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

MetWAMer: eukaryotic translation initiation site prediction

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>k</it>-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.</p> <p>Conclusion</p> <p>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.</p

Structure of the 5′ Untranslated Region of Enteroviral Genomic RNA

Enteroviral RNA genomes share a long, highly structured 5= untranslated region (5= UTR) containing a type I internal ribosome entry site (IRES). The 5= UTR is composed of stably folded RNA domains connected by unstructured RNA regions. Proper folding and functioning of the 5= UTR underlies the efficiency of viral replication and also determines viral virulence. We have characterized the structure of 5= UTR genomic RNA from coxsackievirus B3 using selective 2=-hydroxyl acylation analyzed by primer extension (SHAPE) and base-specific chemical probes in solution. Our results revealed novel structural features, including realignment of major domains, newly identified long-range interactions, and an intrinsically disordered connecting region. Together, these newly identified features contribute to a model for enteroviral 5= UTRs with type I IRES elements that links structure to function during the hierarchical processes directed by genomic RNA during viral infection. IMPORTANCE: Enterovirus infections are responsible for human diseases, including myocarditis, pancreatitis, acute flaccid paralysis, and poliomyelitis. The virulence of these viruses depends on efficient recognition of the RNA genome by a large family of host proteins and protein synthesis factors, which in turn relies on the threedimensional folding of the first 750 nucleotides of the molecule. Structural information about this region of the genome, called the 5= untranslated region (5= UTR), is needed to assist in the process of vaccine and antiviral development. This work presents a model for the structure of the enteroviral 5= UTR. The model includes an RNA element called an intrinsically disordered RNA region (IDRR). Intrinsically disordered proteins (IDPs) are well known, but correlates in RNA have not been proposed. The proposed IDRR is a 20-nucleotide region, long known for its functional importance, where structural flexibility helps explain recognition by factors controlling multiple functional states

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Through this methodology, the measures of performance used (accuracy, sensitivity, specificity and adjusted accuracy) are greater than 93% for the <it>Mus musculus</it> and <it>Rattus norvegicus</it> organisms, and varied between 72.97% and 97.43% for the other organisms evaluated: <it>Arabidopsis thaliana</it>, <it>Caenorhabditis elegans</it>, <it>Drosophila melanogaster</it>, <it>Homo sapiens</it>, <it>Nasonia vitripennis</it>. The precision increases significantly by 39% and 22.9% for <it>Mus musculus</it> and <it>Rattus norvegicus</it>, 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% (<it>Mus musculus</it>) and from 47.45% to 88.09% (<it>Rattus norvegicus</it>).</p> <p>Conclusions</p> <p>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.</p

Defining the Transcriptional and Post-transcriptional Landscapes of Mycobacterium smegmatis in Aerobic Growth and Hypoxia

The ability of Mycobacterium tuberculosis to infect, proliferate, and survive during long periods in the human lungs largely depends on the rigorous control of gene expression. Transcriptome-wide analyses are key to understanding gene regulation on a global scale. Here, we combine 5′-end-directed libraries with RNAseq expression libraries to gain insight into the transcriptome organization and post-transcriptional mRNA cleavage landscape in mycobacteria during log phase growth and under hypoxia, a physiologically relevant stress condition. Using the model organism Mycobacterium smegmatis, we identified 6,090 transcription start sites (TSSs) with high confidence during log phase growth, of which 67% were categorized as primary TSSs for annotated genes, and the remaining were classified as internal, antisense, or orphan, according to their genomic context. Interestingly, over 25% of the RNA transcripts lack a leader sequence, and of the coding sequences that do have leaders, 53% lack a strong consensus Shine-Dalgarno site. This indicates that like M. tuberculosis, M. smegmatis can initiate translation through multiple mechanisms. Our approach also allowed us to identify over 3,000 RNA cleavage sites, which occur at a novel sequence motif. To our knowledge, this represents the first report of a transcriptome-wide RNA cleavage site map in mycobacteria. The cleavage sites show a positional bias toward mRNA regulatory regions, highlighting the importance of post-transcriptional regulation in gene expression. We show that in low oxygen, a condition associated with the host environment during infection, mycobacteria change their transcriptomic profiles and endonucleolytic RNA cleavage is markedly reduced, suggesting a mechanistic explanation for previous reports of increased mRNA half-lives in response to stress. In addition, a number of TSSs were triggered in hypoxia, 56 of which contain the binding motif for the sigma factor SigF in their promoter regions. This suggests that SigF makes direct contributions to transcriptomic remodeling in hypoxia-challenged mycobacteria. Taken together, our data provide a foundation for further study of both transcriptional and posttranscriptional regulation in mycobacteria