Wondering what SVS can do for a PhD student? Just ask Sander.

Sander van der Laan is like many Ph.D. students in the genomic analysis space. He has a lot of data and a lot of ideas about how to analyze it. His professor wants results. He’s the only one doing genetics (everyone else in his department is doing proteomics), so there’s always too much to do. And he finds command-line tools frustrating since he was trained as a medical biologist.

Realizing that there must be tools out there to help, Sander selected SNP & Variation Suite (SVS) to enable him to conduct his research faster, easier, and more efficiently.

Recently, we sat down with Sander (okay, talked via Skype) to chat about his research and how SVS has empowered him to do genomic research.

Jessica: Can you tell us a little bit about your research?

Sander: Sure – I am currently one of about sixty people in my department studying atherosclerosis as part of the Athero-Express Biobank. They have collected DNA from blood and plaque in over 800 patients. My research aims specifically at predicting why some patients with atherosclerosis experience adverse cardiovascular events (such as stroke) and others do not base on biological proteins.

J: What made you go looking for genetic analysis software?

S: I began working with genetic data during an internship at Erasmus in their bioinformatics department. The internship was six months long and in that time I was able to get from raw data to something you could work with using command-line tools. However, as soon as my internship was finished, I knew the data wasn’t good enough for reliable results. When I began my Ph.D., I knew I had to start over.

J: Why did you choose SVS?

S: A lot of software I found was geared toward expression pathways and other proteomic applications, but I am doing genomic analysis. One popular commercial package I considered didn’t handle SNP analysis very well, and I didn’t find it very user-friendly. The other thing was that the tool had to be compatible with various file formats since I planned to impute the data using BEAGLE and BEAGLECALL.

J: And SVS fit the bill! Now that you’ve had the software for a while, what do you like about it?

S: It’s intuitively easy to use and importing and exporting various file formats makes it so flexible. I like the free training, tutorials, and website that made learning the software easy. The support team at Golden Helix is incredible and answered every question I had. There are so many things I like. The annotation track library is important for exploring my data. I love that SVS is continually improved through regular updates. The Python scripting interface is a great feature for making my own customizations. And SVS can handle large-scale genomic data. On a particularly large dataset, our free command-line tools crashed and were unable to continue whereas SVS handled the data without any problems.

J: One last question: what results have you been able to achieve using Golden Helix software?

S: I was able to re-clean the data in one-third the time it took me with command-line tools. I imagine that without SVS, I would still be working on quality assurance today. With Golden Helix, my frustration went out the door.

Read the Case Study featuring Sander on www.goldenhelix.com »
…And that’s my (and Sander’s) 2 SNPs.

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