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Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data
BACKGROUND: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750322/ https://www.ncbi.nlm.nih.gov/pubmed/23895370 http://dx.doi.org/10.1186/1471-2164-14-514 |
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author | Hermida, Leandro Poussin, Carine Stadler, Michael B Gubian, Sylvain Sewer, Alain Gaidatzis, Dimos Hotz, Hans-Rudolf Martin, Florian Belcastro, Vincenzo Cano, Stéphane Peitsch, Manuel C Hoeng, Julia |
author_facet | Hermida, Leandro Poussin, Carine Stadler, Michael B Gubian, Sylvain Sewer, Alain Gaidatzis, Dimos Hotz, Hans-Rudolf Martin, Florian Belcastro, Vincenzo Cano, Stéphane Peitsch, Manuel C Hoeng, Julia |
author_sort | Hermida, Leandro |
collection | PubMed |
description | BACKGROUND: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as ” contrast data”) in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). RESULTS: To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset. CONCLUSION: Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/. |
format | Online Article Text |
id | pubmed-3750322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37503222013-08-24 Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data Hermida, Leandro Poussin, Carine Stadler, Michael B Gubian, Sylvain Sewer, Alain Gaidatzis, Dimos Hotz, Hans-Rudolf Martin, Florian Belcastro, Vincenzo Cano, Stéphane Peitsch, Manuel C Hoeng, Julia BMC Genomics Software BACKGROUND: High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as ” contrast data”) in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). RESULTS: To address these challenges, we developed Confero, a contrast data and gene set platform for downstream analysis and biological interpretation of omics data. The Confero software platform provides storage of contrast data in a simple and standard format, data transformation to enable cross-study and platform data comparison, and automatic extraction and storage of gene sets to build new a priori knowledge which is leveraged by integrated and extensible downstream computational analysis tools. Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA) are currently integrated as an analysis module as well as additional tools to support biological interpretation. Confero is a standalone system that also integrates with Galaxy, an open-source workflow management and data integration system. To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset. CONCLUSION: Confero provides a unique and flexible platform to support downstream computational analysis facilitating biological interpretation. The system has been designed in order to provide the researcher with a simple, innovative, and extensible solution to store and exploit analyzed data in a sustainable and reproducible manner thereby accelerating knowledge-driven research. Confero source code is freely available from http://sourceforge.net/projects/confero/. BioMed Central 2013-07-29 /pmc/articles/PMC3750322/ /pubmed/23895370 http://dx.doi.org/10.1186/1471-2164-14-514 Text en Copyright © 2013 Hermida et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Hermida, Leandro Poussin, Carine Stadler, Michael B Gubian, Sylvain Sewer, Alain Gaidatzis, Dimos Hotz, Hans-Rudolf Martin, Florian Belcastro, Vincenzo Cano, Stéphane Peitsch, Manuel C Hoeng, Julia Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title | Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title_full | Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title_fullStr | Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title_full_unstemmed | Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title_short | Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
title_sort | confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750322/ https://www.ncbi.nlm.nih.gov/pubmed/23895370 http://dx.doi.org/10.1186/1471-2164-14-514 |
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