At the beginning of the year, we launched the Bina Science section on our website to showcase open source algorithms and peer reviewed journal articles that our team has produced. We are excited to announce the latest addition to our website, the Bina Library. We invite you to explore white papers and poster presentations, watch webinars and case study videos, and learn more about how our products can help you advance genomic discoveries. Simply choose the media type or topic at the top of the page to get started with discovering what we have in our library.
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.
We are excited to introduce VarSim, a comprehensive tool for the validation of secondary analysis in high throughput sequencing. It assesses both alignment and variant calling accuracy through a simulation that is based on real experimental data, and is capable of handling a wide range of variants, including single nucleotide variants, small indels and large structural variants.
Currently, there are a host of secondary analysis pipelines for analyzing next-gen sequencing data. However, due to the lack of ground truth, validating and comparing the accuracy of these pipelines are challenging. There have been significant efforts towards developing approaches to accuracy validation, which range from simulating the NGS data to actually validating the results using orthogonal technologies. We, at Bina, make every effort to ensure that the accuracy of the analysis is not compromised.
Detecting somatic mutations is a key challenge in cancer research, due to the impurity and heterogeneity of the samples from most cancer studies. This renders each data set as unique problem requiring different and oftentimes a combination of strategies. One mutation detection algorithm may work well for one data set but poorly for another. Bina’s cancer pipeline employs an integrative approach to identify and rank the most clinically important mutations based on a combination of different algorithms, sequencing features, and prior knowledge.