Cluster-Buster: finding dense clusters of motifs in DNA sequences

By Martin C. Frith, Michael C. Li, Zhiping Weng. Nucleic Acids Res., 2003

343 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9abe959e996f796ea5dae5a15bdddff8c87dd6de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Applied bioinformatics for the identification of regulatory elements (2004, methodology): “A subsequent algorithm uses hidden Markov models to circumvent this limitation, improving specificity a further twofol”
Genome-wide prediction of transcription factor binding sites using an integrated model (2010, methodology): “Methods such as Comet [18], Cluster-Buster [19] and ModuleMiner [20] use motifs documented in databases - for example, JASPAR [21] and TRANSFAC [22] - or predicted by de novo motif finding algorithms, and search for clusters of TFBSs.”
JASPAR: an open-access database for eukaryotic transcription factor binding profiles (2004, methodology): “In addition, the web interface supports retrieval of subsets of matrices for use in emerging web-based analysis tools, such as Cluster-Buster (16), MSCAN (17) or MCAST (18).”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, methodology): “…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…”
RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach (2016, methodology): “We firstly downloaded 102 human RBP binding motifs from CISBP-RNA [34], then Cluster-Buster [47] was employed to score RNA sequences for binding sites clusters.”
RSAT: regulatory sequence analysis tools (2008, background): “shtml), including cis-regulatory module detection with Cluster-Buster (29) and search for overrepresentation of PSSM hits with clover (30), rover and MotifViz (31).”
A scalable SCENIC workflow for single-cell gene regulatory network analysis (2020, background): “For a large collection of PWMs or motifs, whole-genome rankings are pre-calculated by scoring putative regulatory regions for the presence of homotypic clusters of motif instances (via the hidden Markov model implementation of cluster-buster 45 ).”
Computational prediction of transcription-factor binding site locations (2003, background): “More recently, approaches have been developed to identify higher-order site clusterings [81-93].”