TISHunter


TISHunter is a highly accurate predictor for translation initiation sites in human mRNAs. TISHunter is based on support vector machines with the edit kernel. For details, please see "A Class of Edit Kernels for SVMs to Predict Translation Initiation Sites in Eukaryotic mRNAs" (in RECOMB04). Oringinally, the algorithm was tested on Pedersen and Nielsen's dataset of 3312 sequences from vertebrates and achieved 99.9% accuracy. Currently, TISHunter is trained on a collection of 8225 human mRNA sequences and the three-fold cross validation accuracy is 96.7%. Besides, the test on 5651 mouse sequences still shows a reasonably high accuracy (89.5%).

Please upload the data file in FASTA format. To achieve accurate predictions, each potential start codon should have at least 10 nucleotides upstream and at least 150 nucleotides downstream in its sequence. Note that TISHunter supposes that every sequence contains TIS. It predicts the first ATG codon with the positive score as the putative TIS. If all ATG codons have the negative scores, TISHunter simply predicts the ATG codon with the largest score as the TIS. Please carefully check the predicted TIS with the negative score. The predicted TIS and score are reported in the title line with the tag cds_start and score, respectively.

Or, paste your mRNA sequences in FASTA format


Please send comments and questions to Haifeng Li