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Understanding genetic variation, e.g., through mutations, in organisms iscrucial to unravel their effects on the environment and human health. Afundamental characterization can be obtained by solving the haplotype assemblyproblem, which yields the variation across multiple copies of chromosomes.Variations among fast evolving viruses that lead to different strains (calledquasispecies) are also deciphered with similar approaches. In both these cases,high-throughput sequencing technologies that provide oversampled mixtures oflarge noisy fragments (reads) of genomes, are used to infer constituentcomponents (haplotypes or quasispecies). The problem is harder for polyploidspecies where there are more than two copies of chromosomes. State-of-the-artneural approaches to solve this NP-hard problem do not adequately modelrelations among the reads that are important for deconvolving the input signal.We address this problem by developing a new method, called NeurHap, thatcombines graph representation learning with combinatorial optimization. Ourexperiments demonstrate substantially better performance of NeurHap in real andsynthetic datasets compared to competing approaches.

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction