m6A-TCPred

m6A-TCPred predicts tissue-conserved N6-methyladenosine (m6A) sites at base resolution to distinguish conserved from non-conserved m6A modifications across 23 human tissues.


Key Features:

  • Tissue profiling data: Leverages m6A profiling data derived from 23 human tissues.
  • Feature integration: Integrates traditional sequence-based characteristics with additional genome-derived information.
  • Base-resolution prediction: Produces predictions at base resolution to identify specific m6A residues.
  • Conservation discrimination: Discerns distinct patterns between tissue-conserved and non-conserved m6A modifications.
  • Performance metrics: Reports average AUROC of 0.871 in cross-validation and 0.879 on independent datasets.
  • Annotated database: Includes 268,115 high-confidence m6A sites annotated with conserved status across 23 human tissues.

Scientific Applications:

  • Tissue-specific epitranscriptomic studies: Enables analysis of m6A conservation patterns across multiple human tissues.
  • Disease research: Supports investigation of m6A dysregulation in diseases, including cancer.
  • Regulatory mechanism exploration: Assists in elucidating regulatory mechanisms and potential therapeutic targets associated with tissue-specific m6A marks.

Methodology:

Uses m6A profiling data from 23 human tissues and integrates sequence-based characteristics with genome-derived information; model performance was evaluated by cross-validation and independent datasets reporting AUROC values.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Programming Languages:
R
Added:
6/18/2024
Last Updated:
11/24/2024

Operations

Publications

Tu G, Wang X, Xia R, Song B. m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach. BMC Bioinformatics. 2024;25(1). doi:10.1186/s12859-024-05738-1. PMID:38528499. PMCID:PMC10962094.

PMID: 38528499
Funding: - XJTLU Key Program Special Fund: KSF-E-51 and KSF-P-02 - Scientific Research Foundation of Nanjing University of Chinese Medicine: 013038030001

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