Literature DB >> 28690910

Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data.

Yong Wang1,2, Rui Jiang1,3, Wing Hung Wong1.   

Abstract

Cell packs a lot of genetic and regulatory information through a structure known as chromatin, i.e. DNA is wrapped around histone proteins and is tightly packed in a remarkable way. To express a gene in a specific coding region, the chromatin would open up and DNA loop may be formed by interacting enhancers and promoters. Furthermore, the mediator and cohesion complexes, sequence-specific transcription factors, and RNA polymerase II are recruited and work together to elaborately regulate the expression level. It is in pressing need to understand how the information, about when, where, and to what degree genes should be expressed, is embedded into chromatin structure and gene regulatory elements. Thanks to large consortia such as Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomic projects, extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell types. This rich data offer an exciting opportunity to model the causal regulatory relationship. Here, we will review the current experimental approaches, foundational data, computational problems, interpretive frameworks, and integrative models that will enable the accurate interpretation of regulatory landscape. Particularly, we will discuss the efforts to organize, analyze, model, and integrate the DNA accessibility data, transcriptional data, and functional genomic regions together. We believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

Entities:  

Keywords:  DNA accessibility; data integration; gene regulatory network; open chromatin; statistical model; transcription factor colocalization

Year:  2016        PMID: 28690910      PMCID: PMC5501464          DOI: 10.1093/nsr/nww025

Source DB:  PubMed          Journal:  Natl Sci Rev        ISSN: 2053-714X            Impact factor:   17.275


  52 in total

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Journal:  Nature       Date:  2012-09-06       Impact factor: 49.962

9.  Integrative analysis of 111 reference human epigenomes.

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Journal:  Nature       Date:  2015-02-19       Impact factor: 69.504

10.  Integrating human omics data to prioritize candidate genes.

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Journal:  BMC Med Genomics       Date:  2013-12-18       Impact factor: 3.063

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Review 4.  From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.

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6.  Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

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7.  Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.

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