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Abstract / Description of output
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 multi-threaded 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.
Keywords / Materials (for Non-textual outputs)
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hapbin: An efficient program for performing haplotype based scans for positive selection in large genomic datasets