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CAFU: a Galaxy framework for exploring unmapped RNA-Seq data
A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological inform...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299299/ https://www.ncbi.nlm.nih.gov/pubmed/30815667 http://dx.doi.org/10.1093/bib/bbz018 |
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author | Chen, Siyuan Ren, Chengzhi Zhai, Jingjing Yu, Jiantao Zhao, Xuyang Li, Zelong Zhang, Ting Ma, Wenlong Han, Zhaoxue Ma, Chuang |
author_facet | Chen, Siyuan Ren, Chengzhi Zhai, Jingjing Yu, Jiantao Zhao, Xuyang Li, Zelong Zhang, Ting Ma, Wenlong Han, Zhaoxue Ma, Chuang |
author_sort | Chen, Siyuan |
collection | PubMed |
description | A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU. |
format | Online Article Text |
id | pubmed-7299299 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72992992020-06-22 CAFU: a Galaxy framework for exploring unmapped RNA-Seq data Chen, Siyuan Ren, Chengzhi Zhai, Jingjing Yu, Jiantao Zhao, Xuyang Li, Zelong Zhang, Ting Ma, Wenlong Han, Zhaoxue Ma, Chuang Brief Bioinform Problem Solving Protocol A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU. Oxford University Press 2019-02-28 /pmc/articles/PMC7299299/ /pubmed/30815667 http://dx.doi.org/10.1093/bib/bbz018 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Problem Solving Protocol Chen, Siyuan Ren, Chengzhi Zhai, Jingjing Yu, Jiantao Zhao, Xuyang Li, Zelong Zhang, Ting Ma, Wenlong Han, Zhaoxue Ma, Chuang CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title | CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title_full | CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title_fullStr | CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title_full_unstemmed | CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title_short | CAFU: a Galaxy framework for exploring unmapped RNA-Seq data |
title_sort | cafu: a galaxy framework for exploring unmapped rna-seq data |
topic | Problem Solving Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299299/ https://www.ncbi.nlm.nih.gov/pubmed/30815667 http://dx.doi.org/10.1093/bib/bbz018 |
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