Analysis of computational footprinting methods for DNase sequencing experiments
Our paper for the next journal club meeting on 4/18/2016 is Analysis of computational footprinting methods for DNase sequencing experiments by Gusmao et al. (Nature Methods, 2016). The abstract is as follows.
DNase-seq allows nucleotide-level identification of transcription factor binding sites on the basis of a computational search of footprint-like DNase I cleavage patterns on the DNA. Frequently in high-throughput methods, experimental artifacts such as DNase I cleavage bias affect the computational analysis of DNase-seq experiments. Here we performed a comprehensive and systematic study on the performance of computational footprinting methods. We evaluated ten footprinting methods in a panel of DNase-seq experiments for their ability to recover cell-specific transcription factor binding sites. We show that three methods—HINT, DNase2TF and PIQ—consistently outperformed the other evaluated methods and that correcting the DNase-seq signal for experimental artifacts significantly improved the accuracy of computational footprints. We also propose a score that can be used to detect footprints arising from transcription factors with potentially short residence times.
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