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Everything after the "LJB_GERP++" field in exome_summary came from the original VCF file, so this file REALLY contains everything you need to go on to functional analysis! This is one of the many reasons I like Annovar.
Scavenger hunts!
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| title | Find the gene with two frameshift deletions in NA12878 |
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| title | Answer is.... See what you can come up with as answer on your own and then click here for an answer and an expansion of how to generate more meaningful representations of that data |
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The final answer is "DEFB126" | Code Block |
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| grep "frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv |
(just grep "frameshift" from the exome_summary file) |
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"frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv # this will print all the lines which contains the text "frameshift"
# From the output you can key into the first few columns having the information you are interested in: location-classification, gene, mutation type each separated by commas. This should lead you to think about adding the awk command to print only some columns.
grep "frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv | awk -F"," '{print $2"\t"$3}' # the -F"," syntax forces it to split on commas
# you will likely notice this data is easier to visualize, and in this case you can probably see what gene is represented multiple times, but why stop there ... lets add the uniq -c command to the pipes to have linux count for us
grep "frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv | awk -F"," '{print $2"\t"$3}' | uniq -c
# for the number of mutations we have this is sufficient, but for increased numbers of mutations where you may be interested in displaying them in a particular order. This can be done by adding the sort command
grep "frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv | awk -F"," '{print $2"\t"$3}' | uniq -c | sort -r # the -r option on the sort command sorts in reverse order |
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| title | Compare the output of GATK and SAMtools for the NA12878 sample to see if you can find any differences |
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| grep "frameshift" NA12878.chrom20.GATK.vcf.exome_summary.csv | awk -F"," '{print $2"\t"$3}' | uniq -c | sort -r
grep "frameshift" NA12878.chrom20.samtools.vcf.exome_summary.csv | awk -F"," '{print $2"\t"$3}' | uniq -c | sort -r |
GATK detects 2 frameshift insertions in DNMT3B, and 1 each in PRNP and LAMA5. SAMtools detects 1 frameshift deletion in each of NTSR1 and CTSA that GATK does not detect. |
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| title | Test "genetic drift" vs. "functional selection" - e.g. is the distribution of variants different among non-coding regions, synonymous changes in coding regions, and non-synonymous changes in coding regions? |
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Compare the output of these three commands: | Code Block |
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grep intergenic NA12878.chrom20.samtools.vcf.genome_summary.csv | awk 'BEGIN {FS=","} {print $26"\t"$27}' | sort | uniq -c | sort -n -r | head -20
grep exonic NA12878.chrom20.samtools.vcf.genome_summary.csv | grep -w synonymous | awk 'BEGIN {FS=","} {print $25"\t"$26}' | sort | uniq -c | sort -n -r | head -20
grep exonic NA12878.chrom20.samtools.vcf.genome_summary.csv | grep -w nonsynonymous | awk 'BEGIN {FS=","} {print $25"\t"$26}' | sort | uniq -c | sort -n -r | head -20 |
Do you notice a pattern? What's the right statistical test to determine whether non-synonymous mutations might be under different selective pressure than intergenic or synonymous mutations from this data? |
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