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ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

(2009) BMC BIOINFORMATICS. 10(suppl. 1).
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Abstract
Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.
Keywords
MOTIFS, REGIONS, GENE-REGULATION, SEQUENCE-ANALYSIS

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MLA
Sun, Hong et al. “ModuleDigger: An Itemset Mining Framework for the Detection of Cis-regulatory Modules.” Ed. Michael Q Zhang, Michael S Waterman, & Xuegong Zhang. BMC BIOINFORMATICS 10.suppl. 1 (2009): n. pag. Print.
APA
Sun, Hong, De Bie, T., Storms, V., Fu, Q., Dhollander, T., Lemmens, K., Verstuyf, A., et al. (2009). ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules. (M. Q. Zhang, M. S. Waterman, & X. Zhang, Eds.)BMC BIOINFORMATICS, 10(suppl. 1). Presented at the 7th Asia-Pacific Bioinformatics Conference (APBC 2009).
Chicago author-date
Sun, Hong, Tijl De Bie, Valerie Storms, Qiang Fu, Thomas Dhollander, Karen Lemmens, Annemieke Verstuyf, Bart De Moor, and Kathleen Marchal. 2009. “ModuleDigger: An Itemset Mining Framework for the Detection of Cis-regulatory Modules.” Ed. Michael Q Zhang, Michael S Waterman, and Xuegong Zhang. Bmc Bioinformatics 10 (suppl. 1).
Chicago author-date (all authors)
Sun, Hong, Tijl De Bie, Valerie Storms, Qiang Fu, Thomas Dhollander, Karen Lemmens, Annemieke Verstuyf, Bart De Moor, and Kathleen Marchal. 2009. “ModuleDigger: An Itemset Mining Framework for the Detection of Cis-regulatory Modules.” Ed. Michael Q Zhang, Michael S Waterman, and Xuegong Zhang. Bmc Bioinformatics 10 (suppl. 1).
Vancouver
1.
Sun H, De Bie T, Storms V, Fu Q, Dhollander T, Lemmens K, et al. ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules. Zhang MQ, Waterman MS, Zhang X, editors. BMC BIOINFORMATICS. 2009;10(suppl. 1).
IEEE
[1]
H. Sun et al., “ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules,” BMC BIOINFORMATICS, vol. 10, no. suppl. 1, 2009.
@article{3187088,
  abstract     = {Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. 
Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. 
Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.},
  articleno    = {S30},
  author       = {Sun, Hong and De Bie, Tijl and Storms, Valerie and Fu, Qiang and Dhollander, Thomas and Lemmens, Karen and Verstuyf, Annemieke and De Moor, Bart and Marchal, Kathleen},
  editor       = {Zhang, Michael Q and Waterman, Michael S and Zhang, Xuegong},
  issn         = {1471-2105},
  journal      = {BMC BIOINFORMATICS},
  keywords     = {MOTIFS,REGIONS,GENE-REGULATION,SEQUENCE-ANALYSIS},
  language     = {eng},
  location     = {Beijing, PR China},
  number       = {suppl. 1},
  pages        = {12},
  title        = {ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules},
  url          = {http://dx.doi.org/10.1186/1471-2105-10-S1-S30},
  volume       = {10},
  year         = {2009},
}

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