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Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.

Hermida L, Poussin C, Stadler MB, Gubian S, Sewer A, Gaidatzis D, Hotz HR, Martin F, Belcastro V, Cano S, Peitsch MC, Hoeng J - BMC Genomics (2013)

Bottom Line: 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.).To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Philip Morris International Research & Development, Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland. leandro@leandrohermida.com

ABSTRACT

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/.

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Biological knowledge building process enabled by Confero which enhances the standard omics analysis workflow. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge in the form of gene set is stored. This database of knowledge grows with new experimental and external data and is leveraged by integrated tools for biological interpretation of current and future studies. The overall process drives and enhances the experimental research lifecycle.
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Figure 1: Biological knowledge building process enabled by Confero which enhances the standard omics analysis workflow. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge in the form of gene set is stored. This database of knowledge grows with new experimental and external data and is leveraged by integrated tools for biological interpretation of current and future studies. The overall process drives and enhances the experimental research lifecycle.

Mentions: The overall goal and spirit of Confero is illustrated in FigureĀ 1. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge (e.g. gene sets) is stored. This knowledge base grows with new experimental and external data and is leveraged by integrated tools for biological interpretation. The added loop in the analysis workflow facilitates and accelerates knowledge acquisition in biological research, for example in areas such as biomarker and gene function discovery, understanding of molecular mechanisms, and cross-study comparison. The overall process drives and enhances the experimental research lifecycle.


Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.

Hermida L, Poussin C, Stadler MB, Gubian S, Sewer A, Gaidatzis D, Hotz HR, Martin F, Belcastro V, Cano S, Peitsch MC, Hoeng J - BMC Genomics (2013)

Biological knowledge building process enabled by Confero which enhances the standard omics analysis workflow. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge in the form of gene set is stored. This database of knowledge grows with new experimental and external data and is leveraged by integrated tools for biological interpretation of current and future studies. The overall process drives and enhances the experimental research lifecycle.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750322&req=5

Figure 1: Biological knowledge building process enabled by Confero which enhances the standard omics analysis workflow. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge in the form of gene set is stored. This database of knowledge grows with new experimental and external data and is leveraged by integrated tools for biological interpretation of current and future studies. The overall process drives and enhances the experimental research lifecycle.
Mentions: The overall goal and spirit of Confero is illustrated in FigureĀ 1. The left loop depicts the typical omics workflow from data generation, processing, and statistical analysis followed by downstream computational analysis and biological interpretation leading to new hypothesis generation. With Confero in place a second loop is added where statistical analysis results are automatically processed and new a priori biological knowledge (e.g. gene sets) is stored. This knowledge base grows with new experimental and external data and is leveraged by integrated tools for biological interpretation. The added loop in the analysis workflow facilitates and accelerates knowledge acquisition in biological research, for example in areas such as biomarker and gene function discovery, understanding of molecular mechanisms, and cross-study comparison. The overall process drives and enhances the experimental research lifecycle.

Bottom Line: 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.).To illustrate Confero platform functionality we walk through major aspects of the Confero workflow and results using the Bioconductor estrogen package dataset.

View Article: PubMed Central - HTML - PubMed

Affiliation: Philip Morris International Research & Development, Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland. leandro@leandrohermida.com

ABSTRACT

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/.

Show MeSH
Related in: MedlinePlus