MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction

Abstract

The prediction of the translation initiation site in a genomic sequence with the highest possible accuracy is an important problem that still has to be investigated by the research community. Current approaches perform quite well, however there is still room for a more general framework for the researchers who want to follow an effective and reliable methodology. We developed a prediction methodology that combines ad hoc as well as discovered knowledge in order to significantly increase the achieved accuracy reliably. Our methodology is modular and consists of three major decision components: a consensus component, a coding region classification component and a novel ATG location-based component that allows for the utilization of the advantages of the popular Ribosome Scanning Model while overcoming its limitations. All three of them are combined into a meta-classification system, using stacked generalization, in a highly effective prediction framework. We performed extensive comparative experiments on four different datasets, showing that the increase in terms of accuracy and adjusted accuracy is not only statistically significant, but also the highest reported.

Improvement in the prediction of the translation initiation site through balancing methods, inclusion of acquired knowledge and addition of features to sequences of mRNA

“…Tzanis et. al [15] also used a component which analyzed the region around the TIS, from -7 to +5, using Markov chains to capture the consensus pattern, indicating that for the identification of the TIS it is important to examine a restricted area around it.…”

“…Thus, by generating the training and test sets, a combination was formed between the extracted sequences and the selected features: presence or absence of an ATG upstream in frame with the TIS, presence or absence of a stop codon in the following 100 nucleotides [1,4,15,29,30], presence or absence of the codons CTG, GAC, GAG and GCC in the downstream region in frame with the TIS [4,15,29]. …”

“…Five measures were used to evaluate the performance of the classifier: accuracy (Ac), precision (Pr), sensitivity (Se), specificity (Sp) and adjusted accuracy (Acj) [2,4,10,15,29]. …”

“…Tzanis et. al [15] developed a methodology for the prediction of the TIS, called MANTIS, with three main components: Consensus , Coding Region classification , and ATG Location . The Coding Region Classification component involves training a model to classify whether or not the ATG of a sequence is the TIS.…”

Regulation of translation by upstream translation initiation codons of surfactant protein A1 splice variants

“…Over the last few years there have been several attempts to increase the specificity and efficiency of algorithms that allow the scoring of potential translation initiation (24,83). Most of these algorithms are based on neural networks.…”