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Updated: May 9, 2026
07:55
Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Published on: May 31, 2011
Statistical significance of combinatorial regulations
Aika Terada1, Mariko Okada-Hatakeyama, Koji Tsuda
1Department of Computer Science, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan.
Summary
We developed a new algorithm, the limitless arity multiple-testing procedure (LAMP), to efficiently detect combinations of transcription factors. LAMP overcomes computational challenges, enabling the discovery of complex regulatory pathways in biological data.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Transcription factors (TFs) often function in combinations to regulate cellular responses.
- Detecting these TF combinations is computationally challenging due to multiple testing corrections.
- Current methods limit the number of TFs that can be analyzed together.
Purpose of the Study:
- To develop an efficient algorithm for detecting combinatorial TF regulation without arity limits.
- To rigorously control the family-wise error rate (FWER) while identifying significant TF combinations.
- To apply the method to identify complex regulatory patterns in biological datasets.
Main Methods:
- Proposed the Limitless Arity Multiple-testing Procedure (LAMP), an efficient branch-and-bound algorithm.
- Developed a method to count testable combinations and minimize the Bonferroni correction factor.
- Implemented LAMP for analyzing TF binding motif combinations.
Main Results:
- LAMP precisely counts testable combinations and calibrates the Bonferroni factor effectively.
- The algorithm identifies significant TF combinations without an upper limit on arity.
- Discovered statistically significant combinations of up to eight binding motifs in human breast cancer transcriptomes.
Conclusions:
- LAMP offers a powerful tool for uncovering coordinated gene regulation pathways.
- The method facilitates the discovery of complex, multi-TF regulatory mechanisms.
- LAMP can reveal hidden associations within heterogeneous biological data, advancing systems biology.
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