Ping Liang

French

Ping Liang has created and made freely available software (pBWA) that can speed up large-scale analysis of personal genome data, which will lead to a better understanding of human genetics and diseases.
http://scitechnol.com/speeding-up-largescale-next-generation-sequencing-data-analysis-with-pbwa-ltkv.php?article_id=441%3Farticle_id=441

Dr. Ping Liang’s research focuses on characterization and documentation of genetic variations in humans. Among all genomic variant types, structural variation has gained much attention in recent years due to its newly recognized prominence of occurrence and its further appreciated significance in genetics and diseases. However, detection and characterization represent a major challenge in the field. Current methods focus on analysis of individual genomes and lack the accuracy and robustness to provide details required for accessing the functional impact of the variants. Dr. Liang’s group is working on developing a novel robust systematic approach to solving these bottlenecks by first collectively analyzing all human-genome sequence data, which is now available at a magnitude of thousands covering all major human populations, followed by compiling the detailed sequences for all known structural variations. The availability of such comprehensive structural variation sequences will greatly facilitate the analysis of personal genome data, which will ultimately help to better understand human genetics and diseases. Building from research done using resources from Compute Canada’s 2014 resource allocation competition, we have retrieved and processed the whole genome sequence data for more than 3,000 individuals or a total of more than 100 trillion base pairs of raw sequence data, and will be able to move to detailed variant characterization and development of reference databases and tools with resources from Compute Canada’s 2015 resource allocation competition. This research group is also collaborating with several leading research groups in the U.S. and Canada to perform high-throughput genomic analysis of cancer genomes and plant genomes.

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