ArticleBMC genomics2014
SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences.
Article in BMC genomics, 2014. 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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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- RNA-binding proteins in aging and age-related diseases: roles, mechanisms, and a large language model analysis.Biogerontology · 2026Review
- Design and characterization of G-quadruplex RNA aptamers reveal RNA-binding by KDM5 lysine demethylases.Computational and structural biotechnology journal · 2025Article
- Article
- Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.Biochemical Society transactions · 2020Review
- Mechanisms and consequences of subcellular RNA localization across diverse cell types.Traffic (Copenhagen, Denmark) · 2020Review
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.BMC bioinformatics · 2017Article
- Advances in the characterization of RNA-binding proteins.Wiley interdisciplinary reviews. RNA · 2016Review
- The orientation of transcription factor binding site motifs in gene promoter regions: does it matter?BMC genomics · 2016Article
- DynaMIT: the dynamic motif integration toolkit.Nucleic acids research · 2016Article
- By the company they keep: interaction networks define the binding ability of transcription factors.Nucleic acids research · 2015Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.
resultsHere, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.
conclusionsSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.
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