Category Archives: Best practices in genetic analysis

Gabe Rudy

Between Two Bases: Coordinate Representations for Describing Variants

Have you ever scratched your head when looking up a variant and it seems like the number you have for its position is one off from what it looks like in the file or database? You may be running into … Continue reading

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Gabe Rudy

The Clinical Genome Conference 2015 Highlights

This last week I had the pleasure of attending the fourth annual Clinical Genome Conference (TCGC) in Japantown, San Francisco and kicking off the conference by teaching a short course on Personal Genomics Variant Analysis and Interpretation. Some highlights of … Continue reading

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Bryce Christensen

Looking Beyond the Exons: Splice Altering Variants

There are many approaches that one might use to define a variant as potentially deleterious. For example, we often see analysis workflows based on rare, non-synonymous variants, perhaps incorporating additional annotation sources that capture known or predicted consequences of coding … Continue reading

Posted in Best practices in genetic analysis, How to's and advanced workflows | 1 Comment
Andrew Jesaitis

What’s in a Name: The Intricacies of Identifying Variants

There’s a strong desire in the genetics community for a set of canonical transcripts. It’s a completely understandable and reasonable thing to want since it would simplify many aspects of analysis and especially the downstream communicating and reporting of variants. … Continue reading

Posted in Best practices in genetic analysis, Clinical genetics | Tagged , , | 2 Comments
Greta Linse Peterson

Comparing Meta-Analysis Methods: A Meta Examination

Meta-analysis is an important tool to have in the bioinformatics toolbox. The numbers alone speak for themselves. It is the fourth most requested feature for SVS, and a simple google scholar search for 2014 and 2015 find 17,300 results for … Continue reading

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Gabe Rudy

Unique Labs, Common Tool: Making VarSeq Ready for Clinical Workflows

As VarSeq has been evaluated and chosen by more and more clinical labs, I have come to respect how unique each lab’s analytical use cases are. Different labs may specialize in cancer therapy management, specific hereditary disorders, focused gene panels … Continue reading

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Andreas Scherer

Introducing Phenotype Gene Ranking in VarSeq

Personal genome sequencing is rapidly changing the landscape of clinical genetics. With this development also comes a new set of challenges. For example, every sequenced exome presents the clinical geneticist with thousands of variants. The job at hand is to find out … Continue reading

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Cheryl Rogers

Q&A Surrounding Population-Based DNA Variant Analysis

Last month, Dr. Bryce Christensen presented Population-Based DNA Variant Analysis via webcast. The webcast reviewed the fundamentals of population-based variant analysis and demonstrated some of the tools available in SVS for analysis of both common and rare variants such as the … Continue reading

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Cheryl Rogers

Q&A from our December Genomic Prediction webcast

Our Genomic Prediction webcast in December discussed using Bayes-C pi and Genomic Best Linear Unbiased Predictors (GBLUP) to predict phenotypic traits from genotypes in order to identify the plants or animals with the best breeding potential for desirable traits. The … Continue reading

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Bryce Christensen

To Impute, or not to Impute

Genotype imputation is a statistical technique for estimating sample genotypes at loci that were not directly assayed by sequencing or microarray experiments.  There are several reasons why you might want to use imputation in a research study.  For example: Improve … Continue reading

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