Toward kingdom-wide analyses of gene expression.

Gene Expression Profiling Sequence Analysis, RNA Software Gene Expression Transcriptome / genetics
coexpression comparative evolution gene function transcriptomics

Journal

Trends in plant science
ISSN: 1878-4372
Titre abrégé: Trends Plant Sci
Pays: England
ID NLM: 9890299

Informations de publication

Date de publication:
02 2023
Historique:
received: 29 04 2022
revised: 22 09 2022
accepted: 30 09 2022
pubmed: 8 11 2022
medline: 24 1 2023
entrez: 7 11 2022
Statut: ppublish

Résumé

Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.

Identifiants

DOI: 10.1016/j.tplants.2022.09.007 PMID: 36344371
pubmed: 36344371
pii: S1360-1385(22)00269-2
doi: 10.1016/j.tplants.2022.09.007
pii:
doi:

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

235-249

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests No interests are declared.

Auteurs

Irene Julca (I)

School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.

Qiao Wen Tan (QW)

School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.

Marek Mutwil (M)

School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore. Electronic address: mutwil@ntu.edu.sg.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software

Systematical characterization of Rab7 gene family in Gossypium and potential functions of GhRab7B3-A gene in drought tolerance.

Mengyuan Yan, Zhiwei Dong, Tian Pan et al.
1.00
Drought Resistance Gene Expression Profiling Gene Expression Regulation, Plant Gossypium Multigene Family

A cytoplasmic osmosensing mechanism mediated by molecular crowding-sensitive DCP5.

Zhenyu Wang, Qiuhua Yang, Dan Zhang et al.
1.00
Arabidopsis Arabidopsis Proteins Osmotic Pressure Cytoplasm RNA, Messenger

Classifications MeSH

questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de profil d'expression de gènes › Toward kingdom-wide analyses of gene expression.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ARN › Toward kingdom-wide analyses of gene expression.
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Logiciel › Toward kingdom-wide analyses of gene expression.
questionsmedicales.fr › Phénomènes génétiques › Expression des gènes › Toward kingdom-wide analyses of gene expression.
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Transcriptome › Toward kingdom-wide analyses of gene expression.