Reliable scaling of position weight matrices for binding strength comparisons between transcription factors.
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 parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches.


CONCLUSIONS
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.
Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. xm227@cam.ac.uk.|Cambridge Systems Biology Center, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK. xm227@cam.ac.uk.
Xiaoyan
X
Ma
Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. de276@cam.ac.uk.|Cambridge Systems Biology Center, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK. de276@cam.ac.uk.
Daphne
D
Ezer
Cambridge Systems Biology Center, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK. cnluzon@decsai.ugr.es.|Department of Computer Science and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda, Granada, Spain. cnluzon@decsai.ugr.es.
Carmen
C
Navarro
Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK. ba255@cam.ac.uk.|Cambridge Systems Biology Center, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK. ba255@cam.ac.uk.
Boris
B
Adryan
D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000378:metabolism*; D004331:Drosophila melanogaster / Q000235:genetics / Q000378:metabolism; D006801:Humans; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism*; D014714:Vertebrates / Q000235:genetics / Q000378:metabolism
D014157:Transcription Factors; D004247:DNA