Tip of the Week: SKIPPY predicting variants w/ splicing affects
More and more disease-causing mutations are being identified in exonic splicing regulatory sequences (ESRs). These disease effects can result from ESR mutations that cause exon skipping in functionally diverse genes. In today’s tip I’d like to introduce you to a tool designed to detect exon variants that modulate splicing. The tool is named SKIPPY and has been developed and is maintained by groups in the Genomic Functional Analysis research section of the NHGRI.
At the end of the post I cite a very well-written paper describing the development of SKIPPY, as well as the background on why the tool was developed. I won’t have time to go into all those details, but if you are interested the paper is freely available from Genome Biology. The site also has nice, clear documentation and example inputs – which I will use as my examples. Splicing can be modulated in a variety of ways, including the loss or gain of exonic splicing enhancers (ESEs) or silencers (ESSs). Variants accomplishing either of those are referred to as splice-affecting genome variants, or SAVs. Not all of the abbreviations are explained on the results page, as you will see in the tip, but all are explained in detail in the SKIPPY publication, and the ‘Methods and Interpretations‘ and ‘Quick Reference and Tutorial‘ areas of the site.
I first found the tool because it was mentioned in a nice review entitled “Using Bioinformatics to predict the functional impact of SNVs“, which is a paper that reviews mechanisms by which point mutations can effect function, describes many of the algorithms and resources available & provides some sage advice. I’ll post more on it in a later post. For now, check out the tip & the SKIPPY resource, and if you use the site please let us know what you think.
Woolfe, A., Mullikin, J., & Elnitski, L. (2010). Genomic features defining exonic variants that modulate splicing Genome Biology, 11 (2) DOI: 10.1186/gb-2010-11-2-r20
Cline, M., & Karchin, R. (2010). Using bioinformatics to predict the functional impact of SNVs Bioinformatics DOI: 10.1093/bioinformatics/btq695