No abstract

Rapid growth of sequencing technologies has greatly contributed to increasing our understanding of human genetics. Yet, in spite of this growth, mainstream technologies have been largely unsuccessful in resolving the diploid nature of the human genome. Here we describe statistically aided long read haplotyping (SLRH), a rapid, accurate method based on a simple experimental protocol that requires potentially as little as 30 Gbp of sequencing in addition to a standard (50x coverage) whole-genome analysis for human samples. Using this technology, we phase 99% of single-nucleotide variants in three human genomes into long haplotype blocks of 200 kbp to 1 Mbp in length. As a demonstration of the potential applications of our method, we determine allele-specific methylation patterns in a human genome and identify hundreds of differentially methylated regions that were previously unknown. Such information may offer insight into the mechanisms behind differential gene expression.

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequence remains a difficult problem. Here, we present an analysis of a human gut microbiome using on Tru-seq synthetic long reads combined with new computational tools for metagenomic long-read assembly, variant-calling and haplotyping (Nanoscope and Lens). Our analysis identifies 178 bacterial species of which 51 were not found using short sequence reads alone. We recover bacterial contigs that comprise multiple operons, including 22 contigs of >1Mbp. Extensive intraspecies variation among microbial strains in the form of haplotypes that span up to hundreds of Kbp can be observed using our approach. Our method incorporates synthetic long-read sequencing technology with standard shotgun approaches to move towards rapid, precise and comprehensive analyses of metagenome and microbiome samples.

Motivation: Accurate haplotyping—determining from which parent particular portions of the genome are inherited—is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such technologies. The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction. In addition to being accurate, ProbHap also provides confidence scores at phased positions.Results: On a standard benchmark dataset, ProbHap makes 11% fewer errors than current state-of-the-art methods. This accuracy can be further increased by excluding low-confidence positions, at the cost of a small drop in haplotype completeness.Availability: Our source code is freely available at: https://github.com/kuleshov/ProbHap.Contact: kuleshov@stanford.edu