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Design, execution, and interpretation of plant RNA-seq analyses
Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-s...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348879/ https://www.ncbi.nlm.nih.gov/pubmed/37457354 http://dx.doi.org/10.3389/fpls.2023.1135455 |
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author | Upton, Racheal N. Correr, Fernando H. Lile, Jared Reynolds, Gillian L. Falaschi, Kira Cook, Jason P. Lachowiec, Jennifer |
author_facet | Upton, Racheal N. Correr, Fernando H. Lile, Jared Reynolds, Gillian L. Falaschi, Kira Cook, Jason P. Lachowiec, Jennifer |
author_sort | Upton, Racheal N. |
collection | PubMed |
description | Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained. |
format | Online Article Text |
id | pubmed-10348879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103488792023-07-15 Design, execution, and interpretation of plant RNA-seq analyses Upton, Racheal N. Correr, Fernando H. Lile, Jared Reynolds, Gillian L. Falaschi, Kira Cook, Jason P. Lachowiec, Jennifer Front Plant Sci Plant Science Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained. Frontiers Media S.A. 2023-06-30 /pmc/articles/PMC10348879/ /pubmed/37457354 http://dx.doi.org/10.3389/fpls.2023.1135455 Text en Copyright © 2023 Upton, Correr, Lile, Reynolds, Falaschi, Cook and Lachowiec https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Upton, Racheal N. Correr, Fernando H. Lile, Jared Reynolds, Gillian L. Falaschi, Kira Cook, Jason P. Lachowiec, Jennifer Design, execution, and interpretation of plant RNA-seq analyses |
title | Design, execution, and interpretation of plant RNA-seq analyses |
title_full | Design, execution, and interpretation of plant RNA-seq analyses |
title_fullStr | Design, execution, and interpretation of plant RNA-seq analyses |
title_full_unstemmed | Design, execution, and interpretation of plant RNA-seq analyses |
title_short | Design, execution, and interpretation of plant RNA-seq analyses |
title_sort | design, execution, and interpretation of plant rna-seq analyses |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348879/ https://www.ncbi.nlm.nih.gov/pubmed/37457354 http://dx.doi.org/10.3389/fpls.2023.1135455 |
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