Cargando…

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for...

Descripción completa

Detalles Bibliográficos
Autores principales: Srikakulam, Nagesh, Sridevi, Ganapathi, Pandi, Gopal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558114/
https://www.ncbi.nlm.nih.gov/pubmed/36246658
http://dx.doi.org/10.3389/fgene.2022.995072
_version_ 1784807375903916032
author Srikakulam, Nagesh
Sridevi, Ganapathi
Pandi, Gopal
author_facet Srikakulam, Nagesh
Sridevi, Ganapathi
Pandi, Gopal
author_sort Srikakulam, Nagesh
collection PubMed
description The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.
format Online
Article
Text
id pubmed-9558114
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95581142022-10-14 High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica Srikakulam, Nagesh Sridevi, Ganapathi Pandi, Gopal Front Genet Genetics The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9558114/ /pubmed/36246658 http://dx.doi.org/10.3389/fgene.2022.995072 Text en Copyright © 2022 Srikakulam, Sridevi and Pandi. 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 Genetics
Srikakulam, Nagesh
Sridevi, Ganapathi
Pandi, Gopal
High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title_full High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title_fullStr High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title_full_unstemmed High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title_short High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica
title_sort high-quality reference transcriptome construction improves rna-seq quantification in oryza sativa indica
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558114/
https://www.ncbi.nlm.nih.gov/pubmed/36246658
http://dx.doi.org/10.3389/fgene.2022.995072
work_keys_str_mv AT srikakulamnagesh highqualityreferencetranscriptomeconstructionimprovesrnaseqquantificationinoryzasativaindica
AT srideviganapathi highqualityreferencetranscriptomeconstructionimprovesrnaseqquantificationinoryzasativaindica
AT pandigopal highqualityreferencetranscriptomeconstructionimprovesrnaseqquantificationinoryzasativaindica