ORegAnno

ORegAnno aggregates and annotates literature-curated regulatory elements (regulatory regions, transcription factor binding sites (TFBS), and regulatory mutations including polymorphisms and haplotypes) to support identification and characterization of transcriptional regulatory elements across species.

Key Features:

• Data Types: Records cover regulatory regions, transcription factor binding sites (TFBS), and regulatory mutations including polymorphisms and haplotypes.
• Dynamic Collection and Curation: Aggregates experimentally identified DNA regulatory elements from scientific literature and houses 30,145 records curated from 922 publications covering over 3,853 genes and 465 transcription factors across 19 species.
• Community-Based Annotation System: Supports community-sourced annotation via a publication queue enabling manual expert curation and text-mining extraction of regulatory elements from publications.
• Cross-Referencing and Integration: Cross-references records to Ensembl, dbSNP, Entrez Gene, the NCBI Taxonomy database, and PubMed for data linkage and validation.
• Evidence Ontology Development: Employs an evidence ontology to categorize experimental outcomes, and records include species, sequence type, target gene, binding factor, and lines of experimental evidence.

Scientific Applications:

• Gene regulation analysis: Enables characterization of transcriptional regulatory elements and their target genes across multiple species.
• Variant interpretation: Supports identification and annotation of regulatory mutations and polymorphisms relevant to disease-associated variants.
• Transcription factor interaction studies: Facilitates exploration of transcription factor binding sites and binding-factor relationships.
• Comparative genomics: Provides curated regulatory element data across 19 species for cross-species regulatory analysis.
• Functional genomics: Supplies literature-curated regulatory annotations to inform functional assays and interpretation.
• Personalized medicine research: Aids investigation of regulatory variants that may contribute to individual disease risk or therapeutic responses.

Methodology:

Methods explicitly include literature aggregation and manual expert curation, text-mining extraction from publications, cross-referencing to Ensembl, dbSNP, Entrez Gene, NCBI Taxonomy and PubMed, and classification of experimental evidence using an evidence ontology.