ArticleNucleic acids research2009
Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 31 citations in OpenAlex.
- Review of single-cell RNA-seq data clustering for cell-type identification and characterization.RNA (New York, N.Y.) · 2023Review
- Towards a map of cis-regulatory sequences in the human genome.Nucleic acids research · 2018Article
- Assessment of transfer methods for comparative genomics of regulatory networks in bacteria.BMC bioinformatics · 2016Article
- An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.BMC genomics · 2016Article
- Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses.Scientific reports · 2016Article
- Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.PloS one · 2016Article
- Article
- Identification of cell types from single-cell transcriptomes using a novel clustering method.Bioinformatics (Oxford, England) · 2015Article
- De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.BMC genomics · 2014Article
- SPIC: a novel similarity metric for comparing transcription factor binding site motifs based on information contents.BMC systems biology · 2013Article
- Phylogenetic footprinting: a boost for microbial regulatory genomics.Protoplasma · 2012Review
- Comparing binding site information to binding affinity reveals that Crp/DNA complexes have several distinct binding conformers.Nucleic acids research · 2011Article
- MotifClick: prediction of cis-regulatory binding sites via merging cliques.BMC bioinformatics · 2011Article
- A new framework for identifying cis-regulatory motifs in prokaryotes.Nucleic acids research · 2011Article
- Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes.BMC bioinformatics · 2010Article
- OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains.BMC systems biology · 2010Article
- A novel alignment-free method for comparing transcription factor binding site motifs.PloS one · 2010Article
- Accurate recognition of cis-regulatory motifs with the correct lengths in prokaryotic genomes.Nucleic acids research · 2010Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.
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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.