Photo by nci from unsplash
Motivation: Structural variation (SV) detection from short‐read whole genome sequencing is error prone, presenting significant challenges for population or family‐based studies of disease. Results: Here, we describe SV2, a machine‐learning… Click to show full abstract
Motivation: Structural variation (SV) detection from short‐read whole genome sequencing is error prone, presenting significant challenges for population or family‐based studies of disease. Results: Here, we describe SV2, a machine‐learning algorithm for genotyping deletions and duplications from paired‐end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. Availability and implementation: SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Journal Title: Bioinformatics
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!