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SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis
BACKGROUND: Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4890512/ https://www.ncbi.nlm.nih.gov/pubmed/27255077 http://dx.doi.org/10.1186/s12859-016-1104-8 |
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author | Hatakeyama, Masaomi Opitz, Lennart Russo, Giancarlo Qi, Weihong Schlapbach, Ralph Rehrauer, Hubert |
author_facet | Hatakeyama, Masaomi Opitz, Lennart Russo, Giancarlo Qi, Weihong Schlapbach, Ralph Rehrauer, Hubert |
author_sort | Hatakeyama, Masaomi |
collection | PubMed |
description | BACKGROUND: Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility. RESULTS: We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI. CONCLUSION: SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1104-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4890512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48905122016-06-10 SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis Hatakeyama, Masaomi Opitz, Lennart Russo, Giancarlo Qi, Weihong Schlapbach, Ralph Rehrauer, Hubert BMC Bioinformatics Software BACKGROUND: Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility. RESULTS: We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI. CONCLUSION: SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1104-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-02 /pmc/articles/PMC4890512/ /pubmed/27255077 http://dx.doi.org/10.1186/s12859-016-1104-8 Text en © Hatakeyama et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Hatakeyama, Masaomi Opitz, Lennart Russo, Giancarlo Qi, Weihong Schlapbach, Ralph Rehrauer, Hubert SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title | SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title_full | SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title_fullStr | SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title_full_unstemmed | SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title_short | SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis |
title_sort | sushi: an exquisite recipe for fully documented, reproducible and reusable ngs data analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4890512/ https://www.ncbi.nlm.nih.gov/pubmed/27255077 http://dx.doi.org/10.1186/s12859-016-1104-8 |
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