Department of Bioinformatics and Computational Biology

TIGRA

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TIGRA

TIGRA: A Targeted Iterative Graph Routing Assembler for Breakpoint Assembly
Overview
Description TIGRA: A Targeted Iterative Graph Routing Assembler for Breakpoint Assembly
Development Information
Language C++
Current Version 0.3.7
License GPL v3
Status Active
Last Updated 2013/06/29
References
Citations Chen K, Chen L, Fan X, Wallis J, Ding L, Weinstock G. TIGRA: A Targeted Iterative Graph Routing Assembler for breakpoint assembly. Genome Res. e-Pub 12/4/2013. PMID: 24307552.
News N.A.
Help and Support
Contact Ken Chen
Discussion Project Forum



Tigra is a computer program that conducts targeted local assembly of structural variants (SV) using targeted iterative graph routing assembly (TIGRA) algorithm. It takes as input a list of putative SV calls and a set of bam files that contain reads mapped to a reference genome such as NCBI build36. For each SV call, it assembles the set of reads that were mapped or partially mapped to the region of interest (ROI) in the corresponding bam files. Instead of outputing a single consensus sequence, tigra_sv attempts to construct all the alternative alleles in the ROI as long as they received sufficient sequence coverage (usually >= 2x). It also utilizes the variant type information in the input files to select reads for assembly. Tigra is effective at improving the SV prediction accuracy and resolution in short reads analysis and can produce accurate breakpoint sequences that are useful to understand the origin, mechanism and pathology underlying the SVs.
Decode SV breakpoints from a TIGRA contig. Use the TIGRA-ext pipeline at https://bitbucket.org/xianfan/tigra-ext

Download

On a unix shell, use tar -zxf to untar the following packages.

Most updated version: available at bitbucket
Source code (39 Kb)
* README
* tigra-0.4.0
Support fetching of mates from outside the window, compatible with recent samtools (e.g. 0.1.19)


* tigra-0.3.7


Test data
* In BreakDancer format (46.2Mb)
* In the 1000 Genomes format (33.7Mb)