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

• Published in: BMC genomics 2011-12, Vol.12 Suppl 4 (Suppl 4), p.S9-S9, Article S9
• Main Authors: Silva, Lívia Márcia, Teixeira, Felipe Carvalho de Souza, Ortega, José Miguel, Zárate, Luis Enrique, Nobre, Cristiane Neri
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Animals; Arabidopsis - genetics; Arabidopsis thaliana; Base Sequence; Bioinformatics; Biology; Caenorhabditis elegans; Caenorhabditis elegans - genetics; Computer applications; Conferences; Drosophila melanogaster; Humans; Hymenoptera - genetics; Meetings; Mice; Mus musculus; Nasonia vitripennis; Neural networks; Peptide Chain Initiation, Translational; Rats; Rattus norvegicus; RNA, Messenger - chemistry; RNA, Messenger - metabolism; ROC Curve; Software; Statistical analysis; Statistical methods; Studies; Translation initiation
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-12-S4-S9

The accurate prediction of the initiation of translation in sequences of mRNA is an important activity for genome annotation. However, obtaining an accurate prediction is not always a simple task and can be modeled as a problem of classification between positive sequences (protein codifiers) and negative sequences (non-codifiers). The problem is highly imbalanced because each molecule of mRNA has a unique translation initiation site and various others that are not initiators. Therefore, this study focuses on the problem from the perspective of balancing classes and we present an undersampling balancing method, M-clus, which is based on clustering. The method also adds features to sequences and improves the performance of the classifier through the inclusion of knowledge obtained by the model, called InAKnow. Through this methodology, the measures of performance used (accuracy, sensitivity, specificity and adjusted accuracy) are greater than 93% for the Mus musculus and Rattus norvegicus organisms, and varied between 72.97% and 97.43% for the other organisms evaluated: Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Nasonia vitripennis. The precision increases significantly by 39% and 22.9% for Mus musculus and Rattus norvegicus, respectively, when the knowledge obtained by the model is included. For the other organisms, the precision increases by between 37.10% and 59.49%. The inclusion of certain features during training, for example, the presence of ATG in the upstream region of the Translation Initiation Site, improves the rate of sensitivity by approximately 7%. Using the M-Clus balancing method generates a significant increase in the rate of sensitivity from 51.39% to 91.55% (Mus musculus) and from 47.45% to 88.09% (Rattus norvegicus). In order to solve the problem of TIS prediction, the results indicate that the methodology proposed in this work is adequate, particularly when using the concept of acquired knowledge which increased the accuracy in all databases evaluated.