datastorm-open.github.io - visNetwork is an R package for network visualization, using vis.js javascript library (http://visjs.org/). All remarks and bugs are welcome on github : https://github.com/datastorm-open/visNetwork.
Features
Based...
github.com - PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and...
github.com - ClipCrop for detecting SVs with single-base resolution using soft-clipping information. A soft-clipped sequence is an unmatched fragment in a partially mapped read. To assess the performance of ClipCrop with other SV-detecting tools, we generated...
github.com - Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a...
github.com - igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)
jb2-web - stock...
github.com - SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li....
github.com - A probabilistic framework for structural variant discovery.
Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6):...
sourceforge.net - EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies...
github.com - MUM&Co is able to detect:Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)Inversions (>=1kb) and translocations (>=10kb)