TY - JOUR

T1 - Transcriptional regulation in plants

T2 - Using omics data to crack the cis-regulatory code

AU - Zemlyanskaya, Elena V.

AU - Dolgikh, Vladislav A.

AU - Levitsky, Victor G.

AU - Mironova, Victoria

N1 - Funding Information: The authors thank Tatyana Merkulova and Pavel Borodin for fruitful discussions and anonymous reviewers for valuable advice. This work was supported by the Russian Science Foundation , grant no. 20-14-00140 . Publisher Copyright: © 2021 Elsevier Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/10

Y1 - 2021/10

N2 - Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor–binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

AB - Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor–binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.

KW - ATAC-seq

KW - Binding site

KW - Chromatin

KW - Cis-regulatory syntax

KW - Composite cis-element

KW - Epigenome

KW - Integrative analysis

KW - Machine learning (ML)

KW - Multiomics

KW - Single-cell RNA-seq

KW - Transcription factor

KW - Transcriptome

UR - http://www.scopus.com/inward/record.url?scp=85107272376&partnerID=8YFLogxK

U2 - 10.1016/j.pbi.2021.102058

DO - 10.1016/j.pbi.2021.102058

M3 - Review article

C2 - 34098218

AN - SCOPUS:85107272376

VL - 63

SP - 102058

JO - Current Opinion in Plant Biology

JF - Current Opinion in Plant Biology

SN - 1369-5266

M1 - 102058

ER -