The DOI matches its Crossref record.
Cluster-Buster: finding dense clusters of motifs in DNA sequences
Open accessAbstract
The signals that determine activation and repression of specific genes in response to appropriate stimuli are one of the most important, but least understood, types of information encoded in genomic DNA. The nucleotide sequence patterns, or motifs, preferentially bound by various transcription factors have been collected in databases. However, these motifs appear to be individually too short and degenerate to enable detection of functional enhancer and silencer elements within a large genome. Several groups have proposed that dense clusters of motifs may diagnose regulatory regions more accurately. Cluster-Buster is the third incarnation of our software for finding clusters of pre-specified motifs in DNA sequences. We offer a Cluster-Buster web server at http://zlab.bu.edu/cluster-buster/.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A scalable SCENIC workflow for single-cell gene regulatory network analysis
Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0336-2 · Cited by 1,494
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900
“…step whereby every gene in the human genome, along with orthologous sequences in ten other vertebrate genomes, is scanned with Cluster-Buster [33] for homotypic clusters of motifs using a library of N position weight matrices (PWMs), generating a database of N ranked lists of genes, each…”
BackgroundMethodSCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778
cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data
Bravo González-Blas, C., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G., Christiaens, V., Davie, K., Wouters, J., & Aerts, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0367-1 · Cited by 456
i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly
Imrichová, H., Hulselmans, G., Kalender Atak, Z., Potier, D., & Aerts, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv395 · Cited by 202
“The predefined CRRs were scored using Cluster-Buster (25) for each PWM across 10 mammalian genomes, namely: hg19 (Homo Sapiens), bosTau4 (Bos Taurus), canFam2 (Canis familiaris), mm9 (Mus musculus), monDom5 (Monodelphis domestica), panTro2 (Pan troglodytes), ponAbe2 (Pongo pygmaeus abelii), rheMac2 (Macaca mulatta), r…”
Metadata
- Authors
- Frith, M. C.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-07-01
- Volume
- 31
- Issue
- 13
- Pages
- 3666-3668
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2150612336
- Cited by
- 324
- References
- 0
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cluster-Buster: finding dense clusters of motifs in DNA sequences. https://app.cochat.ai/papers/10.1093/nar/gkg540
- The paper
- Frith, M. C. (2003). Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Research. https://doi.org/10.1093/nar/gkg540
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
