Category Archives: Best practices in genetic analysis

Optimized Breeding Selection via Genomic Prediction

What is Genomic Prediction? Genomic prediction is an algorithm widely used to improve desirable phenotypic traits in agriculture. For example, the cattle industry uses genomic prediction to improve beef quality and palatability as well as improve dairy production (1,2). By using genomic prediction, researchers can minimize multiple expenses in breeding industries as well as diminish the need for performing cumbersome… Read more »

GRCh38 Liftover: Ensuring Top Quality Variant Analysis


In a recent webcast, our VP of Product and Engineering Gabe Rudy gave us insight into the current capability and benefits to lifting over to the GRCh38 assembly. Golden Helix fully supports this transition into the most recent reference assembly and have developed our tools on both the 38 and 37 fronts. The purpose of this blog is to not… Read more »

VSClinical Best Practice Workflows: Part I

VSClinical is our most recent product that allows users to evaluate variants according to the ACMG guidelines. As with any tertiary analysis, there is a need to implement best practices into your workflow and using VSClinical for the ACMG guidelines is no exception. That said, we have put together a Best Practices Blog Series, with the purpose of discussing some… Read more »

Clinical Variant Interpretation: Part VII

VSClinical algorithm

Automating the ACMG Guidelines and Providing Scoring Recommendations As we discussed in our recent webcast on VSClinical, the process of scoring the ACMG guidelines requires evaluating evidence for the connection between a variant and the disorder or condition being evaluated by the genetic test for an individual. These lines of evidence cover clinical presentation, gene function, bioinformatic annotations and in-silico… Read more »

Clinical Variant Interpretation: Part V

VSClinical algorithm

Revisiting the Five Splice Site Algorithms used in Clinical Genetics Interpretation of variants in accordance with the ACMG guidelines requires that variants near canonical splice boundaries be evaluated for their potential to disrupt gene splicing [1]. The five most common tools for splice site detection are NNSplice, MaxEntScan, GeneSplicer, HumanSplicingFinder, and SpliceSiteFinder-like. Because these algorithms have been made easily accessible… Read more »