Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids

Hashemi, Abolfazl; Zhu, Banghua; Vikalo, Haris

doi:10.1145/3107411.3110417

Cited by 11 publications

(42 citation statements)

References 40 publications

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“…where S k is the set of all permutations of {1, • • • , k}. Note that this is a more demanding notion of recovery compared to that used in [17] and [24]; the metric used there, so-called Reconstruction Rate, we for convenience denote by M-CPR (abbreviating "Modified CPR"). This metric is defined as…”

Section: Recovery Goals and Performance Metricsmentioning

confidence: 99%

“…A number of existing haplotype assembly schemes such as [14,17], attempt to directly minimize the MEC score by solving relaxations of the underlying combinatorial optimization problem that is known to be NP-hard [4,7]. Contrary to this common approach, in this paper we do not attempt to directly minimize the MEC but rather leverage inherent structural properties of the data and the fact that the noise in measurements is introduced randomly, rather than by an adversarial source, to design a randomized assembly algorithm.…”

Section: Recovery Goals and Performance Metricsmentioning

confidence: 99%

“…The clean-up method, at first glance, seems to bear similarities to other dynamical algorithms such as expectation maximization, Belief Propagation (BP) and tensor factorization based methods of [17]. Unlike BP, however, we do not iterate the beliefs or probabilities of a node belonging to various communities; instead, we make a hard assignment at each update step.…”

Section: Local Clusteringmentioning

confidence: 99%

“…To address both the diploid and polyploid scenarios, SDhaP [16] relaxes the MEC minimization problem to a convex optimization program and solves it efficiently by exploiting the underlying data structure. More recently, AltHap [17] casts haplotype assembly as a low-rank tensor factorization problem and solves it via fast alternating optimization heuristics. The connection to matrix factorization was previously exploited in [18] and [19], but those methods were incapable of handling polyploids or polyallelic assembly problem.…”

Section: Introductionmentioning

confidence: 99%

“…The connection to matrix factorization was previously exploited in [18] and [19], but those methods were incapable of handling polyploids or polyallelic assembly problem. Prior to the current paper, AltHap [17] is the only algorithm capable of solving the assembly problem in the polyploids/polyallelic scenario.…”

Section: Introductionmentioning

confidence: 99%

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ComHapDet: a spatial community detection algorithm for haplotype assembly

2020

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Background Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual’s susceptibility to hereditary and complex diseases and affect how our bodies respond to therapeutic drugs. Reconstructing haplotypes of an individual from short sequencing reads is an NP-hard problem that becomes even more challenging in the case of polyploids. While increasing lengths of sequencing reads and insert sizes helps improve accuracy of reconstruction, it also exacerbates computational complexity of the haplotype assembly task. This has motivated the pursuit of algorithmic frameworks capable of accurate yet efficient assembly of haplotypes from high-throughput sequencing data. Results We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph. To this end, we construct a graph where each read is a node with an unknown community label associating the read with the haplotype it samples. Haplotype reconstruction can then be thought of as a two-step procedure: first, one recovers the community labels on the nodes (i.e., the reads), and then uses the estimated labels to assemble the haplotypes. Based on this observation, we propose – a novel assembly algorithm for diploid and ployploid haplotypes which allows both bialleleic and multi-allelic variants. Conclusions Performance of the proposed algorithm is benchmarked on simulated as well as experimental data obtained by sequencing Chromosome 5 of tetraploid biallelic Solanum-Tuberosum (Potato). The results demonstrate the efficacy of the proposed method and that it compares favorably with the existing techniques.

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Section: Recovery Goals and Performance Metricsmentioning

confidence: 99%

Section: Recovery Goals and Performance Metricsmentioning

confidence: 99%

Section: Local Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ComHapDet: a spatial community detection algorithm for haplotype assembly

2020

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flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning

Shaw

2022

Journal of Computational Biology

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Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition problem, which is a more flexible graphical metric compared with the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp . We show that flopp compares favorably with state-of-the-art algorithms—up to 30 times faster with 2 times fewer switch errors on 6 × ploidy simulated data. Further, we show using real nanopore data that flopp can quickly reveal reasonable haplotype structures from the autotetraploid Solanum tuberosum (potato).

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A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Vikalo

2020

Preprint

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Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods.

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Sparse Tensor Decomposition for Haplotype Assembly of Diploids and Polyploids

Cited by 11 publications

References 40 publications

ComHapDet: a spatial community detection algorithm for haplotype assembly

ComHapDet: a spatial community detection algorithm for haplotype assembly

flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

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