Recognition of Translation Initiation Sites of Eukaryotic Genes Based on an EM Algorithm

Global sequence features based translation initiation site prediction in human genomic sequences

Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.

iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition

Translation is an essential genetic process for understanding the mechanism of gene expression. Due to the large number of protein sequences generated in the post-genomic era, conventional methods are unable to identify Translation Initiation Site (TIS) in human genes timely and accurately. It is thus highly desirable to develop an automatic and accurate computational model for identification of TIS. Considerable improvements have been achieved in developing computational models; however, development of accurate and reliable automated systems for TIS identification in human genes is still a challenging task. In this connection, we propose iTIS-PseKNC, a novel protocol for identification of TIS. Three protein sequence representation methods including dinucleotide composition, pseudo-dinucleotide composition and Trinucleotide composition have been used in order to extract numerical descriptors. Support Vector Machine (SVM), K-nearest neighbor and Probabilistic Neural Network are assessed for their performance using the constructed descriptors. The proposed model iTIS-PseKNC has achieved 99.40% accuracy using jackknife test. The experimental results validated the superior performance of iTIS-PseKNC over the existing methods reported in the literature. It is highly anticipated that the iTIS-PseKNC predictor will be useful for basic research studies.

iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

Translation is a key process for gene expression. Timely identification of the translation initiation site (TIS) is very important for conducting in-depth genome analysis. With the avalanche of genome sequences generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively identifying TIS. Although some computational methods were proposed in this regard, none of them considered the global or long-range sequence-order effects of DNA, and hence their prediction quality was limited. To count this kind of effects, a new predictor, called "iTIS-PseTNC," was developed by incorporating the physicochemical properties into the pseudo trinucleotide composition, quite similar to the PseAAC (pseudo amino acid composition) approach widely used in computational proteomics. It was observed by the rigorous cross-validation test on the benchmark dataset that the overall success rate achieved by the new predictor in identifying TIS locations was over 97%. As a web server, iTIS-PseTNC is freely accessible at http://lin.uestc.edu.cn/server/iTIS-PseTNC. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.

MetWAMer: eukaryotic translation initiation site prediction

Background

Translation initiation site (TIS) identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for 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.

Results

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. Also, our program implements clustering capabilities through use of the k-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5'-complete coverage.

Conclusion

We demonstrate that improvements in statistically-based models for TIS prediction can be achieved by taking the class of each potential start-methionine into account pending certain testing conditions, and that our perceptron-based model is suitable for the TIS identification task. MetWAMer represents a well-documented, extensible, and freely available software system that can be readily re-trained for differing target applications and/or extended with existing and novel TIS prediction methods, to support further research efforts in this area.

Translation initiation site prediction on a genomic scale: beauty in simplicity

MOTIVATION

The correct identification of translation initiation sites (TIS) remains a challenging problem for computational methods that automatically try to solve this problem. Furthermore, the lion's share of these computational techniques focuses on the identification of TIS in transcript data. However, in the gene prediction context the identification of TIS occurs on the genomic level, which makes things even harder because at the genome level many more pseudo-TIS occur, resulting in models that achieve a higher number of false positive predictions.

RESULTS

In this article, we evaluate the performance of several 'simple' TIS recognition methods at the genomic level, and compare them to state-of-the-art models for TIS prediction in transcript data. We conclude that the simple methods largely outperform the complex ones at the genomic scale, and we propose a new model for TIS recognition at the genome level that combines the strengths of these simple models. The new model obtains a false positive rate of 0.125 at a sensitivity of 0.80 on a well annotated human chromosome (chromosome 21). Detailed analyses show that the model is useful, both on its own and in a simple gene prediction setting.

AVAILABILITY

Datafiles and a web interface for the StartScan program are available at http://bioinformatics.psb.ugent.be/supplementary_data/.

Feature selection for the prediction of translation initiation sites

Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree, naïve Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.