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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. 

Single-sample variant calling with

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bcftools

We would normally use the BAM file from a previous mapping step to call variants in this raw data. However, for the purposes of this course we will use the actual BAM file provided by the 1000 Genomes Project (from which the .fastq file above was derived, leading to some oddities in it). As a bonus tutorial, you could map the data yourself and using what you learned in the bowtie2 tutorial and then use the resultant .bam files.

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One potential issue with this type of approach is that vcf files only record variation that can be seen with the data provided. When all reads mapping to a given location exactly match the reference (i.e. is homozygous wildtype relative to the reference) there will be no data. Which looks the same as if you had no data in those regions; this leads us to our next topic. 

Multiple-sample variant calling with

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bcftools

Not being able to tell between no data and wildtype is not the end of the world for a single sample, but if you're actually trying to study human (or other organism) genetics, you must discriminate homozygous WT from a lack of data. This is done by providing many samples to the variant caller simultaneously. This concept extends further to populations; calling variants across a large and diverse population provides a stronger Bayesian prior probability distribution for more sensitive detection.

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