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Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads
BACKGROUND: Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of refer...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3152782/ https://www.ncbi.nlm.nih.gov/pubmed/21106091 http://dx.doi.org/10.1186/1471-2164-11-663 |
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author | Martin, Jeffrey Bruno, Vincent M Fang, Zhide Meng, Xiandong Blow, Matthew Zhang, Tao Sherlock, Gavin Snyder, Michael Wang, Zhong |
author_facet | Martin, Jeffrey Bruno, Vincent M Fang, Zhide Meng, Xiandong Blow, Matthew Zhang, Tao Sherlock, Gavin Snyder, Michael Wang, Zhong |
author_sort | Martin, Jeffrey |
collection | PubMed |
description | BACKGROUND: Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied. RESULTS: Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95%) and reconstruct full-length genes for the majority of the existing gene models (54.3%). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics. CONCLUSIONS: These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome. |
format | Online Article Text |
id | pubmed-3152782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31527822011-08-10 Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads Martin, Jeffrey Bruno, Vincent M Fang, Zhide Meng, Xiandong Blow, Matthew Zhang, Tao Sherlock, Gavin Snyder, Michael Wang, Zhong BMC Genomics Methodology Article BACKGROUND: Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied. RESULTS: Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95%) and reconstruct full-length genes for the majority of the existing gene models (54.3%). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics. CONCLUSIONS: These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome. BioMed Central 2010-11-24 /pmc/articles/PMC3152782/ /pubmed/21106091 http://dx.doi.org/10.1186/1471-2164-11-663 Text en Copyright ©2010 Martin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Martin, Jeffrey Bruno, Vincent M Fang, Zhide Meng, Xiandong Blow, Matthew Zhang, Tao Sherlock, Gavin Snyder, Michael Wang, Zhong Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title | Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title_full | Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title_fullStr | Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title_full_unstemmed | Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title_short | Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads |
title_sort | rnnotator: an automated de novo transcriptome assembly pipeline from stranded rna-seq reads |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3152782/ https://www.ncbi.nlm.nih.gov/pubmed/21106091 http://dx.doi.org/10.1186/1471-2164-11-663 |
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