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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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Related in: MedlinePlus

Confero platform overview. Depicts where Confero fits into a typical high-throughput transcriptomics analysis workflow. Contrast data is fed into the platform after the statistical analysis step where it is then converted to idMAPS format and loaded using Confero tools. Contrast data is automatically processed and stored and gene sets are extracted. Data can be analyzed for gene set enrichment and results can be used in other Confero tools or exported for other analyses.
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Figure 2: Confero platform overview. Depicts where Confero fits into a typical high-throughput transcriptomics analysis workflow. Contrast data is fed into the platform after the statistical analysis step where it is then converted to idMAPS format and loaded using Confero tools. Contrast data is automatically processed and stored and gene sets are extracted. Data can be analyzed for gene set enrichment and results can be used in other Confero tools or exported for other analyses.

Mentions: As shown in the FigureĀ 2, processing and analysis of omics data involves a number of steps, from data acquisition and transformation, quality control (QC) (e.g. outlier detection, batch effect correction, etc.), preprocessing and normalization, and statistical analysis. According to the experimental design and biological questions of interest, statistical analysis (e.g. pairwise comparisons, multiple linear regression models) is performed to determine the effect(s) of interest (e.g. effect of treatment over time, over dosage, interaction of both time and dose, using pairwise comparison of treated and control samples, etc.) also termed the contrast(s) of interest from a dataset.


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)

Confero platform overview. Depicts where Confero fits into a typical high-throughput transcriptomics analysis workflow. Contrast data is fed into the platform after the statistical analysis step where it is then converted to idMAPS format and loaded using Confero tools. Contrast data is automatically processed and stored and gene sets are extracted. Data can be analyzed for gene set enrichment and results can be used in other Confero tools or exported for other analyses.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Confero platform overview. Depicts where Confero fits into a typical high-throughput transcriptomics analysis workflow. Contrast data is fed into the platform after the statistical analysis step where it is then converted to idMAPS format and loaded using Confero tools. Contrast data is automatically processed and stored and gene sets are extracted. Data can be analyzed for gene set enrichment and results can be used in other Confero tools or exported for other analyses.
Mentions: As shown in the FigureĀ 2, processing and analysis of omics data involves a number of steps, from data acquisition and transformation, quality control (QC) (e.g. outlier detection, batch effect correction, etc.), preprocessing and normalization, and statistical analysis. According to the experimental design and biological questions of interest, statistical analysis (e.g. pairwise comparisons, multiple linear regression models) is performed to determine the effect(s) of interest (e.g. effect of treatment over time, over dosage, interaction of both time and dose, using pairwise comparison of treated and control samples, etc.) also termed the contrast(s) of interest from a dataset.

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