ArticleBriefings in bioinformatics2022
Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- ETNet: an interpretable transformer framework for enhancer-enhancer interaction prediction with cross-context transferability.Briefings in bioinformatics · 2025Article
- Negative dataset selection impacts machine learning-based predictors for multiple bacterial species promoters.Bioinformatics (Oxford, England) · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- The role of structure in regulatory RNA elements.Bioscience reports · 2024Review
- Identifying transcription factors with cell-type specific DNA binding signatures.BMC genomics · 2024Article
- Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds.Tree physiology · 2024Article
- MMGAT: a graph attention network framework for ATAC-seq motifs finding.BMC bioinformatics · 2024Article
- GNNMF: a multi-view graph neural network for ATAC-seq motif finding.BMC genomics · 2024Article
- A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.Patterns (New York, N.Y.) · 2024Article
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.Nucleic acids research · 2024Article
- Article
- MMGraph: a multiple motif predictor based on graph neural network and coexisting probability for ATAC-seq data.Bioinformatics (Oxford, England) · 2022Article
- Predicting miRNA-disease associations based on multi-view information fusion.Frontiers in genetics · 2022Article
- DESSO-DB: A web database for sequence and shape motif analyses and identification.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs.
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