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In 2014, I did some benchmarking of variant effect prediction algorithms. You can read about that in this blog post.

I wanted to follow up on that work and see how the current batch of algorithms are preforming. VEP and SnpEff are the most commonly used algorithms these days, so I limited my analysis to them.

I used the same input VCF that I created originally. It contains all snps, all 1 base pair insertions and deletions, and 2 possible 2 and 3 base pair insertions and deletions at all locations spanning the CFTR gene with 100bp margins on either side.

You can annotate this vcf with each algorithm using the CWL scripts provided.

For VEP:

docker build --tag="andrewjesaitis/vep" -f Dockerfile.vep .
cwltool vep-workflow.cwl cftr-job-vep.yml

Similarly for SnpEff:

docker build --tag="andrewjesaitis/snpeff" -f Dockerfile.snpeff .
cwltool snpeff-workflow.cwl cftr-job-snpeff.yml

Then you can open the Jupyter Notebook and rerun all cells.

Otherwise just skip to the punchline and open the notebook on Github.

I've written up a discussion of the results and dug into some particularly troublesome variants on my blog.

I've also added the gzip'd vcf that highligh some mismatches. Note that these vcfs contain repeated variants (since I am outputing a single variant-transcript pair per line). The keys in the INFO field are self documenting. These files are impact_mismatch.vcf.gz, effect_mismatch.vcf.gz, and hgvs_mismatch.vcf.gz.


Work supporting the comparison of SnpEff and VEP effect prediction and HGVS identifiers







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