Golden Helix Newsletter

 
Precision Medicine
Precision Medicine Part VI - The Educational Challenge
by Dr. Andreas Scherer,
President & CEO


Precision medicine will fundamentally change how health care is practiced. Of course, we have a long way to go. For most practitioners today, their knowledge of the human genome was established many years ago. However, new therapies and diagnostic methods are pouring in on a daily basis. So, how do we make sure that the current and future health care workforce understands the complexities and intricate details of this field?

A starting point is a better understanding of how to use an individual's genomic information to determine targeted treatment options, tailored to the individual patient. This requires:
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Precision Medicine Part IV Adoption by Patients and Health Care Professionals
by Dr. Andreas Scherer,
President & CEO


Precision Medicine leverages the most innovative technology advances in the field of genetics. The concept is "en vouge"! We know that the science will give us increasingly better treatment options. I have covered this in my previous blog post. But does it really matter? Precision medicine only will become a reality if both patients and the health care professionals treating them will act on the information at hand.

So, where do we stand currently on this issue?
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Bioinformatics
Precision Medicine - Part V - Bioinformatics Pipelines and Systems Infrastructure
by Dr. Andreas Scherer,
President & CEO


The genetics industry is undergoing a fundamental shift from a clinical science focus to a bioinformatics focus. Genetic services require a greater level of data analytics sophistication than is required for other laboratory testing. Currently, data generated by new tests overwhelms current information technology systems and human interpretation capabilities. This is one of the reasons that we at Golden Helix strive to simplify the process of analyzing and interpreting the data, so that it is possible for a wider group of users to conduct work in this space.

Ultimately, the output of the NGS pipeline needs to be integrated into the electronic health record and to be aggregated across a patient population. Robust informatics systems and trained bioinformaticians are critical new additions to the clinical team. Servant et al. (2014) covered this issue in detail. I agree with their findings. Here is the upshot.
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Cross-Validation for Genomic Prediction in SVS
by Bryce Christensen,
Director of Services and Statistical Geneticist


The SNP and Variation Suite (SVS) software currently supports three methods for genomic prediction: Genomic Best Linear Unbiased Predictors (GBLUP), Bayes C and Bayes C-pi. We have discussed these methods extensively in previous blogs and webcast events. Although there are extensive applications for these methods, they are primarily used for trait selection in agricultural genetics. Each method can be used to create models that predict phenotypic traits based on genotype data. The model is trained on samples for whom phenotypic data is available, and then used to estimate the same phenotype for samples with unknown phenotypes. But how can you determine if the model is really accurate?
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Meta-Analysis
Comparing Meta-Analysis Methods: A Meta Examination
by Greta Linse Peterson,
Director of Product Management and Quality


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 genetics + meta-analysis. There are several meta-analysis utilities out there that will take results from studies and perform the meta-analysis. Fingers crossed that you have all of the information you need and in a usable format!

Let's step back for a minute and talk about meta-analysis. What it is and why should you consider it?
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   Latest Webcast
Using VarSeq to Improve Variant Analysis Research Workflows
Many questions must be answered when analyzing DNA sequence variants: How do I determine which variants are potentially deleterious? Is the sequencing quality sufficient? How do I prioritize the results? Which annotation sources may help answer my research question?

This webinar reviews workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.

The presentation includes interactive demonstrations using VarSeq to analyze variants found by exome sequencing of an extended family with a complex disease. We will review strategies for assessing variant quality, applying genomic annotations, incorporating custom annotation sources, and creating variant filters in VarSeq. It also demonstrates the PhoRank gene ranking algorithm and its application for prioritizing variants.
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About Golden Helix
Golden Helix has been delivering industry leading bioinformatics solutions for the advancement of life science research and translational medicine for over 16 years. Our innovative technologies and analytic services empower scientists and healthcare professionals at all levels to derive meaning from the rapidly increasing volumes of genomic data produced from microarrays and next-generation sequencing. With our solutions, hundreds of the world's top pharmaceutical, biotech, and academic research organizations are able to harness the full potential of genomics to identify the cause of disease, improve the efficacy and safety of drugs, develop genomic diagnostics, and advance the quest for personalized medicine. Golden Helix products and services have been cited in over 850 peer-reviewed publications.