Journal Article

BAUM: improving genome assembly by adaptive unique mapping and local overlap-layout-consensus approach

Anqi Wang, Zhanyu Wang, Zheng Li and Lei M Li

Edited by Bonnie Berger

in Bioinformatics

Volume 34, issue 12, pages 2019-2028
Published in print June 2018 | ISSN: 1367-4803
Published online January 2018 | e-ISSN: 1460-2059 | DOI:
BAUM: improving genome assembly by adaptive unique mapping and local overlap-layout-consensus approach

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It is highly desirable to assemble genomes of high continuity and consistency at low cost. The current bottleneck of draft genome continuity using the second generation sequencing (SGS) reads is primarily caused by uncertainty among repetitive sequences. Even though the single-molecule real-time sequencing technology is very promising to overcome the uncertainty issue, its relatively high cost and error rate add burden on budget or computation. Many long-read assemblers take the overlap-layout-consensus (OLC) paradigm, which is less sensitive to sequencing errors, heterozygosity and variability of coverage. However, current assemblers of SGS data do not sufficiently take advantage of the OLC approach.


Aiming at minimizing uncertainty, the proposed method BAUM, breaks the whole genome into regions by adaptive unique mapping; then the local OLC is used to assemble each region in parallel. BAUM can (i) perform reference-assisted assembly based on the genome of a close species (ii) or improve the results of existing assemblies that are obtained based on short or long sequencing reads. The tests on two eukaryote genomes, a wild rice Oryza longistaminata and a parrot Melopsittacus undulatus, show that BAUM achieved substantial improvement on genome size and continuity. Besides, BAUM reconstructed a considerable amount of repetitive regions that failed to be assembled by existing short read assemblers. We also propose statistical approaches to control the uncertainty in different steps of BAUM.

Availability and implementation

Supplementary information

Supplementary data are available at Bioinformatics online.

Journal Article.  8384 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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