Reliable scaling of position weight matrices for binding strength comparisons between transcription factors. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Ma, Xiaoyan; Ezer, Daphne; Navarro, Carmen; Adryan, Boris
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-12
• Subjects: Transcription factor -- Position weight matrix (Position-Specific Scoring Matrix) -- Binding site strength; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0666-1
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities. Results Here, we provide two different ways to find the scaling parameterλ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimateλ for a specific transcription factor, which we applied to show thatλ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convertλ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches. Conclusion These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.