ArticleNucleic acids research2014
Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- Review
- RNA binding motif protein 10 suppresses lung cancer progression by controlling alternative splicing of eukaryotic translation initiation factor 4H.EBioMedicine · 2020Article
- MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.Bioinformatics (Oxford, England) · 2020Article
- Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation.Computational and structural biotechnology journal · 2020Review
- MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.BMC bioinformatics · 2019Article
- DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.BMC bioinformatics · 2018Article
- RBM10: Harmful or helpful-many factors to consider.Journal of cellular biochemistry · 2018Review
- HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.Nucleic acids research · 2018Article
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- An RRM-ZnF RNA recognition module targets RBM10 to exonic sequences to promote exon exclusion.Nucleic acids research · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.Nucleic acids research · 2016Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models. Sets of positive and negative example sequences are mined for sequence motifs whose occurrence frequency varies between the sets. The method offers several objective functions, but we concentrate on mutual information of condition and motif occurrence. We perform a systematic comparison of our method and numerous published motif-finding tools. Our method achieves the highest motif discovery performance, while being faster than most published methods. We present case studies of data from various technologies, including ChIP-Seq, RIP-Chip and PAR-CLIP, of embryonic stem cell transcription factors and of RNA-binding proteins, demonstrating practicality and utility of the method. For the alternative splicing factor RBM10, our analysis finds motifs known to be splicing-relevant. The motif discovery method is implemented in the free software package Discrover. It is applicable to genome- and transcriptome-scale data, makes use of available repeat experiments and aside from binary contrasts also more complex data configurations can be utilized.
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