant>>=
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene 
print(locateVariants(vcf[6,], txdb, AllVariants()))
@ 
\end{small}

\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{packed_enum}

  \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{packed_enum}


\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{small}
\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}  

\end{small}

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

\nocite{*} 

\bibliography{filterVcf}

\end{document}
