H. Richard JohnstonPankaj ChopraThomas S. WingoViren PatelMichael P. EpsteinJennifer G. MulleStephen T. WarrenMichael E. ZwickDavid J. CutlerBernice MorrowBeverly EmanuelDonna M. McDonald-McGinnSteve SchererAnne BassettEva ChowJoris VermeeschAnn SwillenRaquel GurCarrie BeardenWendy KatesVandana ShashiTony SimonPankj ChopraJoseph CubellsDavid J. CutlerMichael P. EpsteinH. Richard JohnstonJennifer MulleViren PatelStephen T. WarrenThomas S. WingoMichael E. ZwickLinda CampbellREPETTO LISBOA, MARIA GABRIELAMARIA GABRIELAREPETTO LISBOAJacob VorstmanTherese Van AmelsvoortStephen EliezNicole PhilipDoron GothelfMarianne Van Den BreeMichael OwenClodagh MurphyDeclan MurphySixto Garcia-MinaurDamian Neine-SunerKieran MurphyMarco ArmandoStefano Vicari2024-04-012024-04-012017http://hdl.handle.net/11447/1929https://investigadores.udd.cl/handle/123456789/890310.1073/pnas.1618065114WOS:000395511400019<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>Resource Types::text::journal::journal article