At Bina, we employ various variant standardization methods to improve variant annotation performance. Previously we’ve shared how the Bina AAiM software applies variant normalization and its impact. In this post, we’ll show another method that we use.
Pancreatic Neuroendocrine Tumors (PNETs) are a rare but clinically important form of cancer. Because many PNETs grow silently and are therefore frequently diagnosed at a late stage, approximately 65% of patients present with unresectable or metastatic disease. As a result, these patients have a poor prognosis and a median survival time of 24 months with limited treatment options.
The Bina AAiM software is designed to provide deeper insight into variants identified by next-generation sequencing. After uploading a vcf file to the software, variants are annotated against a number of databases that provide information such as predicted pathogenicity, disease association, population frequency, and more. While at small scale, the operation is usually a simple lookup in a text file or a database, performing this annotation for large datasets and at scale is a challenge. Further, due to the variability in the way variants are represented across data sources and VCF files, finding all matching variants requires careful standardization of how variants are represented. Variant normalization and lift-over to a consistent reference genome are two standardization steps that the Bina AAiM software applies in order to find the maximum number of matching variants, which we will explore in this blog post and the next.
In the previous post, we shared 2 posters presented at ASHG 2015 on cancer sequencing analysis and tertiary annotation. Here are 3 more posters that were presented by the Bina team:
Welcome back from ASHG! The 2015 conference was bustling with activities from workshops, scientific sessions and exhibitions. In addition, the poster presentations drew huge crowds daily as well. The Bina team presented 5 posters in total, and here are two that we'd like to share in this post:
The American Society of Human Genetics is holding its Annual Meeting this year in Baltimore Maryland, from October 6 through October 10. Being the largest human genetics meeting and exposition worldwide, the agenda is packed with workshops, social events, platform talks and poster presentations.
Each fall, the National Institutes of Health holds a Research Festival on campus to promote intramural science and invites leading biomedical research suppliers to join their technology showcase. Schedule for this year’s plenary, workshop and poster sessions can be found here.
The use of next generation sequencing (NGS) data has grown rapidly in many areas of scientific and clinical research. Extracting biological meaning from the large numbers of variants identified in these studies, however, remains a significant challenge.
The Bina Annotation and Analytics Intelligence Module (Bina AAiM) is an efficient, scalable, and easy-to-use system for investigating and prioritizing variants from NGS analyses, along with extensive visualization capabilities to aid in interpretation.
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Advancements in next-gen sequencing have increased the speed and depth in which cancer genomes can be surveyed. However, extracting biological meaning from the large number of variants identified by the technology remains a significant informatics challenge.
The Bina Annotation, Analytics and Intelligence Module™ (Bina AAiM) is designed for rapid and scalable analysis of sequencing data from whole genomes, exomes, and targeted panels.
Next-Generation Sequencing (NGS) is changing the way pharmaceutical companies develop drugs, perform patient stratification, and evaluate treatment efficacy. However, implementing a sound NGS strategy and maintaining a robust bioinformatics pipeline can be time consuming and costly. More importantly, presenting the data and insights gained from genomic analysis in a way that is consumable by translational scientists is essential.