Application of a MAX-CUT Heuristic to the Contig Orientation Problem in Genome Assembly

Bodily, Paul; Clement, Mark J.; Snell, Quinn; Price, Joseph; Fujimoto, Stanley; Okuda, Nozomu

doi:10.1145/2506583.2506606

Cited by 1 publication

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References 29 publications

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“…First, to simplify the problem, we will assume that there are no large-scale rearrangements between haplotypes. Although such rearrangements happen and several can be algorithmically identified [ 29 ], our proposed solution assumes that variation between haplotypes is small-scale. Essentially this allows us to assume that heterozygous sequence that is sequentially similar will be reconstructed in the same orientation at the same position on opposite haplotypes.…”

Section: Methodsmentioning

confidence: 99%

Heterozygous genome assembly via binary classification of homologous sequence

et al. 2015

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BackgroundGenome assemblers to date have predominantly targeted haploid reference reconstruction from homozygous data. When applied to diploid genome assembly, these assemblers perform poorly, owing to the violation of assumptions during both the contigging and scaffolding phases. Effective tools to overcome these problems are in growing demand. Increasing parameter stringency during contigging is an effective solution to obtaining haplotype-specific contigs; however, effective algorithms for scaffolding such contigs are lacking.MethodsWe present a stand-alone scaffolding algorithm, ScaffoldScaffolder, designed specifically for scaffolding diploid genomes. The algorithm identifies homologous sequences as found in "bubble" structures in scaffold graphs. Machine learning classification is used to then classify sequences in partial bubbles as homologous or non-homologous sequences prior to reconstructing haplotype-specific scaffolds. We define four new metrics for assessing diploid scaffolding accuracy: contig sequencing depth, contig homogeneity, phase group homogeneity, and heterogeneity between phase groups.ResultsWe demonstrate the viability of using bubbles to identify heterozygous homologous contigs, which we term homolotigs. We show that machine learning classification trained on these homolotig pairs can be used effectively for identifying homologous sequences elsewhere in the data with high precision (assuming error-free reads).ConclusionMore work is required to comparatively analyze this approach on real data with various parameters and classifiers against other diploid genome assembly methods. However, the initial results of ScaffoldScaffolder supply validity to the idea of employing machine learning in the difficult task of diploid genome assembly. Software is available at http://bioresearch.byu.edu/scaffoldscaffolder.

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Section: Methodsmentioning

confidence: 99%