This is the first computational tool to identify tissue-conserved m⁶A methylation sites. This modification is the most prevalent post-transcriptional modification in eukaryotic cells. Considering its crucial role in regulating various biological processes, predicting and locating its conserved methylation sites among multiple human tissues will lay the foundation for m⁶A methylation research. m6A-TCPred was mainly built upon the machine learning algorithm, support vector machine, to achieve the powerful predictive function.

We have uploaded a database of 268,115 high-confidence experimentally validated m⁶A sites identified in at least two independent studies. Users could filter data by selecting the range of counts. Details can be downloaded from the button below the table in CSV format.

Users could click an ID for detailed information with tissue contexts which shown below the table.

Users can input or upload the query sequences in the txt format. The txt file should include three columns which are seqnames, position and strand, respectively. Results will be presented after a while.

The result table shows seqnames, position, strand, probability and prediction result.