Tag Archives: SVS

Genomic Prediction and How it’s Used

Golden Helix is excited to host a webinar on Tuesday August 26th discussing the Genomic Prediction methods which were recently integrated into the SVS software. Genomic prediction uses several pieces of information when calculating its results. Genetic information is used to predict the phenotype or trait for the individuals. The phenotypic trait data can be provided for a subset or for all… Read more »

Leveraging SVS for NGS Workflows

Over the last decade, DNA sequencing has made vast technological improvements. With the cost of sequencing decreasing significantly, sequencing technology has become a product for the masses. The sequencing technology and programs that were once used exclusively by major research institutions are now becoming available in many research facilities around the globe. These tools produce large amounts of data sets… Read more »

Runs of Homozygosity Updated

      Alison Figueira    August 12, 2014    No Comments on Runs of Homozygosity Updated

For the SVS 8.2 release we decided to improve upon the existing ROH feature. The improvements include new parameters to define a run and a new clustering algorithm to aide in finding more stringent clusters of runs. The improvements were motivated by customer comments and a recent research paper by Zhang 2013, “cgaTOH: Extended Approach for Identifying Tracts of Homozygosity,”… Read more »

Have you ever had a bad experience with a VCF file?

“Who has ever had a bad experience with a VCF file?” I like to ask that question to the audience when I present data analysis workshops for Golden Helix. The question invariably draws laughter as many people raise their hands in the affirmative. It seems that just about everybody who has ever worked VCF files has encountered some sort of… Read more »

New MM-KBAC Method Explained

      Golden Helix    July 29, 2014    6 Comments on New MM-KBAC Method Explained

Last month, June 2014, we announced a new method that Golden Helix developed–the soon to be available MM-KBAC. MM-KBAC, or Mixed Model Kernel Based Adaptive Clustering combines the KBAC method developed by Lui and Leal (2010) with a random effects matrix to adjust for relationships between samples. The KBAC algorithm takes a binary dependent variable and transformations are used to convert… Read more »