Bgee

Bgee provides a reference database to retrieve and compare normal gene expression patterns across multiple animal species by integrating and annotating RNA-Seq, Affymetrix microarray, in situ hybridization and expressed sequence tag (EST) datasets.

Key Features:

• Integrated data types: Combines RNA-Seq, Affymetrix microarray, in situ hybridization and EST expression data for comparative analyses.
• Curated healthy wild-type datasets: Includes only curated healthy wild-type expression datasets to represent natural physiological conditions.
• GTEx re-annotation: Re-annotates GTEx samples to distinguish healthy versus non-healthy specimens.
• Ontology mapping: Maps expression data onto anatomical and developmental ontologies to enable consistent annotation across datasets.
• Anatomical homology relationships: Designs homology relationships across anatomical ontologies to support cross-species comparisons.
• Developmental-stage comparison criteria: Establishes comparison criteria between developmental stages for automated staging comparisons.
• Automated cross-species comparisons: Performs automated comparisons of gene expression patterns within and between species and data types.
• Preferred expression condition retrieval: Retrieves preferred expression conditions for specific genes.
• Enrichment analysis: Supports enrichment analyses to identify gene sets expressed under particular conditions.
• Release and species coverage: Release 14.1 encompasses data from 29 animal species.
• Programmatic access: Provides a Bioconductor R package, BgeeDB, for programmatic interaction with the resource.

Scientific Applications:

• Comparative transcriptomics: Identify conserved and divergent gene expression patterns across animal species.
• Evolutionary biology: Study evolutionary conservation and divergence of expression in anatomical structures and developmental stages.
• Developmental biology: Compare gene expression dynamics across defined developmental stages and mapped ontologies.
• Functional genomics: Determine expression contexts and preferred conditions for genes to infer function.
• Gene set analysis: Use enrichment analyses to detect sets of genes associated with specific anatomical or developmental conditions.

Methodology:

Methods include curation of healthy wild-type expression datasets; re-annotation of GTEx to label healthy versus non-healthy samples; annotation and processing of RNA-Seq, Affymetrix microarray, in situ hybridization and EST data; mapping data to anatomical and developmental ontologies; designing anatomical homology relationships and developmental-stage comparison criteria; and automated detection of expression conditions across species and data types.