TY - JOUR T1 - PEMapper / PECaller: A simplified approach to whole-genome sequencing JF - bioRxiv DO - 10.1101/076968 SP - 076968 AU - H Richard Johnston AU - Pankaj Chopra AU - Thomas S Wingo AU - Viren Patel AU - Internation Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome AU - Michael P Epstein AU - Jennifer Mulle AU - Stephen T Warren AU - Michael E Zwick AU - David J. Cutler Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/09/22/076968.abstract N2 - 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 novel 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. ER -