ArticlePLoS computational biology2010
Assessing computational methods of cis-regulatory module prediction.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Article
- Detecting clusters of transcription factors based on a nonhomogeneous poisson process model.BMC bioinformatics · 2022Article
- Review
- AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain.Journal of visualized experiments : JoVE · 2022Article
- regCNN: identifyingComputational and structural biotechnology journal · 2022Article
- Annotating the Insect Regulatory Genome.Insects · 2021Review
- Interpretation of deep learning in genomics and epigenomics.Briefings in bioinformatics · 2021Review
- Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns.BMC bioinformatics · 2020Article
- Computational enhancer prediction: evaluation and improvements.BMC bioinformatics · 2019Article
- Analysis of Genetic Variation Indicates DNA Shape Involvement in Purifying Selection.Molecular biology and evolution · 2018Article
- A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model.BioMed research international · 2017Article
- Linking Core Promoter Classes to Circadian Transcription.PLoS genetics · 2016Article
- Article
- Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism.Nucleic acids research · 2015Article
- Article
- Comprehensive discovery of DNA motifs in 349 human cells and tissues reveals new features of motifs.Nucleic acids research · 2015Article
- BorreliaBase: a phylogeny-centered browser of Borrelia genomes.BMC bioinformatics · 2014Article
- iRegulon: from a gene list to a gene regulatory network using large motif and track collections.PLoS computational biology · 2014Article
- GAMI-CRM: Using de novo motif inference to detect cis-regulatory modules.Proceedings of the ... Congress on Evolutionary Computation. Congress on Evolutionary Computation · 2014Article
- Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation.Nucleic acids research · 2014Article
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Authors and funding
3 authors.
Funding
Abstract
Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods.
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Registered trials
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.