Structure-Seq, CLIP-Seq, Hi-C Data Analysis ------------------------------------------- (x in front of a number means the paper has been chosen) 1: Ding Y, Tang Y, Kwok CK, Zhang Y, Bevilacqua PC, Assmann SM. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature. 2014 Jan 30;505(7485):696-700. doi: 10.1038/nature12756. Epub 2013 Nov 24. PubMed PMID: 24270811 2: Fukunaga T, Ozaki H, Terai G, Asai K, Iwasaki W, Kiryu H. CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data. Genome Biol. 2014 Jan 21;15(1):R16. [Epub ahead of print] PubMed PMID: 24447569. 3: Wang T, Xie Y, Xiao G. dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol. 2014 Jan 7;15(1):R11. [Epub ahead of print] PubMed PMID: 24398258. 4: Ay F, Bailey TL, Noble WS. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res. 2014 Mar 19. [Epub ahead of print] PubMed PMID: 24501021. 5: Trieu T, Cheng J. Large-scale reconstruction of 3D structures of human chromosomes from chromosomal contact data. Nucleic Acids Res. 2014 Jan 24. [Epub ahead of print] PubMed PMID: 24465004. 6: Korbel JO, Lee C. Genome assembly and haplotyping with Hi-C. Nat Biotechnol. 2013 Dec;31(12):1099-101. doi: 10.1038/nbt.2764. PubMed PMID: 24316648. 7: Zhang Z, Li G, Toh KC, Sung WK. 3D chromosome modeling with semi-definite programming and Hi-C data. J Comput Biol. 2013 Nov;20(11):831-46. doi: 10.1089/cmb.2013.0076. PubMed PMID: 24195706. 8: Jin F, Li Y, Dixon JR, Selvaraj S, Ye Z, Lee AY, Yen CA, Schmitt AD, Espinoza CA, Ren B. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature. 2013 Nov 14;503(7475):290-4. doi: 10.1038/nature12644. Epub 2013 Oct 20. PubMed PMID: 24141950; PubMed Central PMCID: PMC3838900. 9: Hu M, Deng K, Qin Z, Dixon J, Selvaraj S, Fang J, Ren B, Liu JS. Bayesian inference of spatial organizations of chromosomes. PLoS Comput Biol. 2013;9(1):e1002893. doi: 10.1371/journal.pcbi.1002893. Epub 2013 Jan 31. PubMed PMID: 23382666; PubMed Central PMCID: PMC3561073. 10: Yaffe E, Tanay A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet. 2011 Oct 16;43(11):1059-65. doi: 10.1038/ng.947. PubMed PMID: 22001755. 11: Berger B, Peng J, Singh M. Computational solutions for omics data. Nat Rev Genet. 2013 May;14(5):333-46. doi: 10.1038/nrg3433. Review. PubMed PMID: 23594911; PubMed Central PMCID: PMC3966295. 12. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015 Oct;12(10):931-4. 13. Zhang S, et. al. A deep learning framework for modeling structural features of RNA-binding protein targets. NAR 2015. 14. Alipanahi, B., Delong, A., Weirauch, M.T. & Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015). 15. Yongjin Park and Manolis Kellis. Deep learning for regulatory genomics. Nat. Biotech., 2015. x16. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539. x17. Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016 Feb 11. pii: btw074. [Epub ahead of print] 18. Michael K.K. Leung, Hui Yuan Xiong, Leo J. Lee, and Brendan J. Frey. Deep learning of the tissue-regulated splicing code. Bioinformatics. 2014 Jun 15; 30(12): i121–i129. Published online 2014 Jun 11. doi: 10.1093/bioinformatics/btu277 x19. Lotfi M, Zare-Mirakabad F, Montaseri S. RNA secondary structure prediction based on SHAPE data in helix regions. J Theor Biol. 2015 Sep 7;380:178-82. doi: 10.1016/j.jtbi.2015.05.026. Epub 2015 May 30. 20. Tran et al. De novo peptide sequencing by deep learning, PNAS, 2017 21. Dai et al. Learning combinatorial optimization algorithms over graphs. NIPS'2017.