Author Archives: Nathan Fortier

About Nathan Fortier

Nathan Fortier, Ph.D, Director of Research for Golden Helix, joined the development team in June of 2014. Nathan obtained his Bachelor’s degree in Software Engineering from Montana Tech University in May 2011, received a Master’s degree in Computer Science from Montana State University in May 2014, and received his Ph.D. in Computer Science from Montana State University in May 2015. Nathan works on data curation, script development, and product code. When not working, Nathan enjoys hiking and playing music.

Annotating and Cataloging CNVs in Varseq – Webcast Q&A

We love when our viewers send questions in during the webcast but unfortunately we can’t answer all of them during the time allotted!  If you asked a question see below for answers, or if after viewing, you have any questions that weren’t asked, please feel free to send those over to support@goldenhelix.com. Does this work for FFPE derived DNA or ctDNA?… Read more »

Functional Predictions & Conservation Scores in VSClinical

In our previous webcast, we discussed the splice site algorithms for clinical genomics within VSClinical. We took it a step further in yesterday’s webcast and looked at the functional predictions and conservation scores. We had a great turnout for this event with lots of great questions from the attendees. I’d like to recap our Q&A for anyone else who might… Read more »

Clinical Variant Interpretation: Part VI

VSClinical algorithm

Functional Predictions and Conservation Scores in VSClinical Several algorithms have been developed to predict the impact of amino acid substitutions on protein function and quantify conservation of nucleotide positions. These methods provide vital supporting evidence to clinicians when interpreting variants in accordance with the ACMG guidelines. The two most popular functional prediction algorithms are SIFT and PolyPhen2, while the most… 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 »

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 in the bioinformatics tool Alamut, they have been canonized for… Read more »