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14 results on '"Vitor Onuchic"'

1. Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk

Author

Sairam Behera, Jonathan R. Belyeu, Xiao Chen, Luis F. Paulin, Ngoc Quynh H. Nguyen, Emma Newman, Medhat Mahmoud, Vipin K. Menon, Qibin Qi, Parag Joshi, Santica Marcovina, Massimiliano Rossi, Eric Roller, James Han, Vitor Onuchic, Christy L. Avery, Christie M. Ballantyne, Carlos J. Rodriguez, Robert C. Kaplan, Donna M. Muzny, Ginger A. Metcalf, Richard A. Gibbs, Bing Yu, Eric Boerwinkle, Michael A. Eberle, and Fritz J. Sedlazeck

Subjects
LPA, Copy number variation, Cardiovascular disease, Next Generation Sequencing, Illumina, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase approximately 50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.

Published
2024
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2. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

Author

Bohu Pan, Luyao Ren, Vitor Onuchic, Meijian Guan, Rebecca Kusko, Steve Bruinsma, Len Trigg, Andreas Scherer, Baitang Ning, Chaoyang Zhang, Christine Glidewell-Kenney, Chunlin Xiao, Eric Donaldson, Fritz J. Sedlazeck, Gary Schroth, Gokhan Yavas, Haiying Grunenwald, Haodong Chen, Heather Meinholz, Joe Meehan, Jing Wang, Jingcheng Yang, Jonathan Foox, Jun Shang, Kelci Miclaus, Lianhua Dong, Leming Shi, Marghoob Mohiyuddin, Mehdi Pirooznia, Ping Gong, Rooz Golshani, Russ Wolfinger, Samir Lababidi, Sayed Mohammad Ebrahim Sahraeian, Steve Sherry, Tao Han, Tao Chen, Tieliu Shi, Wanwan Hou, Weigong Ge, Wen Zou, Wenjing Guo, Wenjun Bao, Wenzhong Xiao, Xiaohui Fan, Yoichi Gondo, Ying Yu, Yongmei Zhao, Zhenqiang Su, Zhichao Liu, Weida Tong, Wenming Xiao, Justin M. Zook, Yuanting Zheng, and Huixiao Hong

Subjects
Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. Conclusions Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Published
2022
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3. ToPS: a framework to manipulate probabilistic models of sequence data.

Author

André Yoshiaki Kashiwabara, Igor Bonadio, Vitor Onuchic, Felipe Amado, Rafael Mathias, and Alan Mitchell Durham

Subjects
Biology (General), QH301-705.5
Abstract

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

Published
2013
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4. Integrative analysis of 111 reference human epigenomes Open.

Author

Roadmap Epigenomics Consortium, 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 T. 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 A. Moore, Eric Chuah, Angela Tam, Theresa K. Canfield, R. Scott Hansen, Rajinder Kaul, Peter J. Sabo, Mukul S. Bansal, Annaick Carles, Jesse R. Dixon, Kyle Kai-How Farh, Soheil Feizi, Rosa Karlic, Ah-Ram Kim, Ashwinikumar Kulkarni, Daofeng Li, Rebecca F. 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 0009, 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 R. Sunyaev, James A. Thomson, Thea D. Tlsty, Li-Huei Tsai, Wei Wang 0051, 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, and Manolis Kellis

Published
2015
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5. Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

Author

Bohu Pan, Luyao Ren, Vitor Onuchic, Meijian Guan, Rebecca Kusko, Steve Bruinsma, Len Trigg, Andreas Scherer, Baitang Ning, Chaoyang Zhang, Christine Glidewell-Kenney, Chunlin Xiao, Eric Donaldson, Fritz J. Sedlazeck, Gary Schroth, Gokhan Yavas, Haiying Grunenwald, Haodong Chen, Heather Meinholz, Joe Meehan, Jing Wang, Jingcheng Yang, Jonathan Foox, Jun Shang, Kelci Miclaus, Lianhua Dong, Leming Shi, Marghoob Mohiyuddin, Mehdi Pirooznia, Ping Gong, Rooz Golshani, Russ Wolfinger, Samir Lababidi, Sayed Mohammad Ebrahim Sahraeian, Steve Sherry, Tao Han, Tao Chen, Tieliu Shi, Wanwan Hou, Weigong Ge, Wen Zou, Wenjing Guo, Wenjun Bao, Wenzhong Xiao, Xiaohui Fan, Yoichi Gondo, Ying Yu, Yongmei Zhao, Zhenqiang Su, Zhichao Liu, Weida Tong, Wenming Xiao, Justin M. Zook, Yuanting Zheng, Huixiao Hong, and Institute for Molecular Medicine Finland

Subjects
11832 Microbiology and virology, 0303 health sciences, 318 Medical biotechnology, Whole Genome Sequencing, QH301-705.5, Genome, Human, Research, 1184 Genetics, developmental biology, physiology, High-Throughput Nucleotide Sequencing, Reproducibility of Results, QH426-470, CANCER, Polymorphism, Single Nucleotide, GENOTYPE, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, DATA SETS, INDEL Mutation, 030220 oncology & carcinogenesis, Genetics, Humans, Biology (General), MUTATION, 030304 developmental biology
Abstract

BackgroundReproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.ResultsTo dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.ConclusionsOur findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Published
2021

6. Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types

Author

Matthew E. Roth, Vitor Onuchic, Wendy M. White, Adrian V. Lee, Ryan J. Hartmaier, Ronak Y. Patel, David N. Boone, Aleksandar Milosavljevic, Vesna D. Garovic, Michael L. Samuels, and Steffi Oesterreich

Subjects
0301 basic medicine, Epigenomics, Cell type, Stromal cell, Carcinogenesis, Breast Neoplasms, Biology, deconvolution, Bioinformatics, medicine.disease_cause, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Breast cancer, breast cancer, Stroma, cell type composition, Cell Line, Tumor, medicine, Tumor Microenvironment, Humans, cancer, Computer Simulation, metabolic coupling, heterotypic interaction, Tumor microenvironment, DNA methylation, Reproducibility of Results, Sequence Analysis, DNA, medicine.disease, 3. Good health, Gene Expression Regulation, Neoplastic, 030104 developmental biology, Phenotype, Adipose Tissue, Cancer research, Disease Progression, gene expression, Female, Warburg effect, Stromal Cells, Oxidation-Reduction, metabolism
Abstract

SummaryCancer progression depends on both cell-intrinsic processes and interactions between different cell types. However, large-scale assessment of cell type composition and molecular profiles of individual cell types within tumors remains challenging. To address this, we developed epigenomic deconvolution (EDec), an in silico method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell types. By applying EDec to The Cancer Genome Atlas (TCGA) breast tumors, we detect changes in immune cell infiltration related to patient prognosis, and a striking change in stromal fibroblast-to-adipocyte ratio across breast cancer subtypes. Furthermore, we show that a less adipose stroma tends to display lower levels of mitochondrial activity and to be associated with cancerous cells with higher levels of oxidative metabolism. These findings highlight the role of stromal composition in the metabolic coupling between distinct cell types within tumors.

Published
2016
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7. Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci

Author

Lillian Ashmore, Timur R. Galeev, Manolis Kellis, Walid D. Fakhouri, Piotr Pawliczek, Zhizhuo Zhang, Aleksandar Milosavljevic, Cristian Coarfa, Robert C. Altshuler, Joel Rozowsky, Jéssica Wildgrube Bertol, Fuli Yu, Zhuoyi Huang, Eugene Lurie, Ronak Y. Patel, R. Alan Harris, Vitor Onuchic, Mark Gerstein, Ivenise Carrero, and Massachusetts Institute of Technology. Department of Biology

Subjects
0301 basic medicine, Bisulfite sequencing, Biology, Allelic Imbalance, Polymorphism, Single Nucleotide, Article, Epigenesis, Genetic, 03 medical and health sciences, Humans, Sulfites, Disease, Gene Regulatory Networks, Allele, Alleles, Epigenomics, Regulation of gene expression, Genetics, Multidisciplinary, Binding Sites, Genome, Human, Methylation, Epigenome, Sequence Analysis, DNA, DNA Methylation, 030104 developmental biology, Genetic Loci, DNA methylation, Human genome, CpG Islands, Genome-Wide Association Study, Transcription Factors
Abstract

INTRODUCTION A majority of imbalances in DNA methylation between homologous chromosomes in humans are sequence-dependent; the DNA sequence differences between the two chromosomes cause differences in the methylation state of neighboring cytosines on the same chromosome. The analyses of this sequence-dependent allele-specific methylation (SD-ASM) traditionally involved measurement of average methylation levels across many cells. Detailed understanding of SD-ASM at the single-cell and single-chromosome levels is lacking. This gap in understanding may hide the connection between SD-ASM, ubiquitous stochastic cell-to-cell and chromosome-to-chromosome variation in DNA methylation, and the puzzling and evolutionarily conserved patterns of intermediate methylation at gene regulatory loci. RATIONALE Whole-genome bisulfite sequencing (WGBS) provides the ultimate single-chromosome level of resolution and comprehensive whole-genome coverage required to explore SD-ASM. However, the exploration of the link between SD-ASM, stochastic variation in DNA methylation, and gene regulation requires deep coverage by WGBS across tissues and individuals and the context of other epigenomic marks and gene transcription. RESULTS We constructed maps of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep (1691-fold) combined WGBS read coverage across 49 methylomes revealed CpG methylation imbalances exceeding 30% differences at 5% of the loci, which is more conservative than previous estimates in the 8 to 10% range; a similar value (8%) is observed in our dataset when we lowered our threshold for detecting allelic imbalance to 20% methylation difference between the two alleles. Extensive sequence-dependent CpG methylation imbalances were observed at thousands of heterozygous regulatory loci. Stochastic switching, defined as random transitions between fully methylated and unmethylated states of DNA, occurred at thousands of regulatory loci bound by transcription factors (TFs). Our results explain the conservation of intermediate methylation states at regulatory loci by showing that the intermediate methylation reflects the relative frequencies of fully methylated and fully unmethylated epialleles. SD-ASM is explainable by different relative frequencies of methylated and unmethylated epialleles for the two alleles. The differences in epiallele frequency spectra of the alleles at thousands of TF-bound regulatory loci correlated with the differences in alleles’ affinities for TF binding, which suggests a mechanistic explanation for SD-ASM. We observed an excess of rare variants among those showing SD-ASM, which suggests that an average human genome harbors at least ~200 detrimental rare variants that also show SD-ASM. The methylome’s sensitivity to genetic variation is unevenly distributed across the genome, which is consistent with buffering of housekeeping genes against the effects of random mutations. By contrast, less essential genes with tissue-specific expression patterns show sensitivity, thus providing opportunity for evolutionary innovation through changes in gene regulation. CONCLUSION Analysis of allelic epigenome maps provides a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, selective buffering of the regulatory circuitry against the effects of random mutations, and disease-associated genetic variation.

Published
2017

8. The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

Author

Sergio Abrignani, David Adams, Melanie de Almeida, ALTUCCI, Lucia, Viren Amin, Ido Amit, Stylianos E, Antonarakis, Samuel Aparicio, Takahiro Arima, Laura Arrigoni, Rob Arts, Vahid Asnafi, Manel Esteller, Jae Bum Bae, Kevin Bassler, Stephan Beck, Benjamin Berkman, Bradley E, Bernstein, Mikhail Bilenky, Adrian Bird, Christoph Bock, Bernhard Boehm, Guillaume Bourque, Charles E, Breeze, Benedikt Brors, David Bujold, Oliver Burren, Marion J, Bussemakers, Adam Butterworth, Elias Campo, Enrique Carrillo de Santa Pau, Lisa Chadwick, Kui Ming Chan, Wei Chen, Tom H, Cheung, Luca Chiapperino, Nak Hyen Choi, Ho Ryun Chung, Laura Clarke, Joseph M, Connors, Philippe Cronet, John Danesh, Manolis Dermitzakis, Gerard Drewes, Pawel Durek, Stephanie Dyke, Tomasz Dylag, Connie J, Eaves, Peter Ebert, Roland Eils, Jürgen Eils, Catherine A, Ennis, Tariq Enver, Elise A, Feingold, Bärbel Felder, Anne Ferguson Smith, Jude Fitzgibbon, Paul Flicek, Roger S, Y, Foo, Peter Fraser, Mattia Frontini, Eileen Furlong, Sitanshu Gakkhar, Nina Gasparoni, Gilles Gasparoni, Daniel H, Geschwind, Petar Glažar, Thomas Graf, Frank Grosveld, Xin Yuan Guan, Roderic Guigo, Ivo G, Gut, Alf Hamann, Bok Ghee Han, R, Alan Harris, Simon Heath, Kristian Helin, Jan G, Hengstler, Alireza Heravi Moussavi, Karl Herrup, Steven Hill, Jason A, Hilton, Benjamin C, Hitz, Bernhard Horsthemke, Ming Hu, Joo Yeon Hwang, Nancy Y, Takashi Ito, Biola Maria Javierre, Sasa Jenko, Thomas Jenuwein, Yann Joly, Steven J, M, Jones, Yae Kanai, Hee Gyung Kang, Aly Karsan, Alexandra K, Kiemer, Song Cheol Kim, Bong Jo Kim, Hyeon Hoe Kim, Hiroshi Kimura, Sarah Kinkley, Filippos Klironomos, In Uk Koh, Myrto Kostadima, Christopher Kressler, Roman Kreuzhuber, Anshul Kundaje, Ralf Küppers, Carolyn Larabell, Paul Lasko, Mark Lathrop, S, Lee, Suman Lee, Hans Lehrach, Elsa Leitão, Thomas Lengauer, Åke Lernmark, David Leslie, Gilberto K, K, Leung, Danny Leung, Markus Loeffler, Yussanne Ma, Antonello Mai, Thomas Manke, Eric R, Marcotte, Marco A, Marra, Joost H, A, Martens, Jose Ignacio Martin Subero, Karen Maschke, Christoph Merten, Aleksandar Milosavljevic, Saverio Minucci, Totai Mitsuyama, Richard A, Moore, Fabian Müller, Andrew J, Mungall, Mihai G, Netea, Karl Nordström, Irene Norstedt, Hiroaki Okae, Vitor Onuchic, Francis Ouellette, Willem Ouwehand, Massimiliano Pagani, Vera Pancaldi, Thomas Pap, Tomi Pastinen, Ronak Patel, Dirk S, Paul, Michael J, Pazin, Pier Giuseppe Pelicci, Anthony G, Phillips, Julia Polansky, Bo Porse, J, Andrew Pospisilik, Shyam Prabhakar, Dena C, Procaccini, Andreas Radbruch, Nikolaus Rajewsky, Vardham Rakyan, Wolf Reik, Bing Ren, David Richardson, Andreas Richter, Daniel Rico, David J, Roberts, Philip Rosenstiel, Mark Rothstein, Abdulrahman Salhab, Hiroyuki Sasaki, John S, Satterlee, Sascha Sauer, Claudia Schacht, Florian Schmidt, Gerd Schmitz, Stefan Schreiber, Christopher Schröder, Dirk Schübeler, Joachim L, Schultze, Ronald P, Schulyer, Marcel Schulz, Martin Seifert, Katsuhiko Shirahige, Reiner Siebert, Thomas Sierocinski, Laura Siminoff, Anupam Sinha, Nicole Soranzo, Salvatore Spicuglia, Mikhail Spivakov, Christian Steidl, Seth Strattan, Michael Stratton, Peter Südbeck, Hao Sun, Narumi Suzuki, Yutaka Suzuki, Amos Tanay, David Torrents, Frederick L, Tyson, Thomas Ulas, Sebastian Ullrich, Toshikazu Ushijima, Alfonso Valencia, Edo Vellenga, Martin Vingron, Chris Wallace, Stefan Wallner, Jörn Walter, Huating Wang, Stephanie Weber, Nina Weiler, Andreas Weller, Andrew Weng, Steven Wilder, Sam M, Wiseman, Angela R, Zhenguo Wu, Jieyi Xiong, Yasuhiro Yamashita, Xinyi Yang, Desmond Y, Yap, Kevin Y, Yip, Stephen Yip, Jae Il Yoo, Daniel Zerbino, Gideon Zipprich, Antonarakis, Stylianos, International Human Epigenome Consortium, [0000-0002-8682-6748], Apollo - University of Cambridge Repository, Centro Nacional de Análisi Genómico (CNAG), Centro Nacional de Análisis Genómico, Radboud University Medical Center [Nijmegen], Technologies avancées pour le génôme et la clinique (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Hematology, University of Groningen and University Medical Center Groningen, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Universitat de Barcelona, Sergio, Abrignani, David, Adam, Melanie de, Almeida, Altucci, Lucia, Viren, Amin, Ido, Amit, Stylianos, E, Antonarakis, Samuel, Aparicio, Takahiro, Arima, Laura, Arrigoni, Rob, Art, Vahid, Asnafi, Manel, Esteller, Jae Bum, Bae, Kevin, Bassler, Stephan, Beck, Benjamin, Berkman, Bradley, E, Bernstein, Mikhail, Bilenky, Adrian, Bird, Christoph, Bock, Bernhard, Boehm, Guillaume, Bourque, Charles, E, Breeze, Benedikt, Bror, David, Bujold, Oliver, Burren, Marion, J, Bussemakers, Adam, Butterworth, Elias, Campo, Enrique Carrillo de Santa, Pau, Lisa, Chadwick, Kui Ming, Chan, Wei, Chen, Tom, H, Cheung, Luca, Chiapperino, Nak Hyen, Choi, Ho Ryun, Chung, Laura, Clarke, Joseph, M, Connors, Philippe, Cronet, John, Danesh, Manolis, Dermitzaki, Gerard, Drewe, Pawel, Durek, Stephanie, Dyke, Tomasz, Dylag, Connie, J, Eaves, Peter, Ebert, Roland, Eil, Jürgen, Eil, Catherine, A, Ennis, Tariq, Enver, Elise, A, Feingold, Bärbel, Felder, Anne Ferguson, Smith, Jude, Fitzgibbon, Paul, Flicek, Roger, S, Y, Foo, Peter, Fraser, Mattia, Frontini, Eileen, Furlong, Sitanshu, Gakkhar, Nina, Gasparoni, Gilles, Gasparoni, Daniel, H, Geschwind, Petar, Glažar, Thomas, Graf, Frank, Grosveld, Xin Yuan, Guan, Roderic, Guigo, Ivo, G, Gut, Alf, Hamann, Bok Ghee, Han, R, Alan, Harri, Simon, Heath, Kristian, Helin, Jan, G, Hengstler, Alireza Heravi, Moussavi, Karl, Herrup, Steven, Hill, Jason, A, Hilton, Benjamin, C, Hitz, Bernhard, Horsthemke, Ming, Hu, Joo Yeon, Hwang, Nancy, Y, Ip, Takashi, Ito, Biola Maria, Javierre, Sasa, Jenko, Thomas, Jenuwein, Yann, Joly, Steven, J, M, Jone, Yae, Kanai, Hee Gyung, Kang, Aly, Karsan, Alexandra, K, Kiemer, Song Cheol, Kim, Bong Jo, Kim, Hyeon Hoe, Kim, Hiroshi, Kimura, Sarah, Kinkley, Filippos, Klironomo, In Uk, Koh, Myrto, Kostadima, Christopher, Kressler, Roman, Kreuzhuber, Anshul, Kundaje, Ralf, Küpper, Carolyn, Larabell, Paul, Lasko, Mark, Lathrop, S, Lee, Suman, Lee, Hans, Lehrach, Elsa, Leitão, Thomas, Lengauer, Åke, Lernmark, David, Leslie, Gilberto, K, K, Leung, Danny, Leung, Markus, Loeffler, Yussanne, Ma, Antonello, Mai, Thomas, Manke, Eric, R, Marcotte, Marco, A, Marra, Joost, H, A, Marten, Jose Ignacio Martin, Subero, Karen, Maschke, Christoph, Merten, Aleksandar, Milosavljevic, Saverio, Minucci, Totai, Mitsuyama, Richard, A, Moore, Fabian, Müller, Andrew, J, Mungall, Mihai, G, Netea, Karl, Nordström, Irene, Norstedt, Hiroaki, Okae, Vitor, Onuchic, Francis, Ouellette, Willem, Ouwehand, Massimiliano, Pagani, Vera, Pancaldi, Thomas, Pap, Tomi, Pastinen, Ronak, Patel, Dirk, S, Paul, Michael, J, Pazin, Pier Giuseppe, Pelicci, Anthony, G, Phillips, Julia, Polansky, Bo, Porse, J, Andrew, Pospisilik, Shyam, Prabhakar, Dena, C, Procaccini, Andreas, Radbruch, Nikolaus, Rajewsky, Vardham, Rakyan, Wolf, Reik, Bing, Ren, David, Richardson, Andreas, Richter, Daniel, Rico, David, J, Roberts, Philip, Rosenstiel, Mark, Rothstein, Abdulrahman, Salhab, Hiroyuki, Sasaki, John, S, Satterlee, Sascha, Sauer, Claudia, Schacht, Florian, Schmidt, Gerd, Schmitz, Stefan, Schreiber, Christopher, Schröder, Dirk, Schübeler, Joachim, L, Schultze, Ronald, P, Schulyer, Marcel, Schulz, Martin, Seifert, Katsuhiko, Shirahige, Reiner, Siebert, Thomas, Sierocinski, Laura, Siminoff, Anupam, Sinha, Nicole, Soranzo, Salvatore, Spicuglia, Mikhail, Spivakov, Christian, Steidl, Seth, Strattan, Michael, Stratton, Peter, Südbeck, Hao, Sun, Narumi, Suzuki, Yutaka, Suzuki, Amos, Tanay, David, Torrent, Frederick, L, Tyson, Thomas, Ula, Sebastian, Ullrich, Toshikazu, Ushijima, Alfonso, Valencia, Edo, Vellenga, Martin, Vingron, Chris, Wallace, Stefan, Wallner, Jörn, Walter, Huating, Wang, Stephanie, Weber, Nina, Weiler, Andreas, Weller, Andrew, Weng, Steven, Wilder, Sam, M, Wiseman, Angela, R, Wu, Zhenguo, Wu, Jieyi, Xiong, Yasuhiro, Yamashita, Xinyi, Yang, Desmond, Y, Yap, Kevin, Y, Yip, Stephen, Yip, Jae Il, Yoo, Daniel, Zerbino, and Gideon, Zipprich

Subjects
Epigenomics, 0301 basic medicine, [SDV]Life Sciences [q-bio], ADN, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Medizin, Biology, Genoma humà, General Biochemistry, Genetics and Molecular Biology, Epigenesis, Genetic, World Wide Web, 03 medical and health sciences, Human health, 0302 clinical medicine, Blueprint, Databases, Genetic, Humans, Disease, ddc:576.5, DNA methylation, databases, genetic, disease, histone code, humans, epigenesis, genetic, epigenomics, genome, human, biochemistry, genetics and molecular biology (all), Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Genetics, 0303 health sciences, Human genome, Genome, Human, DNA, Epigenome, DNA Methylation, Human cell, Epigenètica, Histone Code, 030104 developmental biology, 030220 oncology & carcinogenesis, Epigenetics
Abstract

Item does not contain fulltext The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalog of high-resolution reference epigenomes of major primary human cell types. The studies now presented (see the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic datasets to gain insights in the epigenetic control of cell states relevant for human health and disease. PAPERCLIP.

Published
2016
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9. Epigenomic footprints across 111 reference epigenomes reveal tissue-specific epigenetic regulation of lincRNAs

Author

R. Alan Harris, Cristian Coarfa, Kevin Riehle, Vitor Onuchic, Andrew R. Jackson, Sai Lakshmi Subramanian, Aleksandar Milosavljevic, Tim Charnecki, Viren Amin, and Sameer Paithankar

Subjects
Epigenomics, Cellular differentiation, Polycomb-Group Proteins, General Physics and Astronomy, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Epigenesis, Genetic, 03 medical and health sciences, 0302 clinical medicine, Polycomb-group proteins, Humans, Regulatory Elements, Transcriptional, Epigenetics, Embryonic Stem Cells, 030304 developmental biology, Epigenesis, Genetics, 0303 health sciences, Multidisciplinary, Cell Differentiation, General Chemistry, Embryonic stem cell, Chromatin, Phenotype, Organ Specificity, 030220 oncology & carcinogenesis, H3K4me3, RNA, Long Noncoding
Abstract

Tissue-specific expression of lincRNAs suggests developmental and cell-type-specific functions, yet tissue specificity was established for only a small fraction of lincRNAs. Here, by analysing 111 reference epigenomes from the NIH Roadmap Epigenomics project, we determine tissue-specific epigenetic regulation for 3,753 (69% examined) lincRNAs, with 54% active in one of the 14 cell/tissue clusters and an additional 15% in two or three clusters. A larger fraction of lincRNA TSSs is marked in a tissue-specific manner by H3K4me1 than by H3K4me3. The tissue-specific lincRNAs are strongly linked to tissue-specific pathways and undergo distinct chromatin state transitions during cellular differentiation. Polycomb-regulated lincRNAs reside in the bivalent state in embryonic stem cells and many of them undergo H3K27me3-mediated silencing at early stages of differentiation. The exquisitely tissue-specific epigenetic regulation of lincRNAs and the assignment of a majority of them to specific tissue types will inform future studies of this newly discovered class of genes., Tissue-specific functions have been established for some lincRNAs. Here, by analysing 111 reference epigenomes from the NIH Roadmap Epigenomics project, the authors report tissue-specific epigenomic regulation of 3,753 lincRNAs and their strong connection with tissue-specific pathways.

Published
2015
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10. Mining the Epigenetic Landscape

Author

Viren Amin, Vitor Onuchic, and Aleksandar Milosavljevic

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Surface mining, Computational epigenetics, Profiling (information science), Epigenetics, Cancer epigenetics, Biology, Data science, Cancer Etiology, Epigenomics
Abstract

With improvements in epigenomic profiling assays and with increasing amounts of epigenomic data in public repositories, new opportunities are available to understand normal epigenomes and their cancer-related perturbations. Nascent comparative epigenomic analyses have the potential to provide insights into cancer etiology, identify clinically relevant cancer biomarkers and therapeutic targets. Much of the epigenomic data generated by large consortium projects and small projects are now publicly available at epigenomic data portals. Several computational tools and methodological framework are developed to perform comparative epigenomic analysis. However, the methods for comparative epigenomic analysis are in their infancy and numerous challenges are faced when mining heterogeneous epigenomic datasets. In this chapter, we describe types of epigenomic data available in public repositories, epigenomic tools for comparative analysis, and address challenges faced when mining epigenomic datasets. We also illustrate the utility of mining epigenomic data by providing use cases that utilize publicly available data and tools to provide example into types of analysis that are possible.

Published
2015
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11. List of Contributors

Author

Bryce K. Allen, Donat Alpar, Viren Amin, Fazila Asmar, Nagi G. Ayad, Anne-Marie Baird, Louise J. Barber, Becky A.S. Bibby, Emma Bolderson, Philippe Bouvet, Frédéric Catez, Leandro Cerchietti, Snehajyoti Chatterjee, Taiping Chen, Andreas I. Constantinou, Stuart J. Conway, Dashyant Dhanak, Jean-Jacques Diaz, Marc Diederich, Xinmin Fan, Brendan Ffrench, Michael F. Gallagher, Marco Gerlinger, Steven D. Gore, Steven G. Gray, Kirsten Grønbæk, David S. Hewings, Holger Heyn, Zhe Jin, Stephanie Kaypee, Yutaka Kondo, Tapas K. Kundu, Florian Laforêts, Matthew W. Lawless, Stephen G. Maher, Somnath Mandal, Virginie Marcel, Aleksandar Milosavljevic, Hannah L. Moody, Atsushi Natsume, Thomas B. Nicholson, Kenneth J. O’Byrne, Shane O’Grady, John J. O’Leary, Fumiharu Ohka, Vitor Onuchic, Vineet Pande, Clara Penas, Li-Xia Peng, Benet Pera, Antoinette Sabrina Perry, Olga Piskareva, Chara A. Pitta, David J. Pocalyko, Thomas Prebet, Chao-Nan Qian, Glen Reid, Derek J. Richard, Timothy P.C. Rooney, Michael Schnekenburger, Alexandra Søgaard, Peter Staller, Raymond L. Stallings, Vasileios Stathias, Gabriel Therizols, Nicolas Veland, Casey M. Wright, Xiaojing Zhang, Wei Zhu, and Jiaqi Qian

Published
2015
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12. Integrative analysis of 111 reference human epigenomes

Author

Daofeng Li, Tim R. Mercer, Wei Li, Lisa Helbling Chadwick, Jesse R. Dixon, Pouya Kheradpour, Joseph F. Costello, Pradipta R. Ray, John W. Whitaker, Peggy J. Farnham, Angela Tam, Vitor Onuchic, Robert A. Waterland, Misha Bilenky, James A. Thomson, Zhizhuo Zhang, Yaping Liu, Gerald Quon, Andrew J. Mungall, Steven J.M. Jones, Bradley E. Bernstein, Alexander Meissner, Melina Claussnitzer, Charles B. Epstein, Andreas R. Pfenning, Li-Huei Tsai, Laurie A. Boyer, Angela Yen, Ting Wang, Rajinder Kaul, Alireza Heravi-Moussavi, Danny Leung, Noam Shoresh, Michael T. McManus, Michael Stevens, John A. Stamatoyannopoulos, Mukul S. Bansal, Thea D. Tlsty, Susan J. Fisher, Manolis Kellis, Michael Q. Zhang, Aleksandar Milosavljevic, Viren Amin, Martin Hirst, Matthew D. Schultz, Joseph R. Ecker, Xinchen Wang, Jie Wu, Marco A. Marra, Kyle Siebenthall, Wei Wang, Ashwinikumar Kulkarni, Peter J. Sabo, R. Scott Hansen, Jianrong Wang, Michael J. Ziller, Richard A. Moore, Shane Neph, Richard C Sallari, Robert E. Thurman, Paz Polak, Wei Xie, Eric Chuah, Jason Ernst, Bing Ren, Nisha Rajagopal, Anshul Kundaje, Xin Zhou, Yi-Chieh Wu, Shamil R. Sunyaev, Ginell Elliott, Philippe Gascard, Soheil Feizi, Chibo Hong, R. Alan Harris, Ah Ram Kim, Philip L. De Jager, Rosa Karlic, R. David Hawkins, Matthew L. Eaton, Ryan Lister, Rebecca F. Lowdon, Annaick Carles, Elizabeta Gjoneska, David Haussler, Abhishek Sarkar, Nicholas A Sinnott-Armstrong, Wouter Meuleman, Lucas D. Ward, Kai How Farh, Richard Sandstrom, Arthur E. Beaudet, Theresa K. Canfield, Cristian Coarfa, Bo Zhang, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Biology, Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Picower Institute for Learning and Memory, Kundaje, Anshul, Meuleman, Wouter, Ernst, Jason, Yen, Angela, Kheradpour, Pouya, Zhang, Zhizhuo, Wang, Jianrong, Ward, Lucas D., Sarkar, Abhishek Kulshreshtha, Quon, Gerald, Eaton, Matthew Lucas, Wu, Yi-Chieh, Pfenning, Andreas R., Wang, Xinchen, Claussnitzer, Melina, Liu, Yaping, Bansal, Mukul S., Feizi-Khankandi, Soheil, Kim, Ah Ram, Cowper Sal-lari, Richard, Sinnott-Armstrong, Nicholas A., Kellis, Manolis, Boyer, Laurie, Gjoneska, Elizabeta, and Tsai, Li-Huei

Subjects
Epigenomics, Datasets as Topic, ATAC-seq, Computational biology, Biology, Article, Epigenesis, Genetic, Histones, Reference Values, Computational epigenetics, Chromosomes, Human, Humans, Cell Lineage, Epigenetics, Cells, Cultured, Genetics, Regulation of gene expression, Multidisciplinary, Base Sequence, Genome, Human, Genetic Variation, DNA, Epigenome, DNA Methylation, Chromatin, Human genetics, Enhancer Elements, Genetic, Organ Specificity, RNA, Human genome, chromatin, histone, epigenome, tissue specificity, Genome-Wide Association Study
Abstract

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease., National Human Genome Research Institute (U.S.) (RC1HG005334), National Human Genome Research Institute (U.S.) (R01HG004037), National Human Genome Research Institute (U.S.) (R01HG004037-S1), National Human Genome Research Institute (U.S.) (RO1NS078839), National Science Foundation (U.S.) (CAREER Award 1254200)

Published
2014

13. ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Author

Felipe Amado, Alan Mitchell Durham, André Yoshiaki Kashiwabara, Rafael Mathias, Ígor Bonadio, and Vitor Onuchic

Subjects
Computer science, Markov process, Markov model, Cellular and Molecular Neuroscience, symbols.namesake, Genetics, Hidden Markov model, Molecular Biology, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, Ecology, Markov chain, business.industry, Variable-order Markov model, Maximum-entropy Markov model, Computational Biology, Pattern recognition, Bayes Theorem, FRAMEWORKS, Variable-order Bayesian network, Markov Chains, Computational Theory and Mathematics, lcsh:Biology (General), Modeling and Simulation, symbols, CpG Islands, Forward algorithm, Artificial intelligence, business, Sequence Analysis, Research Article
Abstract

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

Published
2013

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