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 of the individuals being studied. The genomic prediction model (a single mixed model regression equation) also uses the contribution of each genetic loci to build the model, as well as to solve for EBV and ASE. Continue reading
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 that require specialized processing before meaningful interpretation can begin. Continue reading
You probably haven’t spent much time thinking about how we represent genes in a genomic reference sequence context. And by genes, I really mean transcripts since genes are just a collection of transcripts that produce the same product.
But in fact, there is more complexity here than you ever really wanted to know about. Andrew Jesaitis covered some of this in detail as he dove deep in the analysis of variant annotation against transcripts in his recent post The State of Variant Annotation: A Comparison of AnnoVar, snpEff and VEP. Continue reading
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,” that outlined a new approach to identify clusters of runs. Continue reading
Earlier this year we completed the marriage of SVS and GenomeBrowse. When we released Version 8 of SVS we completed a major engineering task. A lot of things under the hood of both products had been changed to create a seamless experience for our users. The new and improved SVS platform is based on a technology stack that allows us to accelerate method development and even helps us to launch new product faster. More about the latter in another blog post.
Now, the new SVS 8.2 comes with a number of new methods and features:
“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 difficulty with the format’s complexity or practical limitations. Among other responsibilities at Golden Helix, I give frequent demonstrations of our SVS software and train people to use it. In this capacity I have observed many of the challenges people encounter with VCF files, and I’d like to discuss some of those issues here. Continue reading
Drumroll, please! Voting has come to a close for the 2014 Golden Helix T-shirt contest, and there were some clear favorites among the finalists.
We are very excited at the wealth of creativity that came forward with this contest, and are happy to announce that our final decisions have been made. Continue reading
Genetic improvement in livestock, particularly dairy cattle, has been a priority for both industry and researchers for nearly a century. While the animal itself is the foundation for improvement, our research and the implementation of improvement has progressed with developing technologies and priorities. In terms of genetics, we have evolved from basic measures of heritability to identifying specific mutations and their biological role affecting a trait. My research focuses on population structure and trait association in domestic animals using high-density genome-wide single-nucleotide polymorphism (SNP) array data and fine-mapping sequence data. My end goal is to conduct research that will assist in the genetic improvement of domestic animals through an increased understanding of the genetic mechanisms controlling biological pathways and the development of genomic tools for implementation. Continue reading
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 the logistic regression model to a linear model so that EMMAX (Efficient Mixed Model Association eXpedited) can be used to solve the equations.
We are also very excited that we have been accepted to present this material at ASHG this October (we’ll be in booth 422)! More importantly, we will be making this method available to our customers with the next release of SVS due out in August. Continue reading
With the t-shirt submission deadline behind us, it’s time for the exciting part of the contest – picking the winners! We received a ton of fantastic designs and had a hard time narrowing them down. But, the Golden Helix team has picked seven designs that truly embody the Golden Helix spirit.