HapTree: A Novel Bayesian Framework for Single Individual Polyplotyping Using NGS Data
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journal.pcbi.1003502.pdf
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Author(s)
Berger, Emily
Yorukoglu, Deniz
Peng, Jian
Berger, Bonnie
Date Issued
March 2014
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Berger, Emily, Deniz Yorukoglu, Jian Peng, and Bonnie Berger. “HapTree: A Novel Bayesian Framework for Single Individual Polyplotyping Using NGS Data.” Edited by Isidore Rigoutsos. PLoS Comput Biol 10, no. 3 (March 27, 2014): e1003502.
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Final published version
Abstract
As the more recent next-generation sequencing (NGS) technologies provide longer read sequences, the use of sequencing datasets for complete haplotype phasing is fast becoming a reality, allowing haplotype reconstruction of a single sequenced genome. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. In this paper, we describe a novel maximum-likelihood estimation framework, HapTree, for polyploid haplotype assembly of an individual genome using NGS read datasets. We evaluate the performance of HapTree on simulated polyploid sequencing read data modeled after Illumina sequencing technologies. For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003502
