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GeneSCF: a real-time based functional enrichment tool with support for multiple organisms
BACKGROUND: High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughp...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020511/ https://www.ncbi.nlm.nih.gov/pubmed/27618934 http://dx.doi.org/10.1186/s12859-016-1250-z |
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author | Subhash, Santhilal Kanduri, Chandrasekhar |
author_facet | Subhash, Santhilal Kanduri, Chandrasekhar |
author_sort | Subhash, Santhilal |
collection | PubMed |
description | BACKGROUND: High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. RESULTS: In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. CONCLUSIONS: GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1250-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5020511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50205112016-09-20 GeneSCF: a real-time based functional enrichment tool with support for multiple organisms Subhash, Santhilal Kanduri, Chandrasekhar BMC Bioinformatics Software BACKGROUND: High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. RESULTS: In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. CONCLUSIONS: GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1250-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-13 /pmc/articles/PMC5020511/ /pubmed/27618934 http://dx.doi.org/10.1186/s12859-016-1250-z Text en © The Author(s). 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 Subhash, Santhilal Kanduri, Chandrasekhar GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title | GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title_full | GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title_fullStr | GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title_full_unstemmed | GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title_short | GeneSCF: a real-time based functional enrichment tool with support for multiple organisms |
title_sort | genescf: a real-time based functional enrichment tool with support for multiple organisms |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020511/ https://www.ncbi.nlm.nih.gov/pubmed/27618934 http://dx.doi.org/10.1186/s12859-016-1250-z |
work_keys_str_mv | AT subhashsanthilal genescfarealtimebasedfunctionalenrichmenttoolwithsupportformultipleorganisms AT kandurichandrasekhar genescfarealtimebasedfunctionalenrichmenttoolwithsupportformultipleorganisms |