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