Evidence map›Paper›PMID 30180801›Full record

ArticleBMC bioinformatics2018

BrownieAligner: accurate alignment of Illumina sequencing data to de Bruijn graphs.

Mahdi Heydari, Giles Miclotte, Yves Van de Peer, Jan Fostier

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Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
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  10. Pangenome Graphs.Annual review of genomics and human genetics · 2020
    Review
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mahdi HeydariDepartment of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.
Giles MiclotteDepartment of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium.
Yves Van de PeerBioinformatics Institute Ghent, Ghent, B-9052, Belgium.
Jan FostierDepartment of Information Technology, Ghent University-imec, IDLab, Ghent, B-9052, Belgium. jan.fostier@ugent.be.ORCID http://orcid.org/0000-0002-9994-8269

Funding

The Research Foundation - Flanders (FWO) G0C3914N
6 · The paper itself

Abstract

backgroundAligning short reads to a reference genome is an important task in many genome analysis pipelines. This task is computationally more complex when the reference genome is provided in the form of a de Bruijn graph instead of a linear sequence string.

resultsWe present a branch and bound alignment algorithm that uses the seed-and-extend paradigm to accurately align short Illumina reads to a graph. Given a seed, the algorithm greedily explores all branches of the tree until the optimal alignment path is found. To reduce the search space we compute upper bounds to the alignment score for each branch and discard the branch if it cannot improve the best solution found so far. Additionally, by using a two-pass alignment strategy and a higher-order Markov model, paths in the de Bruijn graph that do not represent a subsequence in the original reference genome are discarded from the search procedure.

conclusionsBrownieAligner is applied to both synthetic and real datasets. It generally outperforms other state-of-the-art tools in terms of accuracy, while having similar runtime and memory requirements. Our results show that using the higher-order Markov model in BrownieAligner improves the accuracy, while the branch and bound algorithm reduces runtime. BrownieAligner is written in standard C++11 and released under GPL license. BrownieAligner relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at: https://github.com/biointec/browniealigner.

Indexed as

AlgorithmsComputer GraphicsGenome, HumanComputational BiologyHigh-Throughput Nucleotide SequencingHumansProgramming LanguagesSequence Analysis, DNAde Bruijn GraphGraph alignmentIlluminaMarkov ModelNext-generation sequencing

Identifiers

PMID30180801
PMCPMC6122196

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.