BML: a versatile web server for bipartite motif discovery.
Gibbs sampling
Shannon’s entropy
expectation–maximization
motif
transcription factor
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
17 01 2022
17 01 2022
Historique:
received:
18
06
2021
revised:
18
11
2021
accepted:
19
11
2021
pubmed:
3
1
2022
medline:
12
3
2022
entrez:
2
1
2022
Statut:
ppublish
Résumé
Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).
Identifiants
pubmed: 34974623
pii: 6490318
doi: 10.1093/bib/bbab536
pmc: PMC8769915
pii:
doi:
Substances chimiques
Transcription Factors
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIEHS NIH HHS
ID : K01 ES025434
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD084633
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012373
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012907
Pays : United States
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.
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