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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. (these files are from old versions)
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. 

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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.

   Note
   title Warning, DO NOT run this command yet.
   Code Block language bash title samtools multi-sample variants: separate bam files #samtools mpileup -uf $SCRATCH/GVA_Human_trios/raw_files/ref/hs37d5.fa \ $SCRATCH/GVA_Human_trios/raw_files/NA12878.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ $SCRATCH/GVA_Human_trios/raw_files/NA12891.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ $SCRATCH/GVA_Human_trios/raw_files/NA12892.chrom20.ILLUMINA.bwa.CEU.exome.20111114.bam \ | bcftools call -v -c - > multi_sample/trios_tutorial.all.samtools.vcf

   
   The output file from this option is in $BI/ngsgva_course/human_variation as GVA2021.all.samtools.vcf.samtools.vcf if you want to work with it without having to wait on the analysis to run personally.

   
   

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

   Note
   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 -u -f $SCRATCH/GVA_Human_trios/raw_files/ref/hs37d5.fa all.bam | bcftools call -v -c - > trios_tutorial.all.raw.vcf Tip title An observation for the individuals interested in phylogenies I have a hunch that the use of read tags could be useful for your analysis IF you have a finite set of samples that you intend to analyze, have all the data available at once, and are willing to repeat your analysis from near scratch in the event of either of these factors changing. This is an excellent example of the importance of finding papers that have done similar analyses, and mimicking as much of what they have done as possible/reasonable.

Based on the discussion above, we are selecting the first solution and providing details of how this command should be run. As this command will generate very little output and take ~45 minutes to complete, you are once again reminded that the output file from this option is in available $BI/ngsgva_course/human_variation as GVA2021.all.samtools.vcf vcf if you want to work with it without having to wait on the analysis to run personally.

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cat trios_tutorial.all.samtools.vcf | tail -10000 | awk '{if ($6>500) {print $2"\t"$10"\t"$11"\t"$12}}' | grep "0/0" | sed s/':'/'\t'/g | awk '{print $2"\t"$4"\t"$6}' | tail -100 | sort | uniq -c | sort -n -r

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