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bioRxiv · 10.1101/076968

PEMapper / PECaller: A simplified approach to whole-genome sequencing

Henry Richard Johnston·Pankaj Chopra·Thomas Wingo·Viren Patel·Internation Consortium on Brain and Behavior in 22·Michael Epstein·Jennifer Mulle·Stephen Warren·Michael Zwick·David J Cutler
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Abstract

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.

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Henry Richard Johnston, Pankaj Chopra, Thomas Wingo, Viren Patel, Internation Consortium on Brain and Behavior in 22, Michael Epstein, Jennifer Mulle, Stephen Warren, Michael Zwick, David J Cutler. 2016-09-22. PEMapper / PECaller: A simplified approach to whole-genome sequencing. https://doi.org/10.1101/076968

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