The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped k-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos

Predicting variation of DNA shape preferences in protein-DNA interaction in cancer cells with a new biophysical model

DNA shape readout is an important mechanism of target site recognition by transcription factors, in addition to the sequence readout. Several models of transcription factor-DNA binding which consider DNA shape have been developed in recent years. We present a new biophysical model of protein-DNA interaction by considering the DNA shape features, which is based on a neighbour dinucleotide dependency model BayesPI2. The parameters of the new model are restricted to a subspace spanned by the 2-mer DNA shape features, which allowing a biophysical interpretation of the new parameters as position-dependent preferences towards certain values of the features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across cancer cell lines and cellular conditions. We find evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after treatment with steroids. The new model is useful for elucidating finer details of transcription factor-DNA interaction. It may be used to improve the prediction of cancer mutation effects in the future

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application