Browsing by Author "Mulle, Jennifer"

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    PEMapper and PECaller provide a simplified approach to whole-genome sequencing
    (National Academy of Sciences, 2017) Johnston, Richard; Chopra, Pankaj; Wingo, Thomas; Patel, Viren; Epstein, Michael; Mulle, Jennifer; Warren, Stephen; Zwick, Michael; Cutler, David; International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome
    The analysis of human whole-genome sequencing data presents significant computational challenges. The sheer size of datasets places an enormous burden on computational, disk array, and network resources. Here, we present an integrated computational package, PEMapper/PECaller, that was designed specifically to minimize the burden on networks and disk arrays, create output files that are minimal in size, and run in a highly computationally efficient way, with the single goal of enabling whole-genome sequencing at scale. In addition to improved computational efficiency, we implement a statistical framework that allows for a base by base error model, allowing this package to perform as well or better than the widely used Genome Analysis Toolkit (GATK) in all key measures of performance on human whole-genome sequences.

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