Journal
MOLECULAR BIOLOGY AND EVOLUTION
Volume 32, Issue 11, Pages 3027-3029Publisher
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msv172
Keywords
selection; iHS; EHH; XP-EHH; software
Funding
- UK Biotechnology and Biological Sciences Research Council (BBSRC)
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/H043160/1]
- Biotechnology and Biological Sciences Research Council [BBS/E/D/20211551] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/H043160/1] Funding Source: researchfish
- BBSRC [BBS/E/D/20211551] Funding Source: UKRI
- EPSRC [EP/H043160/1] Funding Source: UKRI
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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.
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