In this blog series, I will discuss the architecture of a state of the art secondary pipeline that is able to detect single nucleotide variations (SNV) and copy number variations (CNV) in one test leveraging next-gen sequencing. In Part I, we reviewed genetic variation in humans in general and looked at the key components of a systems architecture supporting this… Read more »
Human genetic variation makes us unique. On average, humans are to 99.9% similar to each other. Understanding in detail what the nature of the difference in our genetic make-up is all about allows us to assess health risks, and eventually enables Precision Medicine as we determine treatment choices. Furthermore, it enables scientists to better understand ancient human migrations. It gives… Read more »
In the past couple of weeks, the topic of the Filter and Quality fields in the popular ExAC population catalog has come up a number of times. It turns out that unlike the 1000 Genomes project, which decided to very heavily filter their variant list to only contain variants they consider high quality, ExAC chose to include more dubious variants… Read more »
Since we released our Phenotype Gene Ranking algorithm in VarSeq, it has become a staple of the way people conduct their analysis. It allows for a combination of filtering with ranking to prioritize follow-up interpretations of analysis results. Our PhoRank algorithm will be available in our upcoming SVS release to also aid in the numerous research workflows performed on SNPs… Read more »
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2000 human GWAS reports now appear in scientific journals. In fact, we see its adoption increasing beyond the human-centric research into the world of… Read more »
ExAC CNVs were released publicly with a recent publication, providing the full set of rare CNVs called on ~60K human exomes. While there are many public CNV databases out there, this is the first one that was derived from exome data, and thus includes both extremely rare and very small CNV events. With the recent release of Golden Helix’s CNV calling… Read more »
December’s webcast will provide the Golden Helix community with a more in-depth look at CNV analysis in VarSeq. On December 7th, Dr. Nathan Fortier will discuss the challenges and metrics surrounding CNV detection and then demonstrate VarSeq’s new capability from VCF to clinical report. Wednesday, December 7th @ 12:00 PM, EST Numerous studies have documented the role of Copy Number Variations (CNVs)… Read more »
One of the tools at the top of the toolbox for researchers working with microarray data is genotype imputation. Genotype imputation is the process of inferring the genotype of one or more markers based on the correlation pattern (aka linkage disequilibrium or LD) of the surrounding markers for which genotypes are known. We have now integrated a natively ported version of BEAGLE into Golden… Read more »
Dr. Sergey Kornilov, a Duncan Scholar in Molecular and Human Genetics at Baylor College of Medicine, combines his broad psychology background with genetics to research the genetic basis of neurodevelopmental disorders with a unique dual perspective. Neuro-developmental disorders, for example, those of the spoken and written language, affect many worldwide – up to 10% of preschool children. In most cases, these… Read more »
Copy Number Variants have been important to clinical genetics for quite a while now. So, what has made now the right time to be looking at calling CNVs from NGS data? Well, there are a number of good reasons. The dominant one is simply that the NGS data you are already creating for calling variants can be used in many cases… Read more »
Question: Now that I’ve added annotation sources for my sample, filtered down to a list of interesting variants, flagged those variants and generated a clinical report, can I save or copy the annotation sources and filters for use on another sample? Short Answer: Yes! Long Answer: VarSeq was created with ease and efficiency in mind. In VarSeq, once you’ve defined… Read more »
Now available in SVS! Increasingly important in the analysis of the genotype to phenotype relationship is accurately accounting for the relatedness of samples. This is especially important to model correctly in plant and animal populations where man-directed breeding shapes the relationship structure. Along with trait association, one of the high-value use cases for genotyping animals and plants is to estimate… Read more »
Every month hundreds of clinicians and researchers access the variety of free resources on the Golden Helix website. Our resource library hosts eBooks, webcasts and tutorials to keep the community apprised of new methods, informed on best practices and to help our customers get the most out of their software purchase. Here is a list of the 5 most watched webcasts… Read more »
Variant Normalization: Underappreciated Critical Infrastructure It may surprise you to learn that every variant in the human genome has an infinite number of representations! Of course, although true, I’m being a bit hyperbolic to prove a point. Even seemingly simple mutations like single letter substitutions are legitimately represented differently in the local context of other mutations that can be described… Read more »
When the new human reference genome was released over two years ago, it was hailed as a significant step forward for next generation sequencing. Compared to GRCh37, the new GRCH38 reference assembly fixed gaps, repaired incorrect sequences and offered access to sections of the genome that had been previously unaccounted for. Despite these improvements, adoption of the new assembly has… Read more »
There used to be much energy expended at conferences, bioinformatics forums and even publications about what was the better strategy for interpreting variants of clinical significance: Rule-based filtering and classification mechanisms or rank-based prioritization through all-encompassing “pathogenicity” scores. Both have shown to be effective. Rule-based systems, as exemplified in this filtering diagram in Baylor’s ground-breaking paper on clinical whole-exome sequencing… Read more »
Solving the Eigenvalue Decomposition Problem for Large Sample Sizes Since our introduction of the mixed model methods in SVS, along with GBLUP, we have been very pleased to see it used by a number of customers working with human and agri-genomic data. As these customers have grown their genomics programs, the number of samples they have for a given analysis… Read more »
Submit directly to N-of-One from VarSeq If you or your lab uses N-of-One solutions for clinical annotations, here’s some good news: You can now submit directly to N-of-One from VarSeq! N-of-One’s set of preferred transcripts may differ from those outputted by our algorithms in VarSeq, so our solution was built with that in mind. Our slick, easy to use, and… Read more »
Concepts and Relevance of Genome-Wide Association Studies Genome-Wide Association Studies continue to be a very effective method for determining the underlying cause of disease, Golden Helix is happy to share our long-standing knowledge with the community. We are very happy to announce that “Concepts and Relevance of Genome-Wide Association Studies”, a paper surrounding GWAS was recently published in Science Progress… Read more »
As VarSeq continues its adoption amongst clinical labs and researchers looking for reproducible workflows for variant annotation, filtering and interpretation, we have continued to prioritize the addition of features to assess the quality of the upstream data at a variant, coverage and now sample level. The Importance of Quality Assurance Sample prep and sequencing problems are difficult to detect through the analysis… Read more »
