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Scoring DNA sequences against Position Weight Matrices (PWMs) is a widelyadopted method to identify putative transcription factor binding sites. Whilecommon bioinformatics tools produce scores that can reflect the bindingstrength between a specific transcription factor and the DNA, these scores arenot directly comparable between different transcription factors. Here, weprovide two different ways to find the scaling parameter $\lambda$ that allowsus to infer binding energy from a PWM score. The first approach uses a PWM andbackground genomic sequence as input to estimate $\lambda$ for a specifictranscription factor, which we applied to show that $\lambda$ distributions fordifferent transcription factor families correspond with their DNA bindingproperties. Our second method can reliably convert $\lambda$ between differentPWMs of the same transcription factor, which allows us to directly compare PWMsthat were generated by different approaches. These two approaches provideconsistent and computationally efficient ways to scale PWMs scores and estimatetranscription factor binding sites strength.

Reliable scaling of Position Weight Matrices for binding strength comparisons between transcription factors