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Published on: February 7, 2019
An efficient method for statistical significance calculation of transcription factor binding sites
Ziliang Qian1, Lingyi Lu, Liu Qi
1Bioinformatics Center, Key Laboratory of Molecular System Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, PR China.
Bioinformation
|February 29, 2008
Summary
We developed an efficient algorithm for calculating the statistical significance (p-values) of transcription factor binding sites (TFBS). This method significantly improves computational efficiency and enhances TFBS identification across various statistical models.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences.
- Identifying transcription factor binding sites (TFBS) is crucial for understanding gene regulation.
- Statistical models are used to predict TFBS, with p-values assessing the significance of predicted sites.
Purpose of the Study:
- To develop an efficient algorithm for precise calculation of TFBS statistical significance (p-values).
- To enhance the computational efficiency of p-value calculation for TFBS identification.
- To apply the algorithm to various statistical models and analyze p-value properties.
Main Methods:
- Developed an efficient algorithm to calculate p-values for TFBS, reducing time complexity from exponential to linear scale.
- Extended the algorithm's application to position weight matrix (PWM) and Bayesian Network (BN) models.
- Calculated p-values for all TFBS in the JASPAR database.
Main Results:
- The new algorithm significantly improves the efficiency of p-value calculation for TFBS.
- The method is applicable to diverse statistical models, including PWM and BN.
- Analysis of JASPAR database revealed novel insights into p-value distributions and variances across models and scoring schemes.
Conclusions:
- The developed algorithm offers a more efficient and precise solution for assessing the statistical significance of TFBS.
- This work enhances the reliability of TFBS identification and provides a deeper understanding of p-value characteristics.
- The software is publicly available for broader application in biological research.
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