Evidence map›Paper›PMID 30383764›Full record

ArticlePLoS computational biology2018

Motif-Aware PRALINE: Improving the alignment of motif regions.

Maurits Dijkstra, Punto Bawono, Sanne Abeln, K Anton Feenstra, Wan Fokkink, Jaap Heringa

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Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing 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

4 citing papers in PubMed.

  1. Molecular characterization and genetic diversity ofFrontiers in cellular and infection microbiology · 2022
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Maurits DijkstraDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-7971-6209
Punto BawonoDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Sanne AbelnDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-2779-7174
K Anton FeenstraDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-6755-9667
Wan FokkinkDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-7443-8978
Jaap HeringaDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein or DNA motifs are sequence regions which possess biological importance. These regions are often highly conserved among homologous sequences. The generation of multiple sequence alignments (MSAs) with a correct alignment of the conserved sequence motifs is still difficult to achieve, due to the fact that the contribution of these typically short fragments is overshadowed by the rest of the sequence. Here we extended the PRALINE multiple sequence alignment program with a novel motif-aware MSA algorithm in order to address this shortcoming. This method can incorporate explicit information about the presence of externally provided sequence motifs, which is then used in the dynamic programming step by boosting the amino acid substitution matrix towards the motif. The strength of the boost is controlled by a parameter, α. Using a benchmark set of alignments we confirm that a good compromise can be found that improves the matching of motif regions while not significantly reducing the overall alignment quality. By estimating α on an unrelated set of reference alignments we find there is indeed a strong conservation signal for motifs. A number of typical but difficult MSA use cases are explored to exemplify the problems in correctly aligning functional sequence motifs and how the motif-aware alignment method can be employed to alleviate these problems.

Indexed as

Amino Acid MotifsAlgorithmsAmino Acid SequenceConserved SequenceDNAenv Gene Products, Human Immunodeficiency VirusHIV-1ProteinsSequence AlignmentSequence Homology, Amino AcidDNAenv Gene Products, Human Immunodeficiency VirusProteins

Identifiers

PMID30383764
PMCPMC6233922

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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.