scholarly journals Rif1 regulates the replication timing domains on the human genome

2012 ◽  
Vol 31 (18) ◽  
pp. 3667-3677 ◽  
Author(s):  
Satoshi Yamazaki ◽  
Aii Ishii ◽  
Yutaka Kanoh ◽  
Masako Oda ◽  
Yasumasa Nishito ◽  
...  
Keyword(s):  
Human Genome ◽  
Replication Timing

scholarly journals Analyzing DNA Replication Timing in the Human Genome

2009 ◽  
Vol 23 (S1) ◽  
Author(s):  
Christopher Michael Taylor ◽  
Neerja Karnani ◽  
Ankit Malhotra ◽  
Gabriel Robins ◽  
Anindya Dutta
Keyword(s):  
Dna Replication ◽  
Human Genome ◽  
Replication Timing ◽  
Dna Replication Timing

p53-Dependent change in replication timing of the human genome

2007 ◽  
Vol 364 (2) ◽  
pp. 289-293 ◽  
Author(s):  
Yoshihisa Watanabe ◽  
Kiyoshi Shibata ◽  
Haruhiko Sugimura ◽  
Masato Maekawa
Keyword(s):  
Human Genome ◽  
Replication Timing ◽  
Dependent Change

scholarly journals Evidence for Sequential and Increasing Activation of Replication Origins along Replication Timing Gradients in the Human Genome

2011 ◽  
Vol 7 (12) ◽  
pp. e1002322 ◽  
Author(s):  
Guillaume Guilbaud ◽  
Aurélien Rappailles ◽  
Antoine Baker ◽  
Chun-Long Chen ◽  
Alain Arneodo ◽  
...  
Keyword(s):  
Human Genome ◽  
Replication Timing ◽  
Replication Origins

scholarly journals Replication timing of the human genome

2004 ◽  
Vol 13 (5) ◽  
pp. 575-575 ◽  
Author(s):  
K. Woodfine
Keyword(s):  
Human Genome ◽  
Replication Timing

scholarly journals Replication timing of the human genome

2003 ◽  
Vol 13 (2) ◽  
pp. 191-202 ◽  
Author(s):  
Kathryn Woodfine ◽  
Heike Fiegler ◽  
David M. Beare ◽  
John E. Collins ◽  
Owen T. McCann ◽  
...  
Keyword(s):  
Human Genome ◽  
Replication Timing

scholarly journals RT States: systematic annotation of the human genome using cell type-specific replication timing programs

2018 ◽  
Author(s):  
Axel Poulet ◽  
Ben Li ◽  
Tristan Dubos ◽  
Juan Carlos Rivera-Mulia ◽  
David M. Gilbert ◽  
...  
Keyword(s):  
Human Genome ◽  
Cell Fate ◽  
Developmental Stages ◽  
Replication Timing ◽  
Cell Types ◽  
Biological Properties ◽  
Biological Processes ◽  
Genomic Locus ◽  
Genome Wide ◽  
Fundamental Biological Process

ABSTRACTThe replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus. In the present study, we explored recurrent and spatially coherent combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We found that a Hidden Markov Model with 15 hidden states provide a good model to describe these genome-wide RT profiling data. Each of the hidden state represents a unique combination of RT profiles across different cell types which we refer to as “RT states”. To understand the biological properties of these RT states, we inspected their relationship with chromatin states, gene expression, functional annotation and 3D chromosomal organization. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome.AUTHOR SUMMARYThe replication timing (RT) program is an important cellular mechanism and has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows us to characterize the RT program in the entire human genome. Results from these experiments suggest that RT changes dynamically across different developmental stages. Since RT is such a fundamental biological process, we believe that the local RT program from a diverse set of cell types in various developmental stages can provide crucial biological insights for a genomic locus. In the present study, we explored combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We developed a statistical model consist of 15 “RT states” to describe these genome-wide RT profiling data. To understand the biological properties of these RT states, we inspected the relationship between RT states and other types of functional annotations of the genome. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome.

scholarly journals RT States: systematic annotation of the human genome using cell type-specific replication timing programs

2018 ◽  
Vol 35 (13) ◽  
pp. 2167-2176
Author(s):  
Axel Poulet ◽  
Ben Li ◽  
Tristan Dubos ◽  
Juan Carlos Rivera-Mulia ◽  
David M Gilbert ◽  
...  
Keyword(s):  
Human Genome ◽  
Cell Fate ◽  
Developmental Stages ◽  
Replication Timing ◽  
Cell Types ◽  
Biological Properties ◽  
Supplementary Information ◽  
Genomic Locus ◽  
Genome Wide ◽  
Fundamental Biological Process

Abstract Motivation The replication timing (RT) program has been linked to many key biological processes including cell fate commitment, 3D chromatin organization and transcription regulation. Significant technology progress now allows to characterize the RT program in the entire human genome in a high-throughput and high-resolution fashion. These experiments suggest that RT changes dynamically during development in coordination with gene activity. Since RT is such a fundamental biological process, we believe that an effective quantitative profile of the local RT program from a diverse set of cell types in various developmental stages and lineages can provide crucial biological insights for a genomic locus. Results In this study, we explored recurrent and spatially coherent combinatorial profiles from 42 RT programs collected from multiple lineages at diverse differentiation states. We found that a Hidden Markov Model with 15 hidden states provide a good model to describe these genome-wide RT profiling data. Each of the hidden state represents a unique combination of RT profiles across different cell types which we refer to as ‘RT states’. To understand the biological properties of these RT states, we inspected their relationship with chromatin states, gene expression, functional annotation and 3D chromosomal organization. We found that the newly defined RT states possess interesting genome-wide functional properties that add complementary information to the existing annotation of the human genome. Availability and implementation R scripts for inferring HMM models and Perl scripts for further analysis are available https://github.com/PouletAxel/script_HMM_Replication_timing. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals De novoidentification of replication-timing domains in the human genome by deep learning

2015 ◽  
Vol 32 (5) ◽  
pp. 641-649 ◽  
Author(s):  
Feng Liu ◽  
Chao Ren ◽  
Hao Li ◽  
Pingkun Zhou ◽  
Xiaochen Bo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Genome ◽  
Replication Timing

scholarly journals Multi-scale deep tensor factorization learns a latent representation of the human epigenome

2018 ◽  
Author(s):  
Jacob Schreiber ◽  
Timothy Durham ◽  
Jeffrey Bilmes ◽  
William Stafford Noble
Keyword(s):  
Gene Expression ◽  
Human Genome ◽  
Replication Timing ◽  
Cell Types ◽  
Factorization Method ◽  
Computational Genomics ◽  
Tensor Factorization ◽  
Multi Scale ◽  
Predict Gene Expression ◽  
Machine Learning Models

AbstractThe human epigenome has been experimentally characterized by measurements of protein binding, chromatin acessibility, methylation, and histone modification in hundreds of cell types. The result is a huge compendium of data, consisting of thousands of measurements for every basepair in the human genome. These data are difficult to make sense of, not only for humans, but also for computational methods that aim to detect genes and other functional elements, predict gene expression, characterize polymorphisms, etc. To address this challenge, we propose a deep neural network tensor factorization method, Avocado, that compresses epigenomic data into a dense, information-rich representation of the human genome. We use data from the Roadmap Epigenomics Consortium to demonstrate that this learned representation of the genome is broadly useful: first, by imputing epigenomic data more accurately than previous methods, and second, by showing that machine learning models that exploit this representation outperform those trained directly on epigenomic data on a variety of genomics tasks. These tasks include predicting gene expression, promoter-enhancer interactions, replication timing, and an element of 3D chromatin architecture. Our findings suggest the broad utility of Avocado’s learned latent representation for computational genomics and epigenomics.

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