The Bina Bioinformatics team is excited to tell you about our latest research in genetic variation analysis at ISMB 2016 this upcoming weekend in Orlando, Florida. Our Bioinformatics Scientist, Mohammad Sahraeian, will be presenting several significant enhancements made to a previously published, integrative structural variation (SV) caller, MetaSV. Recent enhancements have shown improvements in accuracy and speed at detecting more structural variations types, such as insertions, deletions, inversions and duplications, over the original tool. To learn more about this work, be sure to bookmark and check out the following poster presentation on Sunday afternoon:
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.
The much anticipated AACR annual meeting aims to bring together basic, translational and clinical researchers in the field of cancer. To take place April 16-20 in New Orleans, Roche Diagnostics welcomes you to discover an array of cutting-edge technologies and to share your innovations in cancer research with us at booth #922.
Bina is proud to be sponsoring a free luncheon presentation next week with the Stanford Center for Genomics and Personalized Medicine (SCGPM), where we will present our latest scientific advancements in next-generation sequencing analysis. Register and come hungry!
TIME AND PLACE:
Tuesday, April 19, 2016 from 12:00 PM to 1:00 PM. James H. Clark Center Room S360
In the previous series of posts, I’ve highlighted the clinical value gained from the first molecular diagnosis of a disease (part 1) and the increasing value as more cases come to light (part 2, part 3). But parents of rare disease sufferers have also found great emotional value in discovering their child, a rare disease sufferer, is not alone.
The recurring theme for many rare disease sufferers and their families in the past has been one of a diagnostic odyssey. It’s difficult to imagine the parents’ anguish upon discovering their baby’s potentially severe condition, and then add to that the continual testing, a multitude of referrals from one specialist to the next, mounting medical bills, and a string of changing diagnoses each with their requisite treatment. One can only have deep empathy and admiration for what the family and the affected child endure, and the sacrifices they make.
In the previous post, I shared the riveting case of Bertrand Might, as the first known sufferer from a disease due to mutations in the NGLY1 gene, and how his father’s blog helped a researcher at Baylor, Matthew Bainbridge, realize that NGLY1 mutations were giving rise to his patient’s condition as well [1,2].
When I listened to a series of talks from those running clinical exome labs at the Next Generation Diagnostics Summit last year, one of my takeaways was that the information required to identify the causal variant among multiple candidate variants was sometimes fortuitously discovered. Avni Santani of the Children’s Hospital of Philadelphia spoke about how in one patient’s case, the causative gene was identified by a Google search linking to a Facebook page that had mentioned the gene, several search pages later. It turns out the father of another patient some distance away had posted the gene name likely to underlie his child’s rare disease, asking if anyone could help illuminate what this might mean. These two patients happened to have very similar phenotypes and thus, Santani’s patient turned out to have a more definitive exome interpretation as a consequence.
A high-confidence, comprehensive human variant set is critical in assessing the accuracy of sequencing algorithms, which are crucial for precision-based medicine and clinical diagnostics. Although recent research has attempted to provide such a resource, it still does not encompass all major types of variants, in particular structural variants (SVs) that have been linked to debilitating diseases.
Blue Collar Bioinformatics (bcbio) is an open source community-developed Python toolkit for performing various secondary analyses while adhering to best practices for each analysis. The bcbio toolkit has gained a lot of traction within the bioinformatics community since it enables researchers to easily run many of the popular analysis workflows using a Python interface. Brad Chapman, who is one of the main contributors to bcbio, has kindly added support for MetaSV  on bcbio to allow MetaSV be run as part of the bcbio analysis. In this process, MetaSV has been enhanced to support four additional popular structural-variant (SV) callers, namely, CNVkit , LUMPY , Manta  and WHAM. Since MetaSV was designed to be extensible, support for additional SV callers was easy to incorporate.