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01908 am a22003253u 4500 |
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69888 |
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|a dc
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|a Williams, Amy L.
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a Massachusetts Institute of Technology. Department of Biology
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Koch Institute for Integrative Cancer Research at MIT
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|a Housman, David E.
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|a Housman, David E.
|e contributor
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|a Williams, Amy L.
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|a Rinard, Martin C.
|e contributor
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|a Gifford, David K.
|e contributor
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|a Housman, David E
|e author
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|a Rinard, Martin C
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|a Gifford, David K
|e author
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|a Rapid haplotype inference for nuclear families
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|b BioMed Central Ltd.,
|c 2012-03-28T21:06:55Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/69888
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|a Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances.
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|a National Institutes of Health (U.S.) (NIH grant 5-T90-DK070069)
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|a National Institutes of Health (U.S.) (Grant 5-P01-NS055923)
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|a National Science Foundation (U.S.) (Graduate Research Fellowship)
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|a en_US
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|a Article
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|t Genome Biology
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