Dilated Convolutions for Modeling Long-Distance Genomic Dependencies

We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-distance dependencies that arise from DNA’s 3-dimensional conformation. In order to study long-distance dependencies, we develop and release a novel dataset for a larger-context modeling task. Using this new data set we model long-distance interactions using dilated convolutional neural networks, and compare them to standard convolutions and recurrent neural networks. We show that dilated convolutions are effective at modeling the locations of regulatory markers in the human genome, such as transcription factor binding sites, histone modifications, and DNAse hypersensitivity sites.

[1]  B. Frey,et al.  Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.

[2]  Martin J. Wainwright,et al.  Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.

[3]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[4]  Xiaohui S. Xie,et al.  DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.

[5]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[8]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[9]  Andrew McCallum,et al.  Fast and Accurate Entity Recognition with Iterated Dilated Convolutions , 2017, EMNLP.

[10]  J. Dekker,et al.  Hi-C: a comprehensive technique to capture the conformation of genomes. , 2012, Methods.

[11]  D. Perkins,et al.  Expanding the ‘central dogma’: the regulatory role of nonprotein coding genes and implications for the genetic liability to schizophrenia , 2005, Molecular Psychiatry.

[12]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[13]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[14]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[15]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[16]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[17]  J. T. Kadonaga,et al.  Going the distance: a current view of enhancer action. , 1998, Science.

[18]  Andrew McCallum,et al.  Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions , 2017, ArXiv.