Evidence map›Paper›PMID 29890948›Full record

ArticleBMC bioinformatics2018

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.

Chadi Saad, Laurent Noé, Hugues Richard, Julie Leclerc, Marie-Pierre Buisine, Hélène Touzet, Martin Figeac

Abstract read
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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chadi SaadUniv. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France. chadi.saad@univ-lille1.fr.ORCID 0000-0001-6963-9126
Laurent NoéUniv. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France.
Hugues RichardSorbonne Université, UMR7238, Laboratory Computational and Quantitative Biology, LCQB, Paris, F-75005, France.
Julie LeclercUniv. Lille, Inserm, Lille University Hospital, UMR-S 1172 - JPARC - Centre de Recherche Jean-Pierre AUBERT, Lille, F-59000, France.
Marie-Pierre BuisineUniv. Lille, Inserm, Lille University Hospital, UMR-S 1172 - JPARC - Centre de Recherche Jean-Pierre AUBERT, Lille, F-59000, France.
Hélène TouzetUniv. Lille, CNRS, Inria, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, France.
Martin FigeacUniv. Lille. Plateau de génomique fonctionnelle et structurale, Lille, F-59000, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiscovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.

resultsWe developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise.

conclusionsWe shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications. AVAILABILITY: https://github.com/bonsai-team/DiNAMO .

Indexed as

Nucleotide MotifsSoftwareAlgorithmsChromatin ImmunoprecipitationComputational BiologyHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNAChip-SeqDNAMotif

Identifiers

PMID29890948
PMCPMC5996464

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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.