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Bioinformatics. 2014 Sep 1;30(17):i379-85. doi: 10.1093/bioinformatics/btu484.

Probabilistic single-individual haplotyping.

Author information

1
Department of Computer Science, Stanford University, Stanford, CA 94305, USA.

Abstract

MOTIVATION:

Accurate haplotyping-determining from which parent particular portions of the genome are inherited-is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such technologies. The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction. In addition to being accurate, ProbHap also provides confidence scores at phased positions.

RESULTS:

On a standard benchmark dataset, ProbHap makes 11% fewer errors than current state-of-the-art methods. This accuracy can be further increased by excluding low-confidence positions, at the cost of a small drop in haplotype completeness.

AVAILABILITY:

Our source code is freely available at: https://github.com/kuleshov/ProbHap.

PMID:
25161223
PMCID:
PMC4147930
DOI:
10.1093/bioinformatics/btu484
[Indexed for MEDLINE]
Free PMC Article

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