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Strand-seq enables reliable separation of long reads by chromosome via expectation maximization

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Item Type:Article
Title:Strand-seq enables reliable separation of long reads by chromosome via expectation maximization
Creators Name:Ghareghani, M. and Porubskỳ, D. and Sanders, A.D. and Meiers, S. and Eichler, E.E. and Korbel, J.O. and Marschall, T.
Abstract:MOTIVATION: Current sequencing technologies are able to produce reads orders of magnitude longer than ever possible before. Such long reads have sparked a new interest in de novo genome assembly, which removes reference biases inherent to re-sequencing approaches and allows for a direct characterization of complex genomic variants. However, even with latest algorithmic advances, assembling a mammalian genome from long error-prone reads incurs a significant computational burden and does not preclude occasional misassemblies. Both problems could potentially be mitigated if assembly could commence for each chromosome separately. RESULTS: To address this, we show how single-cell template strand sequencing (Strand-seq) data can be leveraged for this purpose. We introduce a novel latent variable model and a corresponding Expectation Maximization algorithm, termed SaaRclust, and demonstrates its ability to reliably cluster long reads by chromosome. For each long read, this approach produces a posterior probability distribution over all chromosomes of origin and read directionalities. In this way, it allows to assess the amount of uncertainty inherent to sparse Strand-seq data on the level of individual reads. Among the reads that our algorithm confidently assigns to a chromosome, we observed more than 99% correct assignments on a subset of Pacific Bioscience reads with 30.1× coverage. To our knowledge, SaaRclust is the first approach for the in silico separation of long reads by chromosome prior to assembly. AVAILABILITY AND IMPLEMENTATION: https://github.com/daewoooo/SaaRclust.
Keywords:Algorithms, Computer Simulation, DNA Sequence Analysis, Genomics, High-Throughput Nucleotide Sequencing, Human Chromosomes, Human Genome, Software
Publisher:Oxford University Press
Page Range:i115-i123
Date:1 July 2018
Official Publication:https://doi.org/10.1093/bioinformatics/bty290
PubMed:View item in PubMed

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