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<meta name="author" content="Valerie Obenchain" />
<meta name="date" content="2024-10-29" />
<title>1. Introduction to VariantAnnotation</title>
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n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var 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) 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<div id="header">
<h1 class="title toc-ignore">1. Introduction to VariantAnnotation</h1>
<p class="author-name">Valerie Obenchain</p>
<h4 class="date">October 29, 2024</h4>
</div>
<h1>Contents</h1>
<div id="TOC">
<ul>
<li><a href="#introduction"><span class="toc-section-number">1</span> Introduction</a></li>
<li><a href="#variant-call-format-vcf-files"><span class="toc-section-number">2</span> Variant Call Format (VCF) files</a><ul>
<li><a href="#data-import-and-exploration"><span class="toc-section-number">2.1</span> Data import and exploration</a><ul>
<li><a href="#header-information"><span class="toc-section-number">2.1.1</span> Header information</a></li>
<li><a href="#genomic-positions"><span class="toc-section-number">2.1.2</span> Genomic positions</a></li>
<li><a href="#genotype-data"><span class="toc-section-number">2.1.3</span> Genotype data</a></li>
<li><a href="#info-data"><span class="toc-section-number">2.1.4</span> Info data</a></li>
</ul></li>
<li><a href="#import-data-subsets"><span class="toc-section-number">2.2</span> Import data subsets</a><ul>
<li><a href="#select-genomic-coordinates"><span class="toc-section-number">2.2.1</span> Select genomic coordinates</a></li>
<li><a href="#select-vcf-fields"><span class="toc-section-number">2.2.2</span> Select VCF fields</a></li>
</ul></li>
</ul></li>
<li><a href="#locating-variants-in-and-around-genes"><span class="toc-section-number">3</span> Locating variants in and around genes</a></li>
<li><a href="#amino-acid-coding-changes"><span class="toc-section-number">4</span> Amino acid coding changes</a></li>
<li><a href="#sift-and-polyphen-databases"><span class="toc-section-number">5</span> SIFT and PolyPhen Databases</a></li>
<li><a href="#other-operations"><span class="toc-section-number">6</span> Other operations</a><ul>
<li><a href="#create-a-snpmatrix"><span class="toc-section-number">6.1</span> Create a SnpMatrix</a></li>
<li><a href="#write-out-vcf-files"><span class="toc-section-number">6.2</span> Write out VCF files</a></li>
</ul></li>
<li><a href="#performance"><span class="toc-section-number">7</span> Performance</a></li>
<li><a href="#references"><span class="toc-section-number">8</span> References</a></li>
<li class="appendix"><span><b>Appendix</b></span></li>
<li><a href="#session-information"><span class="toc-section-number">A</span> Session Information</a></li>
</ul>
</div>
<div id="introduction" class="section level1">
<h1><span class="header-section-number">1</span> Introduction</h1>
<p>This vignette outlines a work flow for annotating and filtering genetic
variants using the <em><a href="https://bioconductor.org/packages/3.20/VariantAnnotation">VariantAnnotation</a></em> package. Sample data are
in VariantCall Format (VCF) and are a subset of chromosome 22 from <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20110521/">1000
Genomes</a>.
VCF text files contain meta-information lines, a header line with column
names, data lines with information about a position in the genome, and
optional genotype information on samples for each position. Samtools
organisation and repositories describes the <a href="http://samtools.github.io/hts-specs/VCFv4.4.pdf">VCF
format</a> in detail.</p>
<p>Data are read in from a VCF file and variants identified according to region
such as <code>coding</code>, <code>intron</code>, <code>intergenic</code>, <code>spliceSite</code> etc. Amino acid coding
changes are computed for the non-synonymous variants and SIFT and PolyPhen
databases provide predictions of how severly the coding changes affect protein
function.</p>
</div>
<div id="variant-call-format-vcf-files" class="section level1">
<h1><span class="header-section-number">2</span> Variant Call Format (VCF) files</h1>
<div id="data-import-and-exploration" class="section level2">
<h2><span class="header-section-number">2.1</span> Data import and exploration</h2>
<p>Data are parsed into a <code>VCF</code> object with <code>readVcf</code>.</p>
<pre class="r"><code>library(VariantAnnotation)
fl <- system.file("extdata", "chr22.vcf.gz", package="VariantAnnotation")
vcf <- readVcf(fl, "hg19")
vcf</code></pre>
<pre><code>## class: CollapsedVCF
## dim: 10376 5
## rowRanges(vcf):
## GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
## DataFrame with 22 columns: LDAF, AVGPOST, RSQ, ERATE, THETA, CIEND, CIPOS,...
## info(header(vcf)):
## Number Type Description
## LDAF 1 Float MLE Allele Frequency Accounting for LD
## AVGPOST 1 Float Average posterior probability from MaCH/Thunder
## RSQ 1 Float Genotype imputation quality from MaCH/Thunder
## ERATE 1 Float Per-marker Mutation rate from MaCH/Thunder
## THETA 1 Float Per-marker Transition rate from MaCH/Thunder
## CIEND 2 Integer Confidence interval around END for imprecise var...
## CIPOS 2 Integer Confidence interval around POS for imprecise var...
## END 1 Integer End position of the variant described in this re...
## HOMLEN . Integer Length of base pair identical micro-homology at ...
## HOMSEQ . String Sequence of base pair identical micro-homology a...
## SVLEN 1 Integer Difference in length between REF and ALT alleles
## SVTYPE 1 String Type of structural variant
## AC . Integer Alternate Allele Count
## AN 1 Integer Total Allele Count
## AA 1 String Ancestral Allele, ftp://ftp.1000genomes.ebi.ac.u...
## AF 1 Float Global Allele Frequency based on AC/AN
## AMR_AF 1 Float Allele Frequency for samples from AMR based on A...
## ASN_AF 1 Float Allele Frequency for samples from ASN based on A...
## AFR_AF 1 Float Allele Frequency for samples from AFR based on A...
## EUR_AF 1 Float Allele Frequency for samples from EUR based on A...
## VT 1 String indicates what type of variant the line represents
## SNPSOURCE . String indicates if a snp was called when analysing the...
## geno(vcf):
## List of length 3: GT, DS, GL
## geno(header(vcf)):
## Number Type Description
## GT 1 String Genotype
## DS 1 Float Genotype dosage from MaCH/Thunder
## GL G Float Genotype Likelihoods</code></pre>
<div id="header-information" class="section level3">
<h3><span class="header-section-number">2.1.1</span> Header information</h3>
<p>Header information can be extracted from the VCF with <code>header()</code>. We see there
are 5 samples, 1 piece of meta information, 22 info fields and 3 geno fields.</p>
<pre class="r"><code>header(vcf)</code></pre>
<pre><code>## class: VCFHeader
## samples(5): HG00096 HG00097 HG00099 HG00100 HG00101
## meta(1): fileformat
## fixed(2): FILTER ALT
## info(22): LDAF AVGPOST ... VT SNPSOURCE
## geno(3): GT DS GL</code></pre>
<p>Data can be further extracted using the named accessors.</p>
<pre class="r"><code>samples(header(vcf))</code></pre>
<pre><code>## [1] "HG00096" "HG00097" "HG00099" "HG00100" "HG00101"</code></pre>
<pre class="r"><code>geno(header(vcf))</code></pre>
<pre><code>## DataFrame with 3 rows and 3 columns
## Number Type Description
## <character> <character> <character>
## GT 1 String Genotype
## DS 1 Float Genotype dosage from..
## GL G Float Genotype Likelihoods</code></pre>
</div>
<div id="genomic-positions" class="section level3">
<h3><span class="header-section-number">2.1.2</span> Genomic positions</h3>
<p><code>rowRanges</code> contains information from the CHROM, POS, and ID fields of the VCF
file, represented as a <code>GRanges</code>. The <code>paramRangeID</code> column is meaningful when
reading subsets of data and is discussed further below.</p>
<pre class="r"><code>head(rowRanges(vcf), 3)</code></pre>
<pre><code>## GRanges object with 3 ranges and 5 metadata columns:
## seqnames ranges strand | paramRangeID REF
## <Rle> <IRanges> <Rle> | <factor> <DNAStringSet>
## rs7410291 22 50300078 * | NA A
## rs147922003 22 50300086 * | NA C
## rs114143073 22 50300101 * | NA G
## ALT QUAL FILTER
## <DNAStringSetList> <numeric> <character>
## rs7410291 G 100 PASS
## rs147922003 T 100 PASS
## rs114143073 A 100 PASS
## -------
## seqinfo: 1 sequence from hg19 genome; no seqlengths</code></pre>
<p>Individual fields can be pulled out with named accessors. Here we see <code>REF</code> is
stored as a <code>DNAStringSet</code> and <code>qual</code> is a numeric vector.</p>
<pre class="r"><code>ref(vcf)[1:5]</code></pre>
<pre><code>## DNAStringSet object of length 5:
## width seq
## [1] 1 A
## [2] 1 C
## [3] 1 G
## [4] 1 C
## [5] 1 C</code></pre>
<pre class="r"><code>qual(vcf)[1:5]</code></pre>
<pre><code>## [1] 100 100 100 100 100</code></pre>
<p><code>ALT</code> is a <code>DNAStringSetList</code> (allows for multiple alternate alleles per
variant) or a <code>DNAStringSet</code>. When structural variants are present it will be a
<code>CharacterList</code>.</p>
<pre class="r"><code>alt(vcf)[1:5]</code></pre>
<pre><code>## DNAStringSetList of length 5
## [[1]] G
## [[2]] T
## [[3]] A
## [[4]] T
## [[5]] T</code></pre>
</div>
<div id="genotype-data" class="section level3">
<h3><span class="header-section-number">2.1.3</span> Genotype data</h3>
<p>Genotype data described in the <code>FORMAT</code> fields are parsed into the geno slot.
The data are unique to each sample and each sample may have multiple values
variable. Because of this, the data are parsed into matrices or arrays where the
rows represent the variants and the columns the samples. Multidimentional
arrays indicate multiple values per sample. In this file all variables are
matrices.</p>
<pre class="r"><code>geno(vcf)</code></pre>
<pre><code>## List of length 3
## names(3): GT DS GL</code></pre>
<pre class="r"><code>sapply(geno(vcf), class)</code></pre>
<pre><code>## GT DS GL
## [1,] "matrix" "matrix" "matrix"
## [2,] "array" "array" "array"</code></pre>
<p>Let’s take a closer look at the genotype dosage (DS) variable. The header
provides the variable definition and type.</p>
<pre class="r"><code>geno(header(vcf))["DS",]</code></pre>
<pre><code>## DataFrame with 1 row and 3 columns
## Number Type Description
## <character> <character> <character>
## DS 1 Float Genotype dosage from..</code></pre>
<p>These data are stored as a 10376 x 5 matrix. Each of the five samples (columns)
has a single value per variant location (row).</p>
<pre class="r"><code>DS <-geno(vcf)$DS
dim(DS)</code></pre>
<pre><code>## [1] 10376 5</code></pre>
<pre class="r"><code>DS[1:3,]</code></pre>
<pre><code>## HG00096 HG00097 HG00099 HG00100 HG00101
## rs7410291 0 0 1 0 0
## rs147922003 0 0 0 0 0
## rs114143073 0 0 0 0 0</code></pre>
<p>DS is also known as ‘posterior mean genotypes’ and range in value from [0, 2].
To get a sense of variable distribution, we compute a five number summary of the
minimum, lower-hinge (first quartile), median, upper-hinge (third quartile) and
maximum.</p>
<pre class="r"><code>fivenum(DS)</code></pre>
<pre><code>## [1] 0 0 0 0 2</code></pre>
<p>The majority of these values (86%) are zero.</p>
<pre class="r"><code>length(which(DS==0))/length(DS)</code></pre>
<pre><code>## [1] 0.8621627</code></pre>
<p>View the distribution of the non-zero values.</p>
<pre class="r"><code>hist(DS[DS != 0], breaks=seq(0, 2, by=0.05),
main="DS non-zero values", xlab="DS")</code></pre>
<p><img 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" width="100%" /></p>
</div>
<div id="info-data" class="section level3">
<h3><span class="header-section-number">2.1.4</span> Info data</h3>
<p>In contrast to the genotype data, the info data are unique to the variant and
the same across samples. All info variables are represented in a single
<code>DataFrame</code>.</p>
<pre class="r"><code>info(vcf)[1:4, 1:5]</code></pre>
<pre><code>## DataFrame with 4 rows and 5 columns
## LDAF AVGPOST RSQ ERATE THETA
## <numeric> <numeric> <numeric> <numeric> <numeric>
## rs7410291 0.3431 0.9890 0.9856 2e-03 0.0005
## rs147922003 0.0091 0.9963 0.8398 5e-04 0.0011
## rs114143073 0.0098 0.9891 0.5919 7e-04 0.0008
## rs141778433 0.0062 0.9950 0.6756 9e-04 0.0003</code></pre>
<p>We will use the info data to compare quality measures between novel (i.e., not
in dbSNP) and known (i.e., in dbSNP) variants and the variant type present in
the file. Variants with membership in dbSNP can be identified by using the
appropriate SNPlocs package for the hg19 genome (GRCh37).</p>
<pre class="r"><code>library(SNPlocs.Hsapiens.dbSNP144.GRCh37)
vcf_rsids <- names(rowRanges(vcf))
chr22snps <- snpsBySeqname(SNPlocs.Hsapiens.dbSNP144.GRCh37, "22")
chr22_rsids <- mcols(chr22snps)$RefSNP_id
in_dbSNP <- vcf_rsids %in% chr22_rsids
table(in_dbSNP) </code></pre>
<pre><code>## in_dbSNP
## FALSE TRUE
## 1114 9262</code></pre>
<p>Info variables of interest are ‘VT’, ‘LDAF’ and ‘RSQ’. The header offers
more details on these variables.</p>
<pre class="r"><code>info(header(vcf))[c("VT", "LDAF", "RSQ"),]</code></pre>
<pre><code>## DataFrame with 3 rows and 3 columns
## Number Type Description
## <character> <character> <character>
## VT 1 String indicates what type ..
## LDAF 1 Float MLE Allele Frequency..
## RSQ 1 Float Genotype imputation ..</code></pre>
<p>Create a data frame of quality measures of interest …</p>
<pre class="r"><code>metrics <- data.frame(QUAL=qual(vcf), in_dbSNP=in_dbSNP,
VT=info(vcf)$VT, LDAF=info(vcf)$LDAF, RSQ=info(vcf)$RSQ)</code></pre>
<p>and visualize the distribution of qualities using <em><a href="https://CRAN.R-project.org/package=ggplot2">ggplot2</a></em>. For
instance, genotype imputation quality is higher for the known variants in dbSNP.</p>
<pre class="r"><code>library(ggplot2)
ggplot(metrics, aes(x=RSQ, fill=in_dbSNP)) +
geom_density(alpha=0.5) +
scale_x_continuous(name="MaCH / Thunder Imputation Quality") +
scale_y_continuous(name="Density") +
theme(legend.position="top")</code></pre>
<p><img 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" width="100%" /></p>
</div>
</div>
<div id="import-data-subsets" class="section level2">
<h2><span class="header-section-number">2.2</span> Import data subsets</h2>
<p>When working with large VCF files it may be more efficient to read in subsets of
the data. This can be accomplished by selecting genomic coordinates (ranges) or
by specific fields from the VCF file.</p>
<div id="select-genomic-coordinates" class="section level3">
<h3><span class="header-section-number">2.2.1</span> Select genomic coordinates</h3>
<p>To read in a portion of chromosome 22, create a <code>GRanges</code> with the regions of
interest.</p>
<pre class="r"><code>rng <- GRanges(seqnames="22", ranges=IRanges(
start=c(50301422, 50989541),
end=c(50312106, 51001328),
names=c("gene_79087", "gene_644186")))</code></pre>
<p>When ranges are specified, the VCF file must have an accompanying Tabix index
file. See <code>indexTabix</code> for help creating an index.</p>
<pre class="r"><code>tab <- TabixFile(fl)
vcf_rng <- readVcf(tab, "hg19", param=rng)</code></pre>
<p>The <code>paramRangesID</code> column distinguishes which records came from which param
range.</p>
<pre class="r"><code>head(rowRanges(vcf_rng), 3)</code></pre>
<pre><code>## GRanges object with 3 ranges and 5 metadata columns:
## seqnames ranges strand | paramRangeID REF
## <Rle> <IRanges> <Rle> | <factor> <DNAStringSet>
## rs114335781 22 50301422 * | gene_79087 G
## rs8135963 22 50301476 * | gene_79087 T
## 22:50301488_C/T 22 50301488 * | gene_79087 C
## ALT QUAL FILTER
## <DNAStringSetList> <numeric> <character>
## rs114335781 A 100 PASS
## rs8135963 C 100 PASS
## 22:50301488_C/T T 100 PASS
## -------
## seqinfo: 1 sequence from hg19 genome; no seqlengths</code></pre>
</div>
<div id="select-vcf-fields" class="section level3">
<h3><span class="header-section-number">2.2.2</span> Select VCF fields</h3>
<p>Data import can also be defined by the <code>fixed</code>, <code>info</code> and <code>geno</code> fields. Fields
available for import are described in the header information. To view the header
before reading in the data, use <code>ScanVcfHeader</code>.</p>
<pre class="r"><code>hdr <- scanVcfHeader(fl)
## e.g., INFO and GENO fields
head(info(hdr), 3)</code></pre>
<pre><code>## DataFrame with 3 rows and 3 columns
## Number Type Description
## <character> <character> <character>
## LDAF 1 Float MLE Allele Frequency..
## AVGPOST 1 Float Average posterior pr..
## RSQ 1 Float Genotype imputation ..</code></pre>
<pre class="r"><code>head(geno(hdr), 3)</code></pre>
<pre><code>## DataFrame with 3 rows and 3 columns
## Number Type Description
## <character> <character> <character>
## GT 1 String Genotype
## DS 1 Float Genotype dosage from..
## GL G Float Genotype Likelihoods</code></pre>
<p>To subset on “LDAF” and “GT” we specify them as <code>character</code> vectors in the
<code>info</code> and <code>geno</code> arguments to <code>ScanVcfParam</code>. This creates a <code>ScanVcfParam</code>
object which is used as the <code>param</code> argument to <code>readVcf</code>.</p>
<pre class="r"><code>## Return all 'fixed' fields, "LAF" from 'info' and "GT" from 'geno'
svp <- ScanVcfParam(info="LDAF", geno="GT")
vcf1 <- readVcf(fl, "hg19", svp)
names(geno(vcf1))</code></pre>
<pre><code>## [1] "GT"</code></pre>
<p>To subset on both genomic coordinates and fields the <code>ScanVcfParam</code> object must
contain both.</p>
<pre class="r"><code>svp_all <- ScanVcfParam(info="LDAF", geno="GT", which=rng)
svp_all</code></pre>
<pre><code>## class: ScanVcfParam
## vcfWhich: 1 elements
## vcfFixed: character() [All]
## vcfInfo: LDAF
## vcfGeno: GT
## vcfSamples:</code></pre>
</div>
</div>
</div>
<div id="locating-variants-in-and-around-genes" class="section level1">
<h1><span class="header-section-number">3</span> Locating variants in and around genes</h1>
<p>Variant location with respect to genes can be identified with the
<code>locateVariants</code> function. Regions are specified in the <code>region</code> argument and
can be one of the following constructors: CodingVariants, IntronVariants,
FiveUTRVariants, ThreeUTRVariants, IntergenicVariants, SpliceSiteVariants or
PromoterVariants. Location definitions are shown in Table <a href="#tab:table">1</a>.</p>
<div class='horizontal-scroll'><table>
<caption><span id="tab:table">Table 1: </span><span class="caption-title">Variant locations</caption></span><br>
<thead>
<tr class="header">
<th>Location</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>coding</td>
<td>falls <em>within</em> a coding region</td>
</tr>
<tr class="even">
<td>fiveUTR</td>
<td>falls <em>within</em> a 5’ untranslated region</td>
</tr>
<tr class="odd">
<td>threeUTR</td>
<td>falls <em>within</em> a 3’ untranslated region</td>
</tr>
<tr class="even">
<td>intron</td>
<td>falls <em>within</em> an intron region</td>
</tr>
<tr class="odd">
<td>intergenic</td>
<td>does not fall <em>within</em> a transcript associated with a gene</td>
</tr>
<tr class="even">
<td>spliceSite</td>
<td>overlaps any portion of the first 2 or last 2</td>
</tr>
<tr class="odd">
<td>promoter</td>
<td>falls <em>within</em> a promoter region of a transcript</td>
</tr>
</tbody>
</table></div>
<p>For overlap methods to work properly the chromosome names (seqlevels) must be
compatible in the objects being compared. The VCF data chromosome names are
represented by number, i.e., ‘22’, but the TxDb chromosome names are preceded
with ‘chr’. Seqlevels in the VCF can be modified with the <code>seqlevels</code> function.</p>
<pre class="r"><code>library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
seqlevels(vcf) <- "chr22"
rd <- rowRanges(vcf)
loc <- locateVariants(rd, txdb, CodingVariants())
head(loc, 3)</code></pre>
<pre><code>## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART LOCEND
## <Rle> <IRanges> <Rle> | <factor> <integer> <integer>
## rs114335781 chr22 50301422 - | coding 939 939
## rs8135963 chr22 50301476 - | coding 885 885
## 22:50301488_C/T chr22 50301488 - | coding 873 873
## QUERYID TXID CDSID GENEID
## <integer> <character> <IntegerList> <character>
## rs114335781 24 75253 218562 79087
## rs8135963 25 75253 218562 79087
## 22:50301488_C/T 26 75253 218562 79087
## PRECEDEID FOLLOWID
## <CharacterList> <CharacterList>
## rs114335781
## rs8135963
## 22:50301488_C/T
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths</code></pre>
<p>Locate variants in all regions with the <code>AllVariants()</code> constructor,</p>
<pre class="r"><code>allvar <- locateVariants(rd, txdb, AllVariants())</code></pre>
<p>To answer gene-centric questions data can be summarized by gene reguardless
of transcript.</p>
<pre class="r"><code>## Did any coding variants match more than one gene?
splt <- split(mcols(loc)$GENEID, mcols(loc)$QUERYID)
table(sapply(splt, function(x) length(unique(x)) > 1))</code></pre>
<pre><code>##
## FALSE TRUE
## 965 15</code></pre>
<pre class="r"><code>## Summarize the number of coding variants by gene ID.
splt <- split(mcols(loc)$QUERYID, mcols(loc)$GENEID)
head(sapply(splt, function(x) length(unique(x))), 3)</code></pre>
<pre><code>## 113730 1890 23209
## 22 15 30</code></pre>
</div>
<div id="amino-acid-coding-changes" class="section level1">
<h1><span class="header-section-number">4</span> Amino acid coding changes</h1>
<p><code>predictCoding</code> computes amino acid coding changes for non-synonymous variants.
Only ranges in query that overlap with a coding region in the <code>subject</code> are
considered. Reference sequences are retrieved from either a <code>BSgenome</code> or fasta
file specified in <code>seqSource</code>. Variant sequences are constructed by
substituting, inserting or deleting values in the <code>varAllele</code> column into the
reference sequence. Amino acid codes are computed for the variant codon sequence
when the length is a multiple of 3.</p>
<p>The query argument to <code>predictCoding</code> can be a <code>GRanges</code> or <code>VCF</code>. When a
<code>GRanges</code> is supplied the <code>varAllele</code> argument must be specified. In the case of
a <code>VCF</code>, the alternate alleles are taken from <code>alt(<VCF>)</code> and the <code>varAllele</code>
argument is not specified.</p>
<p>The result is a modified <code>query</code> containing only variants that fall within
coding regions. Each row represents a variant-transcript match so more than one
row per original variant is possible.</p>
<pre class="r"><code>library(BSgenome.Hsapiens.UCSC.hg19)
coding <- predictCoding(vcf, txdb, seqSource=Hsapiens)
coding[5:7]</code></pre>
<pre><code>## GRanges object with 3 ranges and 17 metadata columns:
## seqnames ranges strand | paramRangeID REF
## <Rle> <IRanges> <Rle> | <factor> <DNAStringSet>
## 22:50301584_C/T chr22 50301584 - | NA C
## rs114264124 chr22 50302962 - | NA C
## rs149209714 chr22 50302995 - | NA C
## ALT QUAL FILTER varAllele
## <DNAStringSetList> <numeric> <character> <DNAStringSet>
## 22:50301584_C/T T 100 PASS A
## rs114264124 T 100 PASS A
## rs149209714 G 100 PASS C
## CDSLOC PROTEINLOC QUERYID TXID CDSID
## <IRanges> <IntegerList> <integer> <character> <IntegerList>
## 22:50301584_C/T 777 259 28 75253 218562
## rs114264124 698 233 57 75253 218563
## rs149209714 665 222 58 75253 218563
## GENEID CONSEQUENCE REFCODON VARCODON
## <character> <factor> <DNAStringSet> <DNAStringSet>
## 22:50301584_C/T 79087 synonymous CCG CCA
## rs114264124 79087 nonsynonymous CGG CAG
## rs149209714 79087 nonsynonymous GGA GCA
## REFAA VARAA
## <AAStringSet> <AAStringSet>
## 22:50301584_C/T P P
## rs114264124 R Q
## rs149209714 G A
## -------
## seqinfo: 1 sequence from hg19 genome; no seqlengths</code></pre>
<p>Using variant rs114264124 as an example, we see varAllele <code>A</code> has been
substituted into the <code>refCodon</code> <code>CGG</code> to produce <code>varCodon</code> <code>CAG.</code> The
<code>refCodon</code> is the sequence of codons necessary to make the variant allele
substitution and therefore often includes more nucleotides than indicated in the
range (i.e. the range is 50302962, 50302962, width of 1). Notice it is the
second position in the <code>refCodon</code> that has been substituted. This position in
the codon, the position of substitution, corresponds to genomic position
50302962. This genomic position maps to position 698 in coding region-based
coordinates and to triplet 233 in the protein. This is a non-synonymous coding
variant where the amino acid has changed from <code>R</code> (Arg) to <code>Q</code> (Gln).</p>
<p>When the resulting <code>varCodon</code> is not a multiple of 3 it cannot be translated.
The consequence is considered a <code>frameshift</code> and <code>varAA</code> will be missing.</p>
<pre class="r"><code>## CONSEQUENCE is 'frameshift' where translation is not possible
coding[mcols(coding)$CONSEQUENCE == "frameshift"]</code></pre>
<pre><code>## GRanges object with 2 ranges and 17 metadata columns:
## seqnames ranges strand | paramRangeID REF
## <Rle> <IRanges> <Rle> | <factor> <DNAStringSet>
## 22:50317001_G/GCACT chr22 50317001 + | NA G
## 22:50317001_G/GCACT chr22 50317001 + | NA G
## ALT QUAL FILTER varAllele
## <DNAStringSetList> <numeric> <character> <DNAStringSet>
## 22:50317001_G/GCACT GCACT 233 PASS GCACT
## 22:50317001_G/GCACT GCACT 233 PASS GCACT
## CDSLOC PROTEINLOC QUERYID TXID
## <IRanges> <IntegerList> <integer> <character>
## 22:50317001_G/GCACT 808 270 359 74357
## 22:50317001_G/GCACT 628 210 359 74358
## CDSID GENEID CONSEQUENCE REFCODON
## <IntegerList> <character> <factor> <DNAStringSet>
## 22:50317001_G/GCACT 216303 79174 frameshift GCC
## 22:50317001_G/GCACT 216303 79174 frameshift GCC
## VARCODON REFAA VARAA
## <DNAStringSet> <AAStringSet> <AAStringSet>
## 22:50317001_G/GCACT GCACTCC
## 22:50317001_G/GCACT GCACTCC
## -------
## seqinfo: 1 sequence from hg19 genome; no seqlengths</code></pre>
</div>
<div id="sift-and-polyphen-databases" class="section level1">
<h1><span class="header-section-number">5</span> SIFT and PolyPhen Databases</h1>
<p>From <code>predictCoding</code> we identified the amino acid coding changes for the
non-synonymous variants. For this subset we can retrieve predictions of how
damaging these coding changes may be. SIFT (Sorting Intolerant From Tolerant)
and PolyPhen (Polymorphism Phenotyping) are methods that predict the impact of
amino acid substitution on a human protein. The SIFT method uses sequence
homology and the physical properties of amino acids to make predictions about
protein function. PolyPhen uses sequence-based features and structural
information characterizing the substitution to make predictions about the
structure and function of the protein.</p>
<p>Collated predictions for specific dbSNP builds are available as downloads from
the SIFT and PolyPhen web sites. These results have been packaged into
<em><a href="https://bioconductor.org/packages/3.20/SIFT.Hsapiens.dbSNP132">SIFT.Hsapiens.dbSNP132</a></em> and
<em><a href="https://bioconductor.org/packages/3.20/PolyPhen.Hsapiens.dbSNP131">PolyPhen.Hsapiens.dbSNP131</a></em> and are designed to be searched by
rsid. Variants that are in dbSNP can be searched with these database packages.
When working with novel variants, SIFT and PolyPhen must be called directly.
See references for home pages.</p>
<p>Identify the non-synonymous variants and obtain the rsids.</p>
<pre class="r"><code>nms <- names(coding)
idx <- mcols(coding)$CONSEQUENCE == "nonsynonymous"
nonsyn <- coding[idx]
names(nonsyn) <- nms[idx]
rsids <- unique(names(nonsyn)[grep("rs", names(nonsyn), fixed=TRUE)])</code></pre>
<p>Detailed descriptions of the database columns can be found with <code>?SIFTDbColumns</code>
and <code>?PolyPhenDbColumns</code>. Variants in these databases often contain more than
one row per variant. The variant may have been reported by multiple sources and
therefore the source will differ as well as some of the other variables.</p>
<p>It is important to keep in mind the pre-computed predictions in the SIFT and
PolyPhen packages are based on specific gene models. SIFT is based on Ensembl
and PolyPhen on UCSC Known Gene. The <code>TxDb</code> we used to identify the coding snps
was based on UCSC Known Gene so we will use PolyPhen for predictions. PolyPhen
provides predictions using two different training datasets and has considerable
information about 3D protein structure. See <code>?PolyPhenDbColumns</code> or the PolyPhen
web site listed in the references for more details.</p>
<p>Query the PolyPhen database,</p>
<pre class="r"><code>library(PolyPhen.Hsapiens.dbSNP131)
pp <- select(PolyPhen.Hsapiens.dbSNP131, keys=rsids,
cols=c("TRAININGSET", "PREDICTION", "PPH2PROB"))
head(pp[!is.na(pp$PREDICTION), ]) </code></pre>
<pre><code>## RSID TRAININGSET OSNPID OACC OPOS OAA1 OAA2 SNPID
## 13 rs8139422 humdiv rs8139422 Q6UXH1-5 182 D E rs8139422
## 14 rs8139422 humvar rs8139422 <NA> <NA> <NA> <NA> rs8139422
## 15 rs74510325 humdiv rs74510325 Q6UXH1-5 189 R G rs74510325
## 16 rs74510325 humvar rs74510325 <NA> <NA> <NA> <NA> rs74510325
## 21 rs73891177 humdiv rs73891177 Q6UXH1-5 207 P A rs73891177
## 22 rs73891177 humvar rs73891177 <NA> <NA> <NA> <NA> rs73891177
## ACC POS AA1 AA2 NT1 NT2 PREDICTION BASEDON EFFECT PPH2CLASS
## 13 Q6UXH1-5 182 D E T A possibly damaging alignment <NA> neutral
## 14 Q6UXH1-5 182 D E <NA> <NA> possibly damaging <NA> <NA> <NA>
## 15 Q6UXH1-5 189 R G C G possibly damaging alignment <NA> neutral
## 16 Q6UXH1-5 189 R G <NA> <NA> possibly damaging <NA> <NA> <NA>
## 21 Q6UXH1-5 207 P A C G benign alignment <NA> neutral
## 22 Q6UXH1-5 207 P A <NA> <NA> benign <NA> <NA> <NA>
## PPH2PROB PPH2FPR PPH2TPR PPH2FDR SITE REGION PHAT DSCORE SCORE1 SCORE2 NOBS
## 13 0.228 0.156 0.892 0.258 <NA> <NA> <NA> 0.951 1.382 0.431 37
## 14 0.249 0.341 0.874 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 15 0.475 0.131 0.858 0.233 <NA> <NA> <NA> 1.198 1.338 0.14 36
## 16 0.335 0.311 0.851 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 21 0.001 0.86 0.994 0.61 <NA> <NA> <NA> -0.225 -0.45 -0.225 1
## 22 0.005 0.701 0.981 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## NSTRUCT NFILT PDBID PDBPOS PDBCH IDENT LENGTH NORMACC SECSTR MAPREG DVOL
## 13 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 15 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 16 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 21 0 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 22 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## DPROP BFACT HBONDS AVENHET MINDHET AVENINT MINDINT AVENSIT MINDSIT TRANSV
## 13 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 1
## 14 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 15 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 1
## 16 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 21 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> 1
## 22 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## CODPOS CPG MINDJNC PFAMHIT IDPMAX IDPSNP IDQMIN COMMENTS
## 13 2 0 <NA> <NA> 18.261 18.261 48.507 chr22:50315363_CA
## 14 <NA> <NA> <NA> <NA> <NA> <NA> <NA> chr22:50315363_CA
## 15 0 1 <NA> <NA> 19.252 19.252 63.682 chr22:50315382_CG
## 16 <NA> <NA> <NA> <NA> <NA> <NA> <NA> chr22:50315382_CG
## 21 0 0 <NA> <NA> 1.919 <NA> 60.697 chr22:50315971_CG
## 22 <NA> <NA> <NA> <NA> <NA> <NA> <NA> chr22:50315971_CG</code></pre>
</div>
<div id="other-operations" class="section level1">
<h1><span class="header-section-number">6</span> Other operations</h1>
<div id="create-a-snpmatrix" class="section level2">
<h2><span class="header-section-number">6.1</span> Create a SnpMatrix</h2>
<p>The ‘GT’ element in the <code>FORMAT</code> field of the VCF represents the genotype. These
data can be converted into a <code>SnpMatrix</code> object which can then be used with the
functions offered in <em><a href="https://bioconductor.org/packages/3.20/snpStats">snpStats</a></em> and other packages making use of the <code>SnpMatrix</code> class.</p>
<p>The <code>genotypeToSnpMatrix</code> function converts the genotype calls in <code>geno</code> to a
<code>SnpMatrix</code>. No dbSNP package is used in this computation. The return value is a
named list where ‘genotypes’ is a <code>SnpMatrix</code> and ‘map’ is a <code>DataFrame</code> with
SNP names and alleles at each loci. The <code>ignore</code> column in ‘map’ indicates
which variants were set to NA (missing) because they met one or more of the
following criteria,</p>
<ul>
<li>variants with >1 ALT allele are set to NA</li>
<li>only single nucleotide variants are included; others are set to NA</li>
<li>only diploid calls are included; others are set to NA</li>
</ul>
<p>See ?<code>genotypeToSnpMatrix</code> for more details.</p>
<pre class="r"><code>res <- genotypeToSnpMatrix(vcf)
res</code></pre>
<pre><code>## $genotypes
## A SnpMatrix with 5 rows and 10376 columns
## Row names: HG00096 ... HG00101
## Col names: rs7410291 ... rs114526001
##
## $map
## DataFrame with 10376 rows and 4 columns
## snp.names allele.1 allele.2 ignore
## <character> <DNAStringSet> <DNAStringSetList> <logical>
## 1 rs7410291 A G FALSE
## 2 rs147922003 C T FALSE
## 3 rs114143073 G A FALSE
## 4 rs141778433 C T FALSE
## 5 rs182170314 C T FALSE
## ... ... ... ... ...
## 10372 rs187302552 A G FALSE
## 10373 rs9628178 A G FALSE
## 10374 rs5770892 A G FALSE
## 10375 rs144055359 G A FALSE
## 10376 rs114526001 G C FALSE</code></pre>
<p>In the map DataFrame, allele.1 represents the reference allele and allele.2 is
the alternate allele.</p>
<pre class="r"><code>allele2 <- res$map[["allele.2"]]
## number of alternate alleles per variant
unique(elementNROWS(allele2))</code></pre>
<pre><code>## [1] 1</code></pre>
<p>In addition to the called genotypes, genotype likelihoods or probabilities can
also be converted to a <code>SnpMatrix</code>, using the <em><a href="https://bioconductor.org/packages/3.20/snpStats">snpStats</a></em> encoding
of posterior probabilities as byte values. To use the values in the ‘GL’ or
‘GP’ <code>FORMAT</code> field instead of the called genotypes, use the <code>uncertain=TRUE</code>
option in <code>genotypeToSnpMatrix</code>.</p>
<pre class="r"><code>fl.gl <- system.file("extdata", "gl_chr1.vcf", package="VariantAnnotation")
vcf.gl <- readVcf(fl.gl, "hg19")
geno(vcf.gl)</code></pre>
<pre><code>## List of length 3
## names(3): GT DS GL</code></pre>
<pre class="r"><code>## Convert the "GL" FORMAT field to a SnpMatrix
res <- genotypeToSnpMatrix(vcf.gl, uncertain=TRUE)
res</code></pre>
<pre><code>## $genotypes
## A SnpMatrix with 85 rows and 9 columns
## Row names: NA06984 ... NA12890
## Col names: rs58108140 ... rs200430748
##
## $map
## DataFrame with 9 rows and 4 columns
## snp.names allele.1 allele.2 ignore
## <character> <DNAStringSet> <DNAStringSetList> <logical>
## 1 rs58108140 G A FALSE
## 2 rs189107123 C TRUE
## 3 rs180734498 C T FALSE
## 4 rs144762171 G TRUE
## 5 rs201747181 TC TRUE
## 6 rs151276478 T TRUE
## 7 rs140337953 G T FALSE
## 8 rs199681827 C TRUE
## 9 rs200430748 G TRUE</code></pre>
<pre class="r"><code>t(as(res$genotype, "character"))[c(1,3,7), 1:5]</code></pre>
<pre><code>## NA06984 NA06986 NA06989 NA06994 NA07000
## rs58108140 "Uncertain" "Uncertain" "A/B" "Uncertain" "Uncertain"
## rs180734498 "Uncertain" "Uncertain" "Uncertain" "Uncertain" "Uncertain"
## rs140337953 "Uncertain" "Uncertain" "Uncertain" "Uncertain" "Uncertain"</code></pre>
<pre class="r"><code>## Compare to a SnpMatrix created from the "GT" field
res.gt <- genotypeToSnpMatrix(vcf.gl, uncertain=FALSE)
t(as(res.gt$genotype, "character"))[c(1,3,7), 1:5]</code></pre>
<pre><code>## NA06984 NA06986 NA06989 NA06994 NA07000
## rs58108140 "A/B" "A/B" "A/B" "A/A" "A/A"
## rs180734498 "A/B" "A/A" "A/A" "A/A" "A/B"
## rs140337953 "B/B" "B/B" "A/B" "B/B" "A/B"</code></pre>
<pre class="r"><code>## What are the original likelihoods for rs58108140?
geno(vcf.gl)$GL["rs58108140", 1:5]</code></pre>
<pre><code>## $NA06984
## [1] -4.70 -0.58 -0.13
##
## $NA06986
## [1] -1.15 -0.10 -0.84
##
## $NA06989
## [1] -2.05 0.00 -3.27
##
## $NA06994
## [1] -0.48 -0.48 -0.48
##
## $NA07000
## [1] -0.28 -0.44 -0.96</code></pre>
<p>For variant rs58108140 in sample NA06989, the "A/B" genotype is much more
likely than the others, so the <code>SnpMatrix</code> object displays the called genotype.</p>
</div>
<div id="write-out-vcf-files" class="section level2">
<h2><span class="header-section-number">6.2</span> Write out VCF files</h2>
<p>A VCF file can be written out from data stored in a <code>VCF</code> class.</p>
<pre class="r"><code>fl <- system.file("extdata", "ex2.vcf", package="VariantAnnotation")
out1.vcf <- tempfile()
out2.vcf <- tempfile()
in1 <- readVcf(fl, "hg19")
writeVcf(in1, out1.vcf)
in2 <- readVcf(out1.vcf, "hg19")
writeVcf(in2, out2.vcf)
in3 <- readVcf(out2.vcf, "hg19")
identical(rowRanges(in1), rowRanges(in3))</code></pre>
<pre><code>## [1] TRUE</code></pre>
<pre class="r"><code>identical(geno(in1), geno(in2))</code></pre>
<pre><code>## [1] TRUE</code></pre>
</div>
</div>
<div id="performance" class="section level1">
<h1><span class="header-section-number">7</span> Performance</h1>
<p>Targeted queries can greatly improve the speed of data input. When all data from
the file are needed define a <code>yieldSize</code> in the <code>TabixFile</code> to iterate through
the file in chunks.</p>
<pre class="r"><code>readVcf(TabixFile(fl, yieldSize=10000))</code></pre>
<p><code>readVcf</code> can be used with a to select any combination of INFO and GENO fields,
samples or genomic positions.</p>
<pre class="r"><code>readVcf(TabixFile(fl), param=ScanVcfParam(info='DP', geno='GT'))</code></pre>
<p>While <code>readvcf</code> offers the flexibility to define combinations of INFO, GENO and
samples in the <code>ScanVcfParam</code>, sometimes only a single field is needed. In this
case the lightweight <code>read</code> functions (<code>readGT</code>, <code>readInfo</code> and <code>readGeno</code>) can
be used. These functions return the single field as a matrix instead of a <code>VCF</code>
object.</p>
<pre class="r"><code>readGT(fl)</code></pre>
<p>The table below highlights the speed differences of targeted queries vs reading
in all data. The test file is from 1000 Genomes and has 494328 variants, 1092
samples, 22 INFO, and 3 GENO fields and is located at
<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20101123/" class="uri">http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20101123/</a>. <code>yieldSize</code> is
used to define chunks of 100, 1000, 10000 and 100000 variants. For each chunk
size three function calls are compared: <code>readGT</code> reading only GT, <code>readVcf</code>
reading both <code>GT</code> and <code>ALT</code> and finally <code>readVcf</code> reading in all the data.</p>
<pre class="r"><code>library(microbenchmark)
fl <- "ALL.chr22.phase1_release_v3.20101123.snps_indels_svs.genotypes.vcf.gz"
ys <- c(100, 1000, 10000, 100000)
## readGT() input only 'GT':
fun <- function(fl, yieldSize) readGT(TabixFile(fl, yieldSize))
lapply(ys, function(i) microbenchmark(fun(fl, i), times=5)
## readVcf() input only 'GT' and 'ALT':
fun <- function(fl, yieldSize, param)
readVcf(TabixFile(fl, yieldSize), "hg19", param=param)
param <- ScanVcfParam(info=NA, geno="GT", fixed="ALT")
lapply(ys, function(i) microbenchmark(fun(fl, i, param), times=5)
## readVcf() input all variables:
fun <- function(fl, yieldSize) readVcf(TabixFile(fl, yieldSize), "hg19")
lapply(ys, function(i) microbenchmark(fun(fl, i), times=5))</code></pre>
<div class='horizontal-scroll'><table>
<caption><span id="tab:performance">Table 2: </span><span class="caption-title">Targeted queries (time in seconds)</caption></span><br>
<thead>
<tr class="header">
<th align="left">n records</th>
<th align="left">readGT</th>
<th align="left">readVcf (GT and ALT)</th>
<th align="left">readVcf (all)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">100</td>
<td align="left">0.082</td>
<td align="left">0.128</td>
<td align="left">0.501</td>
</tr>
<tr class="even">
<td align="left">1000</td>
<td align="left">0.609</td>
<td align="left">0.508</td>
<td align="left">5.878</td>
</tr>
<tr class="odd">
<td align="left">10000</td>
<td align="left">5.972</td>
<td align="left">6.164</td>
<td align="left">68.378</td>
</tr>
<tr class="even">
<td align="left">100000</td>
<td align="left">78.593</td>
<td align="left">81.156</td>
<td align="left">693.654</td>
</tr>
</tbody>
</table></div>
</div>
<div id="references" class="section level1">
<h1><span class="header-section-number">8</span> References</h1>
<div id="refs">
</div>
</div>
<div id="appendix-appendix" class="section level1 unnumbered">
<h1>Appendix</h1>
<p>Wang K, Li M, Hakonarson H, (2010), ANNOVAR: functional annotation of
genetic variants from high-throughput sequencing data. Nucleic Acids
Research, Vol 38, No. 16, e164.</p>
<p>McLaren W, Pritchard B, RiosD, et. al., (2010), Deriving the
consequences of genomic variants with the Ensembl API and SNP Effect
Predictor. Bioinformatics, Vol. 26, No. 16, 2069-2070.</p>
<p>SIFT home page: <a href="http://sift.bii.a-star.edu.sg/" class="uri">http://sift.bii.a-star.edu.sg/</a></p>
<p>PolyPhen home page: <a href="http://genetics.bwh.harvard.edu/pph2/" class="uri">http://genetics.bwh.harvard.edu/pph2/</a></p>
</div>
<div id="session-information" class="section level1">
<h1><span class="header-section-number">A</span> Session Information</h1>
<pre><code>## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] snpStats_1.56.0
## [2] Matrix_1.7-1
## [3] survival_3.7-0
## [4] PolyPhen.Hsapiens.dbSNP131_1.0.2
## [5] RSQLite_2.3.7
## [6] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [7] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [8] GenomicFeatures_1.58.0
## [9] AnnotationDbi_1.68.0
## [10] ggplot2_3.5.1
## [11] SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20
## [12] BSgenome_1.74.0
## [13] rtracklayer_1.66.0
## [14] BiocIO_1.16.0
## [15] VariantAnnotation_1.52.0
## [16] Rsamtools_2.22.0
## [17] Biostrings_2.74.0
## [18] XVector_0.46.0
## [19] SummarizedExperiment_1.36.0
## [20] Biobase_2.66.0
## [21] GenomicRanges_1.58.0
## [22] GenomeInfoDb_1.42.0
## [23] IRanges_2.40.0
## [24] S4Vectors_0.44.0
## [25] MatrixGenerics_1.18.0
## [26] matrixStats_1.4.1
## [27] BiocGenerics_0.52.0
## [28] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] blob_1.2.4 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.16 GenomicAlignments_1.42.0 XML_3.99-0.17
## [10] digest_0.6.37 lifecycle_1.0.4 KEGGREST_1.46.0
## [13] magrittr_2.0.3 compiler_4.4.1 rlang_1.1.4
## [16] sass_0.4.9 tools_4.4.1 utf8_1.2.4
## [19] yaml_2.3.10 knitr_1.48 labeling_0.4.3
## [22] S4Arrays_1.6.0 bit_4.5.0 curl_5.2.3
## [25] DelayedArray_0.32.0 abind_1.4-8 BiocParallel_1.40.0
## [28] withr_3.0.2 grid_4.4.1 fansi_1.0.6
## [31] colorspace_2.1-1 scales_1.3.0 tinytex_0.53
## [34] cli_3.6.3 rmarkdown_2.28 crayon_1.5.3
## [37] generics_0.1.3 httr_1.4.7 rjson_0.2.23
## [40] DBI_1.2.3 cachem_1.1.0 splines_4.4.1
## [43] zlibbioc_1.52.0 parallel_4.4.1 BiocManager_1.30.25
## [46] restfulr_0.0.15 vctrs_0.6.5 jsonlite_1.8.9
## [49] bookdown_0.41 bit64_4.5.2 magick_2.8.5
## [52] jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20
## [55] gtable_0.3.6 UCSC.utils_1.2.0 munsell_0.5.1
## [58] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
## [61] GenomeInfoDbData_1.2.13 R6_2.5.1 evaluate_1.0.1
## [64] lattice_0.22-6 highr_0.11 png_0.1-8
## [67] memoise_2.0.1 bslib_0.8.0 Rcpp_1.0.13
## [70] SparseArray_1.6.0 xfun_0.48 pkgconfig_2.0.3</code></pre>
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