Page Comparison

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1. compressed raw data (the .fastq.gz files)
2. mapped data (the .bam files)
3. variant calls (the .vcf files)
4. the subdirectory ref with special references
5. .bam files containing a subset of mapped human whole exome data are also available on these three; those are the three files "NA*.bam".
6. We've pre-run samtools and GATK on each sample individually - those are the *GATK.vcf and *samtools.vcf files.
7. We've also pre-run samtools and GATK on the trio, resulting in GATK.all.vcf and samtools.all.vcf.
8.  The 1000 Genomes project is really oriented to producing .vcf files; the file "ceu20.vcf" contains all the latest genotypes from this trio based on abundant data from the project. 

Here we show the steps for:

1. Mapping
2. Single-sample variant calling with samtools
3. Multiple-sample variant calling with samtools

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Single-sample variant calling with samtools

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cds 
cd BDIB_Human_tutorial
mkdir samtools_example
cd samtools_example
module unloadload samtools
samtools mpileup -ufu -f $SCRATCH/BDIB_Human_tutorial/raw_files/ref/hs37d5.fa $SCRATCH/BDIB_Human_tutorial/raw_files/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam | bcftools call view-v -vcgc - > trios_tutorial.raw.vcf

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1. By providing separate bam files for each sample:. Note that in the following code block, he trailing \ symbols tells the command line that you are not done entering the command and not to execute any commands until you hit return without a preceding \ mark.

   Wait to run this command until you have after the explanation of the 2 methods and creation of new directories

   Warning
   title Do not Warning, DO NOT run this command yet	.
   Code Block title samtools multi-sample variants: separate bam files samtools mpileup -uf $SCRATCH/BDIB_Human_tutorial/raw_files/ref/hs37d5.fa \ $SCRATCH/BDIB_Human_tutorial/raw_files/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ $SCRATCH/BDIB_Human_tutorial/raw_files/NA12891.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ $SCRATCH/BDIB_Human_tutorial/raw_files/NA12892.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ | bcftools call view-v -vcgc - > multi_sample/trios_tutorial.all.samtools.vcf Note that the trailing \ symbols tells the command line that you are not done entering the command and not to execute any commands until you hit return without a preceding \ mark.

   
   The output file from this option is in $BI/ngs_course/human_variation as all.samtools.vcf

    

    

2. By providing one or more bam files, each containing mapped reads from multiple samples tagged with unique samtools @RG tags.

   Warning
   title Do not run this command
   This command comes with a sizable caveat: If you intend to use this option, you must make sure you tag your reads with the right RG tag; this can easily be done during the samse or sampe stage of mapping with bwa with the -r option, using samtools merge, with picard tools AddOrReplaceReadGroup command, or with your own perl/python/bash commands. The problem is that it requires prior knowledge of all the samples you are going to group together ahead of time, and individual sample input files have to be merged together. There are obviously times that this will be the desired and easier, but for the purpose of this course, we believe it makes more sense to keep all input files separate and only have a single merged output file. As part of the tutorial we do provide the appropriate files necessary for the following command to run. Code Block title samtools multi-sample variants: one or more bam files using @RG samtools mpileup -ufu -f $SCRATCH/BDIB_Human_tutorial/raw_files/ref/hs37d5.fa all.bam | bcftools call view-v -vcgc - > trios_tutorial.all.raw.vcf

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cds
cd BDIB_Human_tutorial
mkdir multi_sample
samtools mpileup -uf $SCRATCH/BDIB_Human_tutorial/raw_files/ref/hs37d5.fa \
  $SCRATCH/BDIB_Human_tutorial/raw_files/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \
  $SCRATCH/BDIB_Human_tutorial/raw_files/NA12891.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \
  $SCRATCH/BDIB_Human_tutorial/raw_files/NA12892.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \
    | bcftools call view-v -vcgc - > multi_sample/trios_tutorial.all.samtools.vcf

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This same type of analysis can be done on much larger cohorts, say cohorts of 100 or even 1000s of individuals with known disease state to attempt to identify associations between allelic state and disease state.

Return to GVA2017 page.