Milos Pavlovic1, Pradipta Ray1,2, Kristina Pavlovic1, Aaron Kotamarti1, Min Chen3,4, Michael Q Zhang1,5. 1. Department of Biological Sciences, Center for Systems Biology. 2. School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA. 3. Department of Clinical Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. 4. Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA. 5. TNLIST, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
Abstract
MOTIVATION: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps. RESULTS: We test our approach on H1 human embryonic stem cells and H1-derived neural progenitor cells. Our predictive model is comparable in accuracy to other state-of-the-art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way for large-scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam-search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of results potentially leading to a better understanding of epigenetic gene regulation. AVAILABILITY AND IMPLEMENTATION: http://www.pradiptaray.com/direction, under CC-by-SA license. CONTACTS: pradiptaray@gmail.com or mchen@utdallas.edu or michael.zhang@utdallas.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps. RESULTS: We test our approach on H1 human embryonic stem cells and H1-derived neural progenitor cells. Our predictive model is comparable in accuracy to other state-of-the-art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way for large-scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam-search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of results potentially leading to a better understanding of epigenetic gene regulation. AVAILABILITY AND IMPLEMENTATION: http://www.pradiptaray.com/direction, under CC-by-SA license. CONTACTS: pradiptaray@gmail.com or mchen@utdallas.edu or michael.zhang@utdallas.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Rajdeep Das; Nevenka Dimitrova; Zhenyu Xuan; Robert A Rollins; Fatemah Haghighi; John R Edwards; Jingyue Ju; Timothy H Bestor; Michael Q Zhang Journal: Proc Natl Acad Sci U S A Date: 2006-07-03 Impact factor: 11.205
Authors: Anshul Kundaje; Wouter Meuleman; Jason Ernst; Misha Bilenky; Angela Yen; Alireza Heravi-Moussavi; Pouya Kheradpour; Zhizhuo Zhang; Jianrong Wang; Michael J Ziller; Viren Amin; John W Whitaker; Matthew D Schultz; Lucas D Ward; Abhishek Sarkar; Gerald Quon; Richard S Sandstrom; Matthew L Eaton; Yi-Chieh Wu; Andreas R Pfenning; Xinchen Wang; Melina Claussnitzer; Yaping Liu; Cristian Coarfa; R Alan Harris; Noam Shoresh; Charles B Epstein; Elizabeta Gjoneska; Danny Leung; Wei Xie; R David Hawkins; Ryan Lister; Chibo Hong; Philippe Gascard; Andrew J Mungall; Richard Moore; Eric Chuah; Angela Tam; Theresa K Canfield; R Scott Hansen; Rajinder Kaul; Peter J Sabo; Mukul S Bansal; Annaick Carles; Jesse R Dixon; Kai-How Farh; Soheil Feizi; Rosa Karlic; Ah-Ram Kim; Ashwinikumar Kulkarni; Daofeng Li; Rebecca Lowdon; GiNell Elliott; Tim R Mercer; Shane J Neph; Vitor Onuchic; Paz Polak; Nisha Rajagopal; Pradipta Ray; Richard C Sallari; Kyle T Siebenthall; Nicholas A Sinnott-Armstrong; Michael Stevens; Robert E Thurman; Jie Wu; Bo Zhang; Xin Zhou; Arthur E Beaudet; Laurie A Boyer; Philip L De Jager; Peggy J Farnham; Susan J Fisher; David Haussler; Steven J M Jones; Wei Li; Marco A Marra; Michael T McManus; Shamil Sunyaev; James A Thomson; Thea D Tlsty; Li-Huei Tsai; Wei Wang; Robert A Waterland; Michael Q Zhang; Lisa H Chadwick; Bradley E Bernstein; Joseph F Costello; Joseph R Ecker; Martin Hirst; Alexander Meissner; Aleksandar Milosavljevic; Bing Ren; John A Stamatoyannopoulos; Ting Wang; Manolis Kellis Journal: Nature Date: 2015-02-19 Impact factor: 69.504