RT states: Systematic annotation of the human genome using cell type-specific replication timing programs

Axel Poulet, Ben Li, Tristan Dubos, Juan Carlos Rivera-Mulia, David M. Gilbert, Zhaohui S. Qin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2167-2176
Number of pages10
Issue number13
StatePublished - Jul 1 2019

Bibliographical note

Funding Information:
This work was supported by National Institutes of Health [P01 GM085354].

Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press. All rights reserved.


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