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PEMapper and PECaller provide a simplified approach to whole-genome sequencing
Journal
Proceedings of the National Academy of Sciences
ISSN
0027-8424
1091-6490
Date Issued
2017
Author(s)
H. Richard Johnston
Pankaj Chopra
Thomas S. Wingo
Viren Patel
Michael P. Epstein
Jennifer G. Mulle
Stephen T. Warren
Michael E. Zwick
David J. Cutler
Bernice Morrow
Beverly Emanuel
Donna M. McDonald-McGinn
Steve Scherer
Anne Bassett
Eva Chow
Joris Vermeesch
Ann Swillen
Raquel Gur
Carrie Bearden
Wendy Kates
Vandana Shashi
Tony Simon
Pankj Chopra
Joseph Cubells
David J. Cutler
Michael P. Epstein
H. Richard Johnston
Jennifer Mulle
Viren Patel
Stephen T. Warren
Thomas S. Wingo
Michael E. Zwick
Linda Campbell
Jacob Vorstman
Therese Van Amelsvoort
Stephen Eliez
Nicole Philip
Doron Gothelf
Marianne Van Den Bree
Michael Owen
Clodagh Murphy
Declan Murphy
Sixto Garcia-Minaur
Damian Neine-Suner
Kieran Murphy
Marco Armando
Stefano Vicari
Type
Resource Types::text::journal::journal article
Abstract
<jats:title>Significance</jats:title>
<jats:p>PEMapper and PECaller are paired software programs that simplify mapping and variant calling for whole-genome datasets. Whole-genome sequencing data are fast becoming the most natural dataset for all genetic studies. Analysis tools for data at this scale are essential. This manuscript describes tools, which solve the challenges of data analysis at whole-genome scale, using an approach involving 16-mer mapping and SNP calling based on a Pólya–Eggenberger distribution for SNP genotypes. We show that our software package is faster (cheaper to run), uses much less disk space (cheaper to store results), requires no previous knowledge of existing genetic variation (easier to deploy to nonhuman species), and achieves calling results that are as good as Genome Analysis Toolkit best practices.</jats:p>
