Extracting sequence features to predict protein-DNA interactions: a comparative study.
Predicting how and where proteins, especially transcription factors (TFs), interact with DNA is an important problem in biology. We present here a systematic study of predictive modeling approaches to the TF-DNA binding problem, which have been frequently shown to be more efficient than those methods only based on position-specific weight matrices (PWMs). In these approaches, a statistical relationship between genomic sequences and gene expression or ChIP-binding intensities is inferred through a regression framework; and influential sequence features are identified by variable selection. We examine a few state-of-the-art learning methods including stepwise linear regression, multivariate adaptive regression splines, neural networks, support vector machines, boosting and Bayesian additive regression trees (BART). These methods are applied to both simulated datasets and two whole-genome ChIP-chip datasets on the TFs Oct4 and Sox2, respectively, in human embryonic stem cells. We find that, with proper learning methods, predictive modeling approaches can significantly improve the predictive power and identify more biologically interesting features, such as TF-TF interactions, than the PWM approach. In particular, BART and boosting show the best and the most robust overall performance among all the methods.
Zhou
Qing
Q
Department of Statistics, University of California, Los Angeles, CA 90095, USA. zhou@stat.ucla.edu
Liu
Jun S
JS
D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D047369:Chromatin Immunoprecipitation; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000378:metabolism*; D053595:Embryonic Stem Cells / Q000378:metabolism; D024002:HMGB Proteins / Q000378:metabolism; D006801:Humans; D051379:Mice; D016571:Neural Networks, Computer; D050814:Octamer Transcription Factor-3 / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011401:Promoter Regions, Genetic; D012044:Regression Analysis; D055748:SOXB1 Transcription Factors; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism*
D004268:DNA-Binding Proteins; D024002:HMGB Proteins; D050814:Octamer Transcription Factor-3; C494173:POU5F1 protein, human; C527782:SOX2 protein, human; D055748:SOXB1 Transcription Factors; C527783:Sox2 protein, mouse; D014157:Transcription Factors