The summary file also contains QC information for the gene annotation used as input. Large deviations from the expected values for the organism being sequenced might indicate problems with either the sequencing or variant calling pipelines. In our example, the summary file contains basic quality control statistics calculated from the variant file: for our data, the Ts/Ts ratio is close to 2.0 (Figure 1c) and missense / silent ratio is around 1.0 (Figure 1d), both of which are expected for human data (but these numbers may differ for other species). It can also be created in comma-separated values format (CSV) to be used by downstream processing programs as part of an automated pipeline. a text file summarizing the number of variant types per gene.Ĭreation of the summary files can be de-activated to speed up the program (for example, when the application is used together with Galaxy).īy default, the statistics file "ex1.html" is a standard HTML file that can be opened in any web browser to view quality control (QC) metrics.the HTML file containing summary statistics about the variants and their annotations.Java -Xmx8g -jar snpEff.jar -v -stats ex1.html GRCh37.75 protocols/ex1.vcf > protocols/
In this example, we annotate (all these annotations are activated by default when using SnpEff): SnpEff has several command line options that can be used in this annotation stage and which are described in detail in the online manual. Our first step is to annotate each of the ~500,000 variants contained in the VCF file.īy default, SnpEff adds primary annotations and basic impact assessment for coding and non-coding variants as described above. Step 1: Primary variant annotation and quality control. We will then use SnpSift, a filtering program to extract the most significant variants having annotations meeting certain criteria. In the following protocol, SnpEff will add annotation fields to each variant record in the input VCF file. Genomic variants are usually provided in a VCF file containing variant information of all the samples storing the variant data in a single VCF file is the standard practice, not only because variant calling algorithms have better accuracy when run on all samples simultaneously, but also because it is much easier to annotate, manipulate and compare individuals when the data is stored and transferred together.Ī caveat of this approach is that VCF files can become very large when performing experiments with thousands of samples (from several Gigabytes to Terabytes in size). For the purpose of this example, we assume that we do not know the causative variant, but that we know that we are dealing with a Mendelian recessive disorder, where the three siblings are affected (cases), but the 14 parents and grandparents are not (controls). This will be done using a dataset of variant calls for chromosome 7 from a pedigree of 17 healthy individuals, sequenced by Complete Genomics, in which a coding variant causing cystic fibrosis was artificially introduced in three siblings (see Materials). The goal in this example is to use SnpEff to find a mutation causing a Mendelian recessive trait.
#ANNOTATE EXAMPLE SOFTWARE#
Step 2: Counting variants in case and control subjects.Įxample 2: Software Integration (GATK & Galaxy) data.jsonl -F recipe.Building databases. Stream = get_stream (source ) return Command-line usage prodigy custom my_dataset. recipe ( "custom" ) def custom_recipe (dataset, source ) :
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