Abstract
BackgroundHaplotype assembly is the task of reconstructing haplotypes of an individual from a mixture of sequenced chromosome fragments. Haplotype information enables studies of the effects of genetic variations on an organism’s phenotype. Most of the mathematical formulations of haplotype assembly are known to be NP-hard and haplotype assembly becomes even more challenging as the sequencing technology advances and the length of the paired-end reads and inserts increases. Assembly of haplotypes polyploid organisms is considerably more difficult than in the case of diploids. Hence, scalable and accurate schemes with provable performance are desired for haplotype assembly of both diploid and polyploid organisms.ResultsWe propose a framework that formulates haplotype assembly from sequencing data as a sparse tensor decomposition. We cast the problem as that of decomposing a tensor having special structural constraints and missing a large fraction of its entries into a product of two factors, U and underline {mathbf {V}}; tensor underline {mathbf {V}} reveals haplotype information while U is a sparse matrix encoding the origin of erroneous sequencing reads. An algorithm, AltHap, which reconstructs haplotypes of either diploid or polyploid organisms by iteratively solving this decomposition problem is proposed. The performance and convergence properties of AltHap are theoretically analyzed and, in doing so, guarantees on the achievable minimum error correction scores and correct phasing rate are established. The developed framework is applicable to diploid, biallelic and polyallelic polyploid species. The code for AltHap is freely available from https://github.com/realabolfazl/AltHap.ConclusionAltHap was tested in a number of different scenarios and was shown to compare favorably to state-of-the-art methods in applications to haplotype assembly of diploids, and significantly outperforms existing techniques when applied to haplotype assembly of polyploids.
Highlights
Haplotype assembly is the task of reconstructing haplotypes of an individual from a mixture of sequenced chromosome fragments
We propose a unified framework for haplotype assembly of diploid and polyploid genomes based on sparse tensor decomposition; the framework essentially solves a relaxed version of the NP-hard minimum error correction (MEC) formulation of the haplotype assembly problem
The benchmarking algorithms include Belief Propagation (BP) [27], a communication-inspired method capable of performing haplotype assembly of diploid and biallelic polyploid species, HapTree [30], integer linear programming (ILP) technique [15], SCGD [32], and H-PoP [8], the state-of-the-art dynamic programming algorithm for haplotype assembly of diploid and biallelic polyploid species shown to be superior to HapTree [30], HapCompass [24], and SDhaP [26] in terms of both accuracy and speed [8, 31]
Summary
Haplotype assembly is the task of reconstructing haplotypes of an individual from a mixture of sequenced chromosome fragments. This leads to ambiguities regarding the origin of a read and renders haplotype assembly challenging. Authors in [9, 11] aim to process long reads by developing algorithms for the exact optimization of weighted variants of the MEC score that scale well with read length but are exponential in the sequencing coverage. These methods, along with ProbHap [10], struggle to remain accurate and practically feasible at high coverages (e.g., higher than 12 [10])
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
