TISSUES

TISSUES aggregates tissue expression evidence from manually curated literature, proteomics, transcriptomics screens, and automatic text mining into a unified dataset mapped to common protein identifiers and Brenda Tissue Ontology terms for comparative analysis of gene and protein tissue-specificity.

Key Features:

• Integrated evidence sources: Aggregates data from manually curated literature, proteomics, transcriptomics screens, and automatic text mining.
• Identifier mapping: Maps all evidence to common protein identifiers to enable cross-dataset comparability.
• Ontology mapping: Maps tissue annotations to Brenda Tissue Ontology terms for standardized tissue representation.
• Confidence scoring: Assigns confidence scores to different evidence types to allow direct comparison of reliability across sources.
• Systematic updates: Maintains a systematically updated repository of tissue expression evidence.
• Concordance assessment: Evaluates agreement among diverse experimental datasets and between experimental data and literature/text-mined evidence.
• Tissue-specificity classification: Supports distinction and comparison between tissue-specific and ubiquitous protein expression.

Scientific Applications:

• Comparative tissue-expression analysis: Compare protein expression patterns across tissues using integrated evidence from multiple sources.
• Quality assessment of datasets: Use confidence scores to assess reliability and coverage of proteomics and transcriptomics datasets.
• Functional annotation in tissue context: Inform studies of gene and protein function by providing tissue-mapped expression evidence.
• Meta-analyses across evidence types: Combine curated, experimental, and text-mined evidence for comprehensive tissue expression studies.

Methodology:

Aggregates evidence from curated literature, proteomics, transcriptomics and text mining; maps evidence to common protein identifiers and Brenda Tissue Ontology terms; assigns confidence scores to evidence types; evaluates concordance among datasets and with literature/text-mined results.