Exome Capture Metrics GVA2020
- Deatherage, Daniel E
Data used in this tutorial
Recommended but not required that you first complete the trios tutorial and use the data you generated here. Alternatively, canned data provided.
Evaluating capture metrics
There are many ways to measure sequence capture. You might care more about minimizing off-target capture, to make your sequencing dollars go as far as possible. Or you might care more about maximizing on-target capture, to make sure you get data from every region of interest. These two are usually negatively correlated.
Using Picard's "CollectHsMetrics" function to evaluate capture
Here is a link to the full picard documentation and here is a link to the CollectHsMetrics tool
To run CollectHsMetrics on Lonestar, there are three prerequisites: 1) A bam file and 2) a list of the genomic intervals that were to be captured and 3) the reference (.fa). As you would guess, the BAM and interval list both have to be based on exactly the same genomic reference file.
For our tutorial, the bam files are one of these:
/corral-repl/utexas/BioITeam/ngs_course/human_variation/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam /corral-repl/utexas/BioITeam/ngs_course/human_variation/NA12892.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam /corral-repl/utexas/BioITeam/ngs_course/human_variation/NA12891.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam
I've started with one of Illumina's target capture definitions (the vendor of your capture kit will provide this) but since the bam files only represent chr21 data I've created a target definitions file from chr21 only as well. Here they are:
/corral-repl/utexas/BioITeam/ngs_course/human_variation/target_intervals.chr20.reduced.withhead.intervallist /corral-repl/utexas/BioITeam/ngs_course/human_variation/target_intervals.reduced.withhead.intervallist
And the relevant reference is:
/corral-repl/utexas/BioITeam/ngs_course/human_variation/ref/hs37d5.fa /corral-repl/utexas/BioITeam/ngs_course/human_variation/ref/hs37d5.fa.fai
mkdir $SCRATCH/GVA_Exome_Capture cd $SCRATCH/GVA_Exome_Capture cp $SCRATCH/GVA_Human_trios/raw_files/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam . cp $SCRATCH/GVA_Human_trios/raw_files/target_intervals.chr20.reduced.withhead.intervallist . cp $SCRATCH/GVA_Human_trios/raw_files/ref/hs37d5.fa . cp $SCRATCH/GVA_Human_trios/raw_files/ref/hs37d5.fa.fai .
The run command looks long but isn't that complicated (like most java programs):
java -Xmx4g -Djava.io.tmpdir=/tmp -jar /corral-repl/utexas/BioITeam/bin/picard.jar CollectHsMetrics BAIT_INTERVALS=target_intervals.chr20.reduced.withhead.intervallist TARGET_INTERVALS=target_intervals.chr20.reduced.withhead.intervallist INPUT=NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam REFERENCE_SEQUENCE=hs37d5.fa OUTPUT=exome.picard.stats PER_TARGET_COVERAGE=exome.pertarget.stats
You may notice that the picard tool is found in the BioITeam directory and it is called using the full path to the .jar file. In tomorrows closing tutorial, you'll see two different options to create a small bash script to avoid the java invocation or at least avoid having to remember where picard.jar is stored as even though it is in our path, jar files are not found with the which command.
The aggregate capture data is in exome.picard.stats, but it's format isn't very nice; here's a linux one-liner to reformat the two useful lines (one is the header, the other is the data) into columns, along with the result:
grep -A 1 '^BAIT' exome.picard.stats | awk 'BEGIN {FS="\t"} {for (i=1;i<=NF;i++) {a[NR"_"i]=$i}} END {for (i=1;i<=NF;i++) {print a[1"_"i]"\t"a[2"_"i]}}'
Here is the output of the above command, DO NOT! paste this into the command line.
BAIT_SET target_intervals GENOME_SIZE 3137454505 BAIT_TERRITORY 1843371 TARGET_TERRITORY 1843371 BAIT_DESIGN_EFFICIENCY 1 TOTAL_READS 4579959 PF_READS 4579959 PF_UNIQUE_READS 4208881 PCT_PF_READS 1 PCT_PF_UQ_READS 0.918978 PF_UQ_READS_ALIGNED 4114249 PCT_PF_UQ_READS_ALIGNED 0.977516 PF_UQ_BASES_ALIGNED 283708397 ON_BAIT_BASES 85464280 NEAR_BAIT_BASES 49788346 OFF_BAIT_BASES 148455771 ON_TARGET_BASES 85464280 PCT_SELECTED_BASES 0.476731 PCT_OFF_BAIT 0.523269 ON_BAIT_VS_SELECTED 0.631886 MEAN_BAIT_COVERAGE 46.363038 MEAN_TARGET_COVERAGE 46.76568 PCT_USABLE_BASES_ON_BAIT 0.245533 PCT_USABLE_BASES_ON_TARGET 0.245533 FOLD_ENRICHMENT 512.716312 ZERO_CVG_TARGETS_PCT 0.009438 FOLD_80_BASE_PENALTY 23.38284 PCT_TARGET_BASES_2X 0.849372 PCT_TARGET_BASES_10X 0.484824 PCT_TARGET_BASES_20X 0.435911 PCT_TARGET_BASES_30X 0.401622 PCT_TARGET_BASES_40X 0.36876 PCT_TARGET_BASES_50X 0.335459 PCT_TARGET_BASES_100X 0.173683 HS_LIBRARY_SIZE 5325189 HS_PENALTY_10X 232.05224 HS_PENALTY_20X -1 HS_PENALTY_30X -1 HS_PENALTY_40X -1 HS_PENALTY_50X -1 HS_PENALTY_100X -1 AT_DROPOUT 2.143632 GC_DROPOUT 10.000011 SAMPLE LIBRARY READ_GROUP
Taking the output even further
It is rare that you ever want to work with a single sample. While this format is nice for a single sample, comparing the same data across multiple samples would not be the easiest to do with this format. Instead, putting this information to a file, then using grep and awk you could make a small table of the specific information you want.
Since I don't actually know what capture kit was used to produce these libraries, these may or may not accurately reflect how well the library prep went, but generally speaking having >40x average coverage on your baits (the target regions) is good, as is over 500 fold enrichment. While it may be tempting to consider 52% of reads being 'off bait' as a bad thing, instead consider that ~48% of reads mapped to just ~0.06% of the genome.
Additional Exercises:
These results were based on sample NA12878. How do the other 2 samples (NA12891, and NA12892) from the trios tutorial compare for their enrichment?
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