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<p>
<center>
<h3>vcfR documentation</h3>
by
<br>
Brian J. Knaus and Niklaus J. Grünwald
</center>
</p>
<div id="header">
<h1 class="title toc-ignore">Filtering data</h1>
</div>
<p>The output of variant calling pipelines result in files containing
called variants. Many of these pipelines recommend further filtering of
this data to ensure that only high quality variants are included in
downstream analyses. In this vignette I discuss strategies to focus on
the high quality fraction of these variants.</p>
<div id="data-input" class="section level2">
<h2>Data input</h2>
<p>As in other vignettes, we begin by loading the example data. Here
we’ll use this data to create a chromR object directly after inputting
the data.</p>
<pre class="r"><code>library(vcfR)
vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = "pinfsc50")
dna_file <- system.file("extdata", "pinf_sc50.fasta", package = "pinfsc50")
gff_file <- system.file("extdata", "pinf_sc50.gff", package = "pinfsc50")
vcf <- read.vcfR(vcf_file, verbose = FALSE)
dna <- ape::read.dna(dna_file, format = "fasta")
gff <- read.table(gff_file, sep="\t", quote="")
chrom <- create.chromR(name="Supercontig", vcf=vcf, seq=dna, ann=gff, verbose=FALSE)
chrom <- proc.chromR(chrom, verbose = TRUE)</code></pre>
<pre><code>## Nucleotide regions complete.</code></pre>
<pre><code>## elapsed time: 0.328</code></pre>
<pre><code>## N regions complete.</code></pre>
<pre><code>## elapsed time: 0.369</code></pre>
<pre><code>## Population summary complete.</code></pre>
<pre><code>## elapsed time: 0.315</code></pre>
<pre><code>## window_init complete.</code></pre>
<pre><code>## elapsed time: 0</code></pre>
<pre><code>## windowize_fasta complete.</code></pre>
<pre><code>## elapsed time: 0.118</code></pre>
<pre><code>## windowize_annotations complete.</code></pre>
<pre><code>## elapsed time: 0.02</code></pre>
<pre><code>## windowize_variants complete.</code></pre>
<pre><code>## elapsed time: 0.003</code></pre>
</div>
<div id="using-a-mask" class="section level2">
<h2>Using a mask</h2>
<p>Instead of removing variants, vcfR implements a mask. This allows
changes to be easily undone and it preserves the geometry of the data
matrix. Preserving the geometry of the matrix also allows for multiple
manipulations to be made easily. When we’ve settled on a set of
manipulations which we like we can then use this mask to subset the
data.</p>
<p>The function masker() censors variants (i.e., sets the mask) based on
several thresholds. A number of summaries regarding variant quality are
reported in VCF files. These summaries may differ in content depending
on what software was used to create them, as well as what options may
have been used. For example, a quality metric may range from 0 to 20 or
0 to 100 depending on choices made by the developer (i.e., are they raw
probabilities or phred converted, etc.). Because of this, the default
values for masker() are not likely to be very useful. The user will need
to explore their data to identify useful thresholds. We can use the
head() function to see what sort of information is contained in our
data.</p>
<pre class="r"><code>head(chrom)</code></pre>
<pre><code>## ***** Class chromR, method head *****
## Name: Supercontig
## Length: 1,042,442
##
## ***** Sample names (chromR) *****
## [1] "BL2009P4_us23" "DDR7602" "IN2009T1_us22" "LBUS5"
## [5] "NL07434" "P10127"
## [1] "..."
## [1] "P17777us22" "P6096" "P7722" "RS2009P1_us8" "blue13"
## [6] "t30-4"
##
## ***** VCF fixed data (chromR) *****
## CHROM POS ID REF ALT QUAL FILTER
## [1,] "Supercontig_1.50" "41" NA "AT" "A" "4784.43" NA
## [2,] "Supercontig_1.50" "136" NA "A" "C" "550.27" NA
## [3,] "Supercontig_1.50" "254" NA "T" "G" "774.44" NA
## [4,] "Supercontig_1.50" "275" NA "A" "G" "714.53" NA
## [5,] "Supercontig_1.50" "386" NA "T" "G" "876.55" NA
## [6,] "Supercontig_1.50" "462" NA "T" "G" "1301.07" NA
## [1] "..."
## CHROM POS ID REF ALT QUAL FILTER
## [22026,] "Supercontig_1.50" "1042176" NA "T" "A" "162.59" NA
## [22027,] "Supercontig_1.50" "1042196" NA "G" "A" "180.86" NA
## [22028,] "Supercontig_1.50" "1042198" NA "T" "G" "60.27" NA
## [22029,] "Supercontig_1.50" "1042303" NA "C" "G" "804.15" NA
## [22030,] "Supercontig_1.50" "1042396" NA "GA" "G" "1578.82" NA
## [22031,] "Supercontig_1.50" "1042398" NA "A" "C" "1587.87" NA
##
## INFO column has been suppressed, first INFO record:
## [1] "AC=32" "AF=1.00"
## [3] "AN=32" "DP=174"
## [5] "FS=0.000" "InbreedingCoeff=-0.0224"
## [7] "MLEAC=32" "MLEAF=1.00"
## [9] "MQ=51.30" "MQ0=0"
## [11] "QD=27.50" "SOR=4.103"
##
## ***** VCF genotype data (chromR) *****
## ***** First 6 columns *********
## FORMAT BL2009P4_us23 DDR7602
## [1,] "GT:AD:DP:GQ:PL" "1|1:0,7:7:21:283,21,0" "1|1:0,6:6:18:243,18,0"
## [2,] "GT:AD:DP:GQ:PL" "0|0:12,0:12:36:0,36,427" "0|0:20,0:20:60:0,60,819"
## [3,] "GT:AD:DP:GQ:PL" "0|0:27,0:27:81:0,81,1117" "0|0:26,0:26:78:0,78,1077"
## [4,] "GT:AD:DP:GQ:PL" "0|0:29,0:29:87:0,87,1243" "0|0:27,0:27:81:0,81,1158"
## [5,] "GT:AD:DP:GQ:PL" "0|0:26,0:26:78:0,78,1034" "0|0:30,0:30:90:0,90,1242"
## [6,] "GT:AD:DP:GQ:PL" "0|0:23,0:23:69:0,69,958" "0|0:36,0:36:99:0,108,1556"
## IN2009T1_us22 LBUS5
## [1,] "1|1:0,8:8:24:324,24,0" "1|1:0,6:6:18:243,18,0"
## [2,] "0|0:16,0:16:48:0,48,650" "0|0:20,0:20:60:0,60,819"
## [3,] "0|0:23,0:23:69:0,69,946" "0|0:26,0:26:78:0,78,1077"
## [4,] "0|0:32,0:32:96:0,96,1299" "0|0:27,0:27:81:0,81,1158"
## [5,] "0|0:41,0:41:99:0,122,1613" "0|0:30,0:30:90:0,90,1242"
## [6,] "0|0:35,0:35:99:0,105,1467" "0|0:36,0:36:99:0,108,1556"
## NL07434
## [1,] "1|1:0,12:12:36:486,36,0"
## [2,] "0|0:28,0:28:84:0,84,948"
## [3,] "0|1:19,20:39:99:565,0,559"
## [4,] "0|1:19,19:38:99:523,0,535"
## [5,] "0|1:22,22:44:99:593,0,651"
## [6,] "0|1:29,25:54:99:723,0,876"
##
## ***** Var info (chromR) *****
## ***** First 6 columns *****
## CHROM POS MQ DP mask n
## 1 Supercontig_1.50 41 51.30 174 TRUE 16
## 2 Supercontig_1.50 136 52.83 390 TRUE 17
## 3 Supercontig_1.50 254 56.79 514 TRUE 17
## 4 Supercontig_1.50 275 57.07 514 TRUE 17
## 5 Supercontig_1.50 386 57.40 509 TRUE 16
## 6 Supercontig_1.50 462 58.89 508 TRUE 17
##
## ***** VCF mask (chromR) *****
## Percent unmasked: 100
##
## ***** End head (chromR) *****</code></pre>
<p>The masker() function uses QUAL, sequence depth and mapping quality
to try to filter out low quality variants. The parameter QUAL is a part
of the VCF file definition. Because of this, it is not documented in the
meta region. This parameter should always be present. However, it may be
set to missing (‘.’ in the file, NA as read in by vcfR) or populated
with a constant, both of which render this metric as unuseful. The
parameters DP and MQ are in the INFO column an are not part of the VCF
definition. The parameter DP is not a required field, so it is defined
in the meta region.</p>
<p>We can remind ourselves how these parameters are defined by querying
the meta region.</p>
<pre class="r"><code>strwrap(grep("ID=MQ,", chrom@vcf@meta, value = T))</code></pre>
<pre><code>## [1] "##INFO=<ID=MQ,Number=1,Type=Float,Description=\"RMS Mapping Quality\">"</code></pre>
<pre class="r"><code>strwrap(grep("ID=DP,", chrom@vcf@meta, value = T))</code></pre>
<pre><code>## [1] "##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Approximate read"
## [2] "depth (reads with MQ=255 or with bad mates are filtered)\">"
## [3] "##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Approximate read"
## [4] "depth; some reads may have been filtered\">"</code></pre>
<p>We see that ‘DP’ has different definitions in the INFO column than in
the genotype region. The discrepancy appears to be due to some variants
which have a high number of raw reads but only a fraction of these have
been determined to be of high quality. Because masker() uses the DP
column in the var.info slot to judge depth, we’ll change it to include
only the high quality depth.</p>
<p>We can now use the plot function to visualize this data.</p>
<pre class="r"><code>plot(chrom)</code></pre>
<p><img src="filtering_data_files/figure-html/unnamed-chunk-4-1.png" width="672" /></p>
<p>Our summary of DP appears continuous. However, we do have variants
which appear to have unusually low coverage which we may try to filter
out. We also see that DP is very long tailed in that it has a number of
variants with exceptionally high coverage. These typically come from
repetetive regions that have reads which map to multiple locations in
the genome. Mapping quality is largely of one value (60). But there are
some lower values which we may want to remove. Quality is notable in
that it is fairly clinal and may not be useful for filtering.</p>
<p>We can also use the chromoqc function to see how the data are
distributed along the chromosome.</p>
<pre class="r"><code>chromoqc(chrom, dp.alpha = 22)</code></pre>
<p><img src="filtering_data_files/figure-html/unnamed-chunk-5-1.png" width="672" /></p>
<p>Now that we have our first peek at the data, we can try to
parameterize the masker function to isolate variants we feel to be of
high quality.</p>
<pre class="r"><code>chrom <- masker(chrom, min_QUAL=0, min_DP=350, max_DP=650, min_MQ=59.5, max_MQ=60.5)
chrom <- proc.chromR(chrom, verbose = FALSE)
plot(chrom)</code></pre>
<p><img src="filtering_data_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
<p>Note that we need to run proc.chrom() again. This will update the
variant count per window.</p>
<p>The distribution of our read depths has now become narrower and
excludes low coverage variants, it is also fairly symmetric. The
distribution of mapping quality has also become more narrow. Note that
while in this example we were able to rapidly pick relatively good
thresholds. This was the result of some trial and error. The user should
expect this process to take a few attempts.</p>
<p>We can use chromoqc() to see how applying this mask affects our
data.</p>
<pre class="r"><code>chromoqc(chrom, dp.alpha = 22)</code></pre>
<p><img src="filtering_data_files/figure-html/unnamed-chunk-7-1.png" width="672" /></p>
<p>The chromo plot shows us that the variants of low quality populated
the 3’ end of the supercontig. Filtering has removed all of the variants
from this region. Use of stringent filtering may be used to build a
conservative set of variants. This conservative set may be used for
analysis or it may be used as a training set for subsequent rounds of
variant discovery.</p>
</div>
<div id="output" class="section level2">
<h2>Output</h2>
<p>Once satisfactory filtering has been completed you’ll want to save
this high quality set of variants. This can be done by writing these
variants to a new VCF file with the function write.vcf().</p>
<pre class="r"><code>write.vcf(chrom, file="good_variants.vcf.gz", mask=TRUE)</code></pre>
</div>
<center>
<hr class="style1">
<p>Copyright © 2017, 2018 Brian J. Knaus. All rights reserved.</p>
<p>USDA Agricultural Research Service, Horticultural Crops Research Lab.</p>
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