new uORFdb

new uORFdb catalogs upstream open reading frames (uORFs) initiated by AUG and near-cognate start codons within transcript leader sequences of eukaryotic transcripts and integrates sequence, literature, and variation data to support analysis of their roles in regulating downstream translation and encoding non-canonical peptides.

Key Features:

• Comprehensive Data Integration: Integrates literature, sequence data, and variation information into a centralized resource for uORF research.
• Extensive Sequence Database: Contains detailed sequence information for over 2.4 million human uORFs and more than 4.2 million uORFs across 12 additional species.
• Transcript- and Reading-Frame-Specific Models: Provides graphical displays and models that represent multiple uORFs within their transcript and reading-frame contexts.
• Advanced Filtering and Analysis Tools: Implements filters and sequence-related information retrieval for targeted analyses and links entries to UCSC Genome Browser, dbSNP, and ClinVar.
• Genetic Variation Data: Includes uORF-related somatic variation data derived from whole-genome sequencing (WGS) of 677 cancer samples collected by the TCGA consortium.

Scientific Applications:

• Translational Regulation Studies: Enables analysis of uORF-mediated regulation of downstream main protein coding sequence translation.
• Non-Canonical Peptide Discovery: Supports identification and characterization of peptides encoded by uORFs.
• Disease-Associated Variant Interpretation: Facilitates investigation of genetic variations in uORFs linked to diseases, including cancer.
• Cancer Genomics and Somatic Variant Analysis: Provides somatic WGS-derived uORF variation data for cancer-related studies using TCGA samples.
• Comparative and Evolutionary Studies: Offers cross-species uORF datasets to support evolutionary and functional comparisons.
• Personalized Medicine Research: Supplies integrated variation and sequence data applicable to patient-specific variant interpretation in clinical research contexts.

Methodology:

Integrates literature, sequence, and variation data; generates graphical transcript- and reading-frame-specific models; applies filters for sequence retrieval and analysis; incorporates somatic variants from WGS of 677 TCGA cancer samples; and links entries to UCSC Genome Browser, dbSNP, and ClinVar.