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BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional

Igor V Deyneko1

  • 1K.A. Timiryazev Institute of Plant Physiology RAS, 35 Botanicheskaya Str., Moscow 127276, Russia.

International Journal of Molecular Sciences
|February 10, 2024
PubMed
Summary

A new computational method, BestCRM, efficiently identifies cis-regulatory modules in gene promoters. It uses an exhaustive search and automatic parameter estimation, outperforming existing tools in accuracy and ease of use.

Keywords:
DNA motifscis-regulatory modulespromoterstranscriptional regulation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene transcriptional regulation is organized by cis-regulatory modules in gene promoters.
  • These modules are combinations of DNA motifs, and their identification is computationally complex (NP-hard).
  • Current methods often rely on heuristics, requiring manual parameter tuning and yielding suboptimal results.

Purpose of the Study:

  • To develop a novel computational method for identifying cis-regulatory elements in gene promoters.
  • To overcome the limitations of existing methods by employing an exhaustive search strategy.
  • To provide a user-friendly tool with minimal parameter input.

Main Methods:

  • Implemented an exhaustive search of all feasible cis-regulatory module configurations.
  • Developed automatic parameter estimation using positive and negative promoter datasets.
  • Utilized a multidimensional hash function to accelerate the computational search for efficiency.

Main Results:

  • The BestCRM method demonstrated superior performance compared to existing tools on benchmark and real-world data.
  • Achieved higher specificity, sensitivity, and Area Under the Curve (AUC) metrics.
  • The method is computationally efficient, completing analyses in hours on standard hardware.

Conclusions:

  • BestCRM offers a more accurate and efficient approach to identifying cis-regulatory modules in gene promoters.
  • Its automatic parameter estimation and minimal input requirements make it a practical tool for researchers.
  • This advancement aids in understanding gene regulation and its associated biological processes.