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A graph-based approach to diploid genome assembly

Authors: Shilpa Garg; Mikko Rautiainen; Adam M. Novak; Erik Garrison; Richard Durbin; Tobias Marschall;

A graph-based approach to diploid genome assembly

Abstract

Abstract Motivation Constructing high-quality haplotype-resolved de novo assemblies of diploid genomes is important for revealing the full extent of structural variation and its role in health and disease. Current assembly approaches often collapse the two sequences into one haploid consensus sequence and, therefore, fail to capture the diploid nature of the organism under study. Thus, building an assembler capable of producing accurate and complete diploid assemblies, while being resource-efficient with respect to sequencing costs, is a key challenge to be addressed by the bioinformatics community. Results We present a novel graph-based approach to diploid assembly, which combines accurate Illumina data and long-read Pacific Biosciences (PacBio) data. We demonstrate the effectiveness of our method on a pseudo-diploid yeast genome and show that we require as little as 50× coverage Illumina data and 10× PacBio data to generate accurate and complete assemblies. Additionally, we show that our approach has the ability to detect and phase structural variants. Availability and implementation https://github.com/whatshap/whatshap Supplementary information Supplementary data are available at Bioinformatics online.

Keywords

Ismb 2018–Intelligent Systems for Molecular Biology Proceedings, Haplotypes, Data Visualization, Yeasts, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, Genome, Fungal, Diploidy

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
57
Top 10%
Top 10%
Top 1%
Green
gold