What coding variants do my exome sequences contain, and are any of them phenotypically interesting?

This recipe provides a method to identify coding variants from exome sequencing data. An example use of this recipe is a case where an investigator may want to identify variants from their alignment data and evaluate the phenotypes.

In particular, this recipe uses the variant detector, FreeBayes, in Galaxy to identify potential variants from short-read alignments and assign them quality scores. We also use other Galaxy tools to pare these potential variants down to a high quality subset and to compare them against a catalog of gold-standard variants. Finally, we use IGV to visualize these variants alongside known pathogenic variants.

Why identify genetic variants? Genetic variants are DNA sequence differences that occur with relative frequency in a population. These variants include single nucleotide polymorphisms (SNPs), insertions and deletions (indels), multi-nucleotide polymorphisms (MNPs), and complex events that are combinations of indels and polymorphisms. With the advent of next generation sequencing technologies, it has become common practice to identify variants using sequence data. This is usually accomplished by comparing sequence data from many samples or individuals against a reference. Identified variants can have many practical applications. For instance, variants that correlate strongly with disease risk may provide insight into the disease's genetic underpinnings. Variants may also be used as biomarkers for disease risk or to DNA "fingerprint" individuals.

Why use exome sequencing data? The human exome comprises all the exonic regions of the human genome. That is, it includes all the DNA sequences that, after transcription into RNA, remain after RNA-splicing, which amounts to about 1% of the entire genome. Whole exome sequencing (WES) thus generates much less data than whole genome sequencing (WGS). Furthermore, since WES focuses almost exclusively on protein-coding regions it is an effective method for identifying coding variants.

NOTE: Working with DNA sequencing data is a computationally and resource intensive process. Files containing raw sequence reads can be many gigabytes in size and can be cumbersome to manage. To maintain the usability of this recipe, we have chosen to work with only a partial region of the human genome, specifically the exonic regions of a chromosome 20.

To complete this recipe, we will need a number of datasets of various types. First, we will need a set of exonic sequence reads that have been pre-processed for quality and duplicates and aligned to the genome. In this example, we will use aligned reads generated by the 1000 Genomes Project that have been mapped to the exonic regions of chromosome 20. These samples are derived from 10 individuals in the project's Great Britain population cohort. We also require a reference genome in FASTA format, which we will use to identify variants. Finally, we will need a set of gold-standard variants as well as a set of variant annotations to compare against our discovered variants. We will need the following datasets, which can be downloaded from the following GenomeSpace Public folder:

Public > RecipeData > AlignmentData > 1000GP.GBR.chrom20.exome.tar.gz: This is an archive of aligned, sorted, and duplicate-marked chromosome 20 exome reads in BAM format. Specifically, the archive includes the alignments of 10 individuals drawn from the Great Britain population in Phase 3 of the 1000 Genome Project.

Public > RecipeData > SequenceData > hs_ref_GRCh37.p5_chr20.fa: This is a FASTA file containing sequences for a human reference genome, chromosome 20 only.

Public > RecipeData > VariantData > hapmap_3.3.b37.chr20.vcf: This is a VCF formatted file containing genetic variants identified by the International HapMap Project (version 3.3) filtered to include only variants on chromosome 20.

Public > RecipeData > VariantData > dbSNP_135.no1000GProduction.chr20.pathogenic.vcf: This is a VCF formatted file containing genetic variants identified by the NCBI Short Genetic Variations database, dbSNP.

NOTE: These data are modified files downloaded from the 1000 Genomes Project, the International HapMap Project, and the NCBI Short Genetic Variations database.