The National Human Genome Research Institute has awarded about $4 million to six research teams to develop bioinformatics software for analyzing sequence data.
The recipients of the funds, which are funded through cooperative agreements, are part of NHGRI's newly established iSeqTools network.
NHGRI said the network was established to enable independent researchers outside the major genome centers to use sequence data analysis tools; democratize access to genome informatics tools; reduce the cost and the skills required for genome analysis; share knowledge and utilize synergies found within the network; and develop metrics for evaluating the positive impact of software.
The recipients and their respective awards are as follows
- Boston College and University of Michigan, Ann Arbor
Principal Investigators: Gabor Marth and Gonçalo Abecasis
$1 million to produce robust software tools and workflows for variant identification and functional assessment
- University of Southern California, Los Angeles
Principal Investigators: Ting Chen, Ewa Deelman, and James Knowles
$345,000 to produce robust and portable workflow-based tools for mRNA and genome resequencing
- The Broad Institute, Cambridge, Mass.
Principal Investigator: Mark DePristo
$1 million to develop the Genome Analysis Toolkit for high-throughput sequence data analysis
- Washington University in St. Louis
Principal Investigators: Li Ding and David Dooling
$805,000 to produce robust toolkits and the GeMS turnkey computational framework for high-throughput variant discovery and interpretation
- Harvard Medical School, Boston
Principal Investigator: Steven McCarroll
$448,000 to produce accurate genome structural variation analysis with Genome STRiP using large-scale sequence data
- Scripps Translational Science Institute, La Jolla, Calif.
Principal Investigator: Ali Torkamani
$382,000 to develop the Scripps Genome ADVISER: annotation and distributed variant interpretation server
According to NHGRI, it plans to invest nearly $20 million over the next four years to make existing computational tools more generally accessible and to speed up the ability of investigators to analyze genome sequence data.