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SecretSanta: flexible pipelines for functional secretome prediction
MOTIVATION: The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022548/ https://www.ncbi.nlm.nih.gov/pubmed/29462238 http://dx.doi.org/10.1093/bioinformatics/bty088 |
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author | Gogleva, Anna Drost, Hajk-Georg Schornack, Sebastian |
author_facet | Gogleva, Anna Drost, Hajk-Georg Schornack, Sebastian |
author_sort | Gogleva, Anna |
collection | PubMed |
description | MOTIVATION: The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich reservoirs of biomarkers for disease classification and host–pathogen interaction studies. Common biomarkers are extracellular proteins secreted via classical pathways that can be predicted from sequence by annotating the presence or absence of N-terminal signal peptides. Several heterogeneous command line tools and web-interfaces exist to identify individual motifs, signal sequences and domains that are either characteristic or strictly excluded from secreted proteins. However, a single flexible secretome-prediction workflow that combines all analytic steps is still missing. RESULTS: To bridge this gap the SecretSanta package implements wrapper and parser functions around established command line tools for the integrative prediction of extracellular proteins that are secreted via classical pathways. The modularity of SecretSanta enables users to create tailored pipelines and apply them across the whole tree of life to facilitate comparison of secretomes across multiple species or under various conditions. AVAILABILITY AND IMPLEMENTATION: SecretSanta is implemented in the R programming language and is released under GPL-3 license. All functions have been optimized and parallelized to allow large-scale processing of sequences. The open-source code, installation instructions and vignette with use case scenarios can be downloaded from https://github.com/gogleva/SecretSanta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6022548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-60225482018-07-10 SecretSanta: flexible pipelines for functional secretome prediction Gogleva, Anna Drost, Hajk-Georg Schornack, Sebastian Bioinformatics Applications Notes MOTIVATION: The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich reservoirs of biomarkers for disease classification and host–pathogen interaction studies. Common biomarkers are extracellular proteins secreted via classical pathways that can be predicted from sequence by annotating the presence or absence of N-terminal signal peptides. Several heterogeneous command line tools and web-interfaces exist to identify individual motifs, signal sequences and domains that are either characteristic or strictly excluded from secreted proteins. However, a single flexible secretome-prediction workflow that combines all analytic steps is still missing. RESULTS: To bridge this gap the SecretSanta package implements wrapper and parser functions around established command line tools for the integrative prediction of extracellular proteins that are secreted via classical pathways. The modularity of SecretSanta enables users to create tailored pipelines and apply them across the whole tree of life to facilitate comparison of secretomes across multiple species or under various conditions. AVAILABILITY AND IMPLEMENTATION: SecretSanta is implemented in the R programming language and is released under GPL-3 license. All functions have been optimized and parallelized to allow large-scale processing of sequences. The open-source code, installation instructions and vignette with use case scenarios can be downloaded from https://github.com/gogleva/SecretSanta. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-07-01 2018-02-16 /pmc/articles/PMC6022548/ /pubmed/29462238 http://dx.doi.org/10.1093/bioinformatics/bty088 Text en © The Author(s) 2018. 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 | Applications Notes Gogleva, Anna Drost, Hajk-Georg Schornack, Sebastian SecretSanta: flexible pipelines for functional secretome prediction |
title | SecretSanta: flexible pipelines for functional secretome prediction |
title_full | SecretSanta: flexible pipelines for functional secretome prediction |
title_fullStr | SecretSanta: flexible pipelines for functional secretome prediction |
title_full_unstemmed | SecretSanta: flexible pipelines for functional secretome prediction |
title_short | SecretSanta: flexible pipelines for functional secretome prediction |
title_sort | secretsanta: flexible pipelines for functional secretome prediction |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022548/ https://www.ncbi.nlm.nih.gov/pubmed/29462238 http://dx.doi.org/10.1093/bioinformatics/bty088 |
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