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