TEx-MST
TEx-MST provides tissue-specific expression information for MANE-select transcripts across normal human tissues using GTEx V9 long-read sequencing data.
Key Features:
- Unified Transcript Information: Leverages MANE (Matched Annotation from the NCBI and EMBL-EBI) select transcripts as harmonized representative transcripts for human protein-coding genes.
- Expression Profiles from GTEx V9: Utilizes the Genotype-Tissue Expression (GTEx) V9 dataset generated by long-read sequencing to quantify expression of alternatively spliced transcripts across tissues.
- Comprehensive Database: Reports 18,083 genes matched between MANE and GTEx and 13,245 MANE-select transcripts corresponding to top-ranked protein-coding transcripts in GTEx V9.
- Biotype Feature Utilization: Employs the GENCODE biotype feature and incorporates GTEx V8 and V9 data to identify the most expressed protein-coding transcripts.
Scientific Applications:
- Transcript expression profiling: Enables analysis of MANE-select transcript abundance and predominant transcript usage across normal human tissues.
- Gene regulation and alternative splicing studies: Supports investigations into gene regulation and alternative splicing by providing tissue-resolved expression of representative transcripts.
- Comparative transcriptome analyses: Facilitates comparison of transcript expression patterns between GTEx V8 and V9 datasets for protein-coding transcripts defined by GENCODE biotypes.
Methodology:
Integrates MANE and GTEx datasets using GTEx V9 long-read sequencing data and employs the GENCODE biotype feature to identify the most expressed protein-coding transcripts, reporting 18,083 matched genes and 13,245 MANE-select transcripts corresponding to top-ranked GTEx V9 transcripts.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Added:
- 10/30/2022
- Last Updated:
- 11/24/2024
Operations
Publications
Tung K, Lin W. TEx-MST: tissue expression profiles of MANE select transcripts. Database. 2022;2022. doi:10.1093/database/baac089. PMID:36170113. PMCID:PMC9518666.
PMID: 36170113
PMCID: PMC9518666
Funding: - National Science and Technology Council: 109-2311-B-001-013-MY3