Introducing SVS 7.6!

         February 15, 2012

It’s that time again! We here at Golden Helix are excited to announce SVS 7.6 with more features for DNA-Seq analysis, the addition of RNA-Seq functionality, reorganization of SVS into “packages” including two new ones, and the release of new plot types enabled by Matplotlib. It’s certainly been busy as we pack all this into the sixth installment of the most powerful collection of high-performance analytic tools for quickly identifying statistically significant genotype/phenotype associations and causal variants: SNP & Variation Suite.

DNA-Seq continues to be an area of focus for SVS development as we have gotten feedback and workflow improvement suggestions from our customers. Many labs are focusing their sequencing efforts on small nuclear family studies since that is where early NGS success was found. To address their needs, we’ve added several new features to SVS:

  • Score Variants by Recessive Model – Ranks rare variants that match an expected inheritance patten if a rare autosomal recessive disease model is suspected.
  • Mendelian Error Check – Detects Mendelian inheritance errors in families for quality assurance purposes.
  • Score Compound Heterozygous Regions – Scores genes with compound heterozygous mutations in trios which may cause a recessive trait when multiple damaging variants within a gene are inherited from each parent.

When performing NGS studies, it is common to not have sequenced control samples and thus rely on reference genomes to perform testing, filtering, recoding, and statistics. Now added to the DNA-Seq functionality in SVS is the ability to perform these tasks based on the reference/alternate allele from a reference genome instead of having to rely on major/minor allele.

While DNA-Seq has certainly got some love in 7.6, we are also proud to launch RNA-Seq analysis, modeled after the DESeq ‘R’ package developed by Simon Anders and Wolfgang Huber in 2010. The RNA-Seq in SVS performs differential expression analysis on RNA sequence count data, which is used to find genes that are being over- or under-expressed in a given set of samples relative to a set of controls. As Golden Helix ventures into this new analysis space, we look forward to adding more to RNA-Seq, especially with the release of the Golden Helix and EA pipeline later this year.

Speaking of packages, the configuration of SVS has gotten a face-lift with modules “out” and packages now “in.” Instead of the system being bought piecemeal, SVS is now offered in packages specifically designed around the intended application with all functionality necessary for that application included. This bundling of SVS gives you peace of mind, knowing that all key functionality for the type of work you do, comes with the package.

And we added two new packages: SVS Core Plus and the Power Seat! SVS Core Plus is a data management and visualization tool designed to give users the freedom to manage their data without the headaches caused by bouncing from program to program. Regardless of where the data came from, it can all be handled in an easy-to-use interface with our streamlined genome browser for visualization. All this for just $995.

At the other end of the spectrum, the SVS Power Seat includes functionality from all packages (except PBAT) for a fraction of the price than if they were bought separately. Plus, it will include all new SVS features as they come out! You get it all! (Am I sounding like a infomercial salesperson? Sorry! We’re pretty excited about these new options over here and what they mean for our customers! I’ll try to tone it down.)

Finally, we have added four new plot types for exploring your data visually: NxN scatter plots, plot proportion by group with confidence intervals, side by side box plots, and stacked histograms. These additions were enabled through the integration of the Python Matplotlib library, which will allow even more plot types to be added easily in the future. Stay tuned.

Whew! Well, I guess that’s it for now. We hope you enjoy version 7.6 as much as we enjoyed creating it! For more information about this release, check out our What’s New page or the official Release Notes.

As always, we appreciate any and all feedback about these and any other features.…And that’s my 2 SNPs.

4 thoughts on “Introducing SVS 7.6!

  1. Diego Herrera

    Hello Mrs. Vionas,
    I am using SNP & Variation Suite for the first time and I think it is great! Though I was wondering if there are plans to create Circos Plots in the near future. Will there also be different licensing packages for college students. I am a senior in Biology and unfortunately my parents don’t have a need for it.

    Also, I am analyzing Complete Genomics sequencing data and have had struggles installing the .tar.gsv files. I know that supporting this file format was recently introduced, but I have not been able to successfully import them. I canceled an import, to transfer two 500mb files, after half a day and all night this morning. I have only been able to use Complete Genomics files from the tutorials. I can only imagine how thrilled I’ll be when able to use the data sets specific to my project.

    1. Jessica Vionas

      Hello Diego,

      Thanks for your comment! I see that you got in touch with our support team this morning, so hopefully that resolved the problem with your CGI files. We don’t plan to add circos plots at this time.

      For those in academia, we offer a significant discount over commercial prices. If you are interested in a quote, please fill out or email, and we will get you in touch with an Account Manager.


  2. Liping Hou

    It is good to know DNA-seq module of SVS can do some job on sequencing data for small nuclear families. My question is can I use DNA-seq for collapsing based association test for family data.


    1. Jessica Vionas

      Hi Liping,

      CMC and KBAC (the collapsing tests that SVS has) are only meant for unrelated subjects. We recently introduced new functions for identifying recessive variants and compound heterozygotes in families. We are very interested in methods to collapse information from multiple families within genes or other functional units. We would be happy to listen to any suggestions you might have in this regard.



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