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RNA-eXpress annotates novel transcript features in RNA-seq data

Summary: Next-generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing...

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Detalles Bibliográficos
Autores principales: Forster, Samuel C., Finkel, Alexander M., Gould, Jodee A., Hertzog, Paul J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597146/
https://www.ncbi.nlm.nih.gov/pubmed/23396121
http://dx.doi.org/10.1093/bioinformatics/btt034
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author Forster, Samuel C.
Finkel, Alexander M.
Gould, Jodee A.
Hertzog, Paul J.
author_facet Forster, Samuel C.
Finkel, Alexander M.
Gould, Jodee A.
Hertzog, Paul J.
author_sort Forster, Samuel C.
collection PubMed
description Summary: Next-generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing a bias towards known genes, require specific experimental design and computational resources, or focus only on identification of splice variants (ignoring other biologically relevant transcribed features contained within the data that may be important for downstream analysis). Biologically relevant transcribed features also include large and small non-coding RNA, new transcription start sites, alternative promoters, RNA editing and processing of coding transcripts. Also, many existing solutions lack accessible interfaces required for wide scale adoption. We present a user-friendly, rapid and computation-efficient feature annotation framework (RNA-eXpress) that enables identification of transcripts and other genomic and transcriptional features independently of current annotations. RNA-eXpress accepts mapped reads in the standard binary alignment (BAM) format and produces a study-specific feature annotation in GTF format, comparison statistics, sequence extraction and feature counts. The framework is designed to be easily accessible while allowing advanced users to integrate new feature-identification algorithms through simple class extension, thus facilitating expansion to novel feature types or identification of study-specific feature types. Availability and implementation: RNA-eXpress software, source code, user manuals, supporting tutorials, developer guides and example data are available at http://www.rnaexpress.org. Contact: paul.hertzog@monash.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-35971462013-03-14 RNA-eXpress annotates novel transcript features in RNA-seq data Forster, Samuel C. Finkel, Alexander M. Gould, Jodee A. Hertzog, Paul J. Bioinformatics Applications Notes Summary: Next-generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing a bias towards known genes, require specific experimental design and computational resources, or focus only on identification of splice variants (ignoring other biologically relevant transcribed features contained within the data that may be important for downstream analysis). Biologically relevant transcribed features also include large and small non-coding RNA, new transcription start sites, alternative promoters, RNA editing and processing of coding transcripts. Also, many existing solutions lack accessible interfaces required for wide scale adoption. We present a user-friendly, rapid and computation-efficient feature annotation framework (RNA-eXpress) that enables identification of transcripts and other genomic and transcriptional features independently of current annotations. RNA-eXpress accepts mapped reads in the standard binary alignment (BAM) format and produces a study-specific feature annotation in GTF format, comparison statistics, sequence extraction and feature counts. The framework is designed to be easily accessible while allowing advanced users to integrate new feature-identification algorithms through simple class extension, thus facilitating expansion to novel feature types or identification of study-specific feature types. Availability and implementation: RNA-eXpress software, source code, user manuals, supporting tutorials, developer guides and example data are available at http://www.rnaexpress.org. Contact: paul.hertzog@monash.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-03-15 2013-02-08 /pmc/articles/PMC3597146/ /pubmed/23396121 http://dx.doi.org/10.1093/bioinformatics/btt034 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Forster, Samuel C.
Finkel, Alexander M.
Gould, Jodee A.
Hertzog, Paul J.
RNA-eXpress annotates novel transcript features in RNA-seq data
title RNA-eXpress annotates novel transcript features in RNA-seq data
title_full RNA-eXpress annotates novel transcript features in RNA-seq data
title_fullStr RNA-eXpress annotates novel transcript features in RNA-seq data
title_full_unstemmed RNA-eXpress annotates novel transcript features in RNA-seq data
title_short RNA-eXpress annotates novel transcript features in RNA-seq data
title_sort rna-express annotates novel transcript features in rna-seq data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597146/
https://www.ncbi.nlm.nih.gov/pubmed/23396121
http://dx.doi.org/10.1093/bioinformatics/btt034
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