SVDetect : a tool to detect genomic structural variations from paired-end and mate-pair sequencing data

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
Higher Edu - Research dev card
  • Creation or important update: 18/04/13
  • Minor correction: 18/04/13
  • Index card author: Bruno Zeitouni (Institut Curie)
  • Theme leader: Christelle Dantec (CRBM)
Keywords
General software features

From NGS paired sequences and mapped onto a reference genome, SVDetect allows you to detect clusters of anomanously mapped pairs (with abnormal order, strand orientation or insert size of fragments), and to predict structural variants (SVs) such as large insertions, deletions, inversions, duplications or intra/inter-chromosomal translocations. SVDetect can also compare the results of SVs from different samples and to identify specific-sample SVs (Tumoral DNA vs Control DNA, for example).
SVDetect is compatible with any type of paired reads ("paired-end" or "mate-pair"), sequencing technology (Illumina, SOLiD, PGM, ...), or type of genome.
SVDetect can compute coverage profiles and to reveal loss or gains of genomic regions from the copy-number information.
It is available into a PERL Script and takes the BAM format as input.
SVDetect is also available at the Galaxy toolshed.

Context in which the software is used

SVDetect is an application for the isolation and the type prediction of intra- and inter-chromosomal rearrangements from paired-end/mate-pair sequencing data provided by the high-throughput sequencing technologies.
It was primarily tested in the context of whole genome resequencing projects from cancer cells, rich in chromosomal rearrangements.
SVDetect can also detect fusion genes from RNA-seq experiments.

Publications related to software
  • SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data
    Bruno Zeitouni; Valentina Boeva; Isabelle Janoueix-Lerosey; Sophie Loeillet; Patricia Legoix-ne; Alain Nicolas; Olivier Delattre; Emmanuel Barillot, Bioinformatics 2010 26: 1895-1896, http://www.hal.inserm.fr/inserm-00508372