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

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.

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

“…With increasing data on drug responses becoming available over time, and extended matrix factorization models to utilize the above heterogeneous data, we hope this matrix factorization based approach will have much better predictive power. Besides, our approach can be applied to other research fields such as modelling the causal regulatory network by integrating chromatin accessibility and transcriptome data in matched samples, which are deposited in Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomic projects [ 34 ].…”

From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

“…We aim to provide an annotated guide for ATAC-seq data analysis instead of an exhaustive collection of tools. Previous reviews regarding ATAC-seq data analysis have focused mainly on peak callers and modeling regulatory networks [37,38], but a systematic review covering major parts of ATAC-seq data analysis is urgently needed. This review will cover the four most important steps listed in the flowchart (Fig.…”

Global Role of Crop Genomics in the Face of Climate Change

“…In the last few years, a significant progress has been made in identification of plant CREs with studies of the model plant species Arabidopsis as well as rice, maize, and cotton ( Zhang et al, 2012 ; Pajoro et al, 2014 ; Zhu et al, 2015 ; Rodgers-Melnick et al, 2016 ; Oka et al, 2017 ; Wang et al, 2017 ; Bajic et al, 2018 ; Tannenbaum et al, 2018 ; Zhao et al, 2018a ; Yan et al, 2019 ). The rapid developments are due to adoption of DNase-Seq and ATAC-Seq techniques in plant research, which measure DNA “openness” as a proxy for the accessibility of DNA to transcription factors, RNA polymerase, and other protein complexes involved in gene expression ( Pajoro et al, 2014 ; Wang et al, 2016 ). Improved understanding of the function of the non-coding elements of the genome will provide a new, yet untapped pool of breeding targets.…”