Decoding Genetic Variations: Communications-Inspired Haplotype Assembly

摘要

High-throughput DNA sequencing technologies allow fast and affordable sequencing of individual genomes and thus enable unprecedented studies of genetic variations. Information about variations in the genome of an individual is provided by haplotypes, ordered collections of single nucleotide polymorphisms. Knowledge of haplotypes is instrumental in finding genes associated with diseases, drug development, and evolutionary studies. Haplotype assembly from high-throughput sequencing data is challenging due to errors and limited lengths of sequencing reads. The key observation made in this paper is that the minimum error-correction formulation of the haplotype assembly problem is identical to the task of deciphering a coded message received over a noisy channel—a classical problem in the mature field of communication theory. Exploiting this connection, we develop novel haplotype assembly schemes that rely on the bit-flipping and belief propagation algorithms often used in communication systems. The latter algorithm is then adapted to the haplotype assembly of polyploids. We demonstrate on both simulated and experimental data that the proposed algorithms compare favorably with state-of-the-art haplotype assembly methods in terms of accuracy, while being scalable and computationally efficient.