BOL

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested...

majiq.biociphers.org -   Ability to detect, quantify, and visualize complex and de-novo splicing variations from RNASeq. MAJIQ’s accuracy compares favorably to other algorithms. MAJIQ 2 is *way* faster, more memory and I/O efficient New visualization (VOILA...

github.com - Flye is a de novo assembler for long and noisy reads, such as those produced by PacBio and Oxford Nanopore Technologies. The algorithm uses an A-Bruijn graph to find the overlaps between reads and does not require them to be error-corrected. After...

github.com - Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The...

Share and watch your favourite bioinformatics training, lecture, tutorial video on BOL

www.codeschool.com - Collections of Ruby and BioRuby learning materials. BioRuby paper link : http://bioinformatics.oxfordjournals.org/content/26/20/2617.abstract

en.wikibooks.org - Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream. This bookmark is created to provide NGS online books links.

An R tutorial for Microsoft Excel users

Part of the reason R has become so popular is the vast array of packages available at the cran and bioconductor repositories. In the last few years, the number of packages has grown exponentially! This is a short post giving steps on how to...

www.r2d3.us - In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. More at http://www.r2d3.us/visual-intro-to-machine-learning-part-1/