title: Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data.
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.
  
  
  CONCLUSIONS
  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/.
authors.affiliation: Philip Morris International Research & Development, Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland. leandro@leandrohermida.com
authors.forename: Leandro
authors.initials: L
authors.lastname: Hermida
authors.forename: Carine
authors.initials: C
authors.lastname: Poussin
authors.forename: Michael B
authors.initials: MB
authors.lastname: Stadler
authors.forename: Sylvain
authors.initials: S
authors.lastname: Gubian
authors.forename: Alain
authors.initials: A
authors.lastname: Sewer
authors.forename: Dimos
authors.initials: D
authors.lastname: Gaidatzis
authors.forename: Hans-Rudolf
authors.initials: HR
authors.lastname: Hotz
authors.forename: Florian
authors.initials: F
authors.lastname: Martin
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Belcastro
authors.forename: Stéphane
authors.initials: S
authors.lastname: Cano
authors.forename: Manuel C
authors.initials: MC
authors.lastname: Peitsch
authors.forename: Julia
authors.initials: J
authors.lastname: Hoeng
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D030541:Databases, Genetic; D004967:Estrogens / Q000378:metabolism; D006801:Humans; D016247:Information Storage and Retrieval; D051379:Mice; D012984:Software
chemical_list: D004967:Estrogens
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