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 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 Understanding sequence conservation with deep learning

2017 ◽  
Author(s):  
Yi Li ◽  
Daniel Quang ◽  
Xiaohui Xie
Keyword(s):  
Deep Learning ◽  
Human Genome ◽  
Dna Sequences ◽  
Mammalian Species ◽  
Neural Net ◽  
Functional Elements ◽  
Sequence Element ◽  
Functional Roles ◽  
Convolution Kernels ◽  
Transcriptional Start Sites

AbstractMotivationComparing the human genome to the genomes of closely related mammalian species has been a powerful tool for discovering functional elements in the human genome. Millions of conserved elements have been discovered. However, understanding the functional roles of these elements still remain a challenge, especially in noncoding regions. In particular, it is still unclear why these elements are evolutionarily conserved and what kind of functional elements are encoded within these sequences.ResultsWe present a deep learning framework, called DeepCons, to uncover potential functional elements within conserved sequences. DeepCons is a convolutional neural net (CNN) that receives a short segment of DNA sequence as input and outputs the probability of the sequence of being evolutionary conserved. DeepCons utilizes hundreds of convolution kernels to detect features within DNA sequences, and automatically learns these kernels after training the CNN model using 887,577 conserved elements and a similar number of nonconserved elements in the human genome. On a balanced test dataset, DeepCons can achieve an accuracy of 75% in determining whether a sequence element is conserved or not, and the area under the ROC curve of 0.83, based on information from the human genome alone. We further investigate the properties of the learned kernels. Some kernels are directly related to well-known regulatory motifs corresponding to transcription factors. Many kernels show positional biases relative to transcriptional start sites or transcription end sites. But most of discovered kernels do not correspond to any known functional element, suggesting that they might represent unknown categories of functional elements. We also utilize DeepCons to annotate how changes at each individual nucleotide might impact the conservation properties of the surrounding sequences.AvailabilityThe source code of DeepCons and all the learned convolution kernels in motif format is publicly available online athttps://github.com/uci-cbcl/[email protected]

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