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  EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications

Sahu, A., Li, N., Dunkel, I., & Chung, H. (2020). EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications. Epigenetics & Chromatin, 13: 13:20. doi:10.1186/s13072-020-00341-z.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-4E1A-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-4E1B-6
Genre: Journal Article

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 Creators:
Sahu, Anshupa 1, 2, Author
Li, Na1, 3, Author
Dunkel, Ilona1, Author              
Chung, Ho‑Ryun 1, 2, Author
Affiliations:
1Regulatory Networks in Stem Cells (Edda G. Schulz), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2117286              
2Institute for Medical Bioinformatics and Biostatistics, Philipps University of Marburg, 35037 Marburg, Germany., ou_persistent22              
3Guangzhou Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guang‑zhou 510623, China, ou_persistent22              

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Free keywords: Transcription, Epigenetics, Histone modifications, Hidden Markov model, Transcript identification
 Abstract: Background:Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA‑seq that, however, require large quantities of mRNA rendering the identification of inherently unstable TUs, e.g. miRNA precursors, dif‑ficult. This problem can be alleviated by chromatin‑based approaches due to a correlation between histone modifica‑tions and transcription.Results:Here, we introduce EPIGENE, a novel chromatin segmentation method for the identification of active TUs using transcription‑associated histone modifications. Unlike the existing chromatin segmentation approaches, EPI‑GENE uses a constrained, semi‑supervised multivariate hidden Markov model (HMM) that models the observed com‑bination of histone modifications using a product of independent Bernoulli random variables, to identify active TUs. Our results show that EPIGENE can identify genome‑wide TUs in an unbiased manner. EPIGENE‑predicted TUs show an enrichment of RNA Polymerase II at the transcription start site and in gene body indicating that they are indeed transcribed. Comprehensive validation using existing annotations revealed that 93% of EPIGENE TUs can be explained by existing gene annotations and 5% of EPIGENE TUs in HepG2 can be explained by microRNA annotations. EPIGENE outperformed the existing RNA‑seq‑based approaches in TU prediction precision across human cell lines. Finally, we identified 232 novel TUs in K562 and 43 novel cell‑specific TUs all of which were supported by RNA Polymerase II ChIP‑seq and Nascent RNA‑seq data.Conclusion:We demonstrate the applicability of EPIGENE to identify genome‑wide active TUs and to provide valu‑able information about unannotated TUs. EPIGENE is an open‑source method and is freely available at: https ://github.com/imbbL ab/EPIGENE.

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Language(s): eng - English
 Dates: 2020-03-282020-04-07
 Publication Status: Published online
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 Rev. Method: -
 Identifiers: DOI: 10.1186/s13072-020-00341-z
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Title: Epigenetics & Chromatin
Source Genre: Journal
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Publ. Info: London : BioMed Central
Pages: - Volume / Issue: 13 Sequence Number: 13:20 Start / End Page: - Identifier: CoNE: https://pure.mpg.de/cone/journals/resource/1756-8935