title: hapbin: An Efficient Program for Performing Haplotype-Based Scans for Positive Selection in Large Genomic Datasets.
abstract: Understanding how the genome is shaped by selective processes forms an integral part of modern biology. However, as genomic datasets continue to grow larger it is becoming increasingly difficult to apply traditional statistics for detecting signatures of selection to these cohorts. There is therefore a pressing need for the development of the next generation of computational and analytical tools for detecting signatures of selection in large genomic datasets. Here, we present hapbin, an efficient multithreaded implementation of extended haplotype homzygosity-based statistics for detecting selection, which is up to 3,400 times faster than the current fastest implementations of these algorithms.
authors.affiliation: EPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
authors.forename: Colin A
authors.initials: CA
authors.lastname: Maclean
authors.affiliation: EPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
authors.forename: Neil P
authors.initials: NP
authors.lastname: Chue Hong
authors.affiliation: Roslin Institute, University of Edinburgh, Scotland, United Kingdom prenderj@gmail.com.
authors.forename: James G D
authors.initials: JG
authors.lastname: Prendergast
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D005828:Genetics, Population / Q000379:methods; D016678:Genome; D023281:Genomics / Q000379:methods*; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic; D012984:Software
keywords: EHH; XP-EHH; iHS; selection; software