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TITER / Translation Initiation site Detector

Predicts translation initiation sites (TISs) based on the available high-throughput sequencing (HTS) data. TITER is a deep learning based framework that allows to model the sequence features of translation initiation and identify potential TISs. In addition, it can identify significant sequence motifs for different TIS codons, including a Kozak-sequence-like motif for the AUG TIS codon. The software can be useful for the community to investigate probable TISs and further expand our understanding of the mechanisms underlying translation initiation.

TICO / Translation Initiation site Correction

A software tool for improving the results of conventional gene finders for prokaryotic genomes with regard to exact localization of the translation initiation site (TIS). At the current state TICO provides an interface for direct post processing of the predictions obtained from the widely used program GLIMMER. Although the underlying method is not based on any specific assumptions about characteristic sequence features of prokaryotic TIS the prediction rates of our tool are competitive on experimentally verified test data.


A package for translation initiation site (TIS) prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations. MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron.


A highly accurate predictor for translation initiation sites in human mRNAs. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string editing, called edit kernels, for use with support vector machines (SVMs) in a discriminative approach to predict TISs. The edit kernels are simple and have significant biological and probabilistic interpretations. Although the edit kernels are not positive definite, it is easy to make the kernel matrix positive definite by adjusting the parameters. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code.