Tag Archives: variants

SGD variant viewer

Video Tip of the Week: SGD’s Variant Viewer

Variant viewers are very popular. As we get more and more sequence data, the challenge of looking across many samples only gets more and more important. So I always like to see how different groups are doing this. I’m still waiting for the killer app on this–the pan-genome graphs with all the paths along different genomes/regions displayed. But there are many good examples out there now for seeing variations in different species, strains, or different individuals.

This week’s Video Tip of the Week looks at the yeast variation viewer from SGD. It shares some features with other visualization tools (lollipops are hip, lately). But it has a very quick way of switching back and forth from DNA to protein that isn’t always available on variation viewers that I’ve tried before.

So this week’s video tip shows you their quick tour of their recently added variant viewer tool.

I won’t go into any more detail–they have a whole paper in the NAR database issue 2016 that describes the development and the features (below). Go over and try it out, it’s speedy and easy to use.

Quick link:

SGD: http://www.yeastgenome.org/


Cherry, J., Hong, E., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E., Christie, K., Costanzo, M., Dwight, S., Engel, S., Fisk, D., Hirschman, J., Hitz, B., Karra, K., Krieger, C., Miyasato, S., Nash, R., Park, J., Skrzypek, M., Simison, M., Weng, S., & Wong, E. (2011). Saccharomyces Genome Database: the genomics resource of budding yeast Nucleic Acids Research, 40 (D1) DOI: 10.1093/nar/gkr1029

Sheppard, T., Hitz, B., Engel, S., Song, G., Balakrishnan, R., Binkley, G., Costanzo, M., Dalusag, K., Demeter, J., Hellerstedt, S., Karra, K., Nash, R., Paskov, K., Skrzypek, M., Weng, S., Wong, E., & Cherry, J. (2016). The Saccharomyces Genome Database Variant Viewer. Nucleic Acids Research, 44 (D1) DOI: 10.1093/nar/gkv1250


Friday SNPpets

This week’s SNPpets offered a lot of good stuff, everybody must be out of their summer vacation mode and back to the lab. There’s a BLASTX alternative, a helpful tip in workshop teaching, quack DTC tests, handy errors (really), new variant database DIVAS, exome sequencing patients and outcomes, imputation, 24M novel rare variants, variant caller comparison, the long term evolution experiment hits a milestone, new citrus resources, eQTLs, and more. The best item this week, though, was a story of a family with a rare disease situation that found genomics researchers via Reddit, and that gave them answers and hope.

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…


Friday SNPpets

A pretty typical week, lots of great software and fascinating data. Methylation analysis, 7-set Venn, a new genomics comic strip, some personal genomics, some clinical genomics.  Gosh, I love my life. But the most amusing and unexpected snippet: misuse of a Circos image of caught the eye of the developer, and he found that Jurassic Park dinosaurs are made of corn.

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…



Friday SNPpets

This week’s SNPpets include free passes for Phil Bourne’s keynote talk in Boston next week, software for analysis of the effects of protein variants, interpreting your own genome, a book review of a tome on bioinformatics that’s getting very good chatter among the practitioners, free image analysis tools for microscopy, and more….

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…


Note: We might need to change the format of FridaySNPpets, since Twitter just made it harder to capture the text format for pasting. But embedded tweets may not be searchable, which is one reason we’ve done these this way. Anyway, stay tuned.

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