Evolutionary Modeling and Prediction of Non-Coding RNAs in Drosophila

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3′ end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.     

Plant noncoding RNA gene discovery by "single-genome comparative genomics"

Plant genomes have undergone multiple rounds of duplications that contributed massively to the growth of gene families. The structure of resulting families has been studied in depth for protein-coding genes. However, little is known about the impact of duplications on noncoding RNA (ncRNA) genes. Here we perform a systematic analysis of duplicated regions in the rice genome in search of such ncRNA repeats. We observe that, just like their protein counterparts, most ncRNA genes have undergone multiple duplications that left visible sequence conservation footprints. The extent of ncRNA gene duplication in plants is such that these sequence footprints can be exploited for the discovery of novel ncRNA gene families on a large scale. We developed an SVM model that is able to retrieve likely ncRNA candidates among the 100,000+ repeat families in the rice genome, with a reasonably low false-positive discovery rate. Among the nearly 4000 ncRNA families predicted by this means, only 90 correspond to putative snoRNA or miRNA families. About half of the remaining families are classified as structured RNAs. New candidate ncRNAs are particularly enriched in UTR and intronic regions. Interestingly, 89% of the putative ncRNA families do not produce a detectable signal when their sequences are compared to another grass genome such as maize. Our results show that a large fraction of rice ncRNA genes are present in multiple copies and are species-specific or of recent origin. Intragenome comparison is a unique and potent source for the computational annotation of this major class of ncRNA.     

Protein subcellular localization based on PSI-BLAST and machine learning

Subcellular location is an important functional annotation of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is necessary for large-scale genome analysis. This paper describes a protein subcellular localization method which extracts features from protein profiles rather than from amino acid sequences. The protein profile represents a protein family, discards part of the sequence information that is not conserved throughout the family and therefore is more sensitive than the amino acid sequence. The amino acid compositions of whole profile and the N-terminus of the profile are extracted, respectively, to train and test the probabilistic neural network classifiers. On two benchmark datasets, the overall accuracies of the proposed method reach 89.1% and 68.9%, respectively. The prediction results show that the proposed method perform better than those methods based on amino acid sequences. The prediction results of the proposed method are also compared with Subloc on two redundance-reduced datasets.     

Comparative genomics approach to detecting split-coding regions in a low-coverage genome: lessons from the chimaera Callorhinchus milii (Holocephali, Chondrichthyes)

Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references.     

Identification of protein-coding sequences using the hybridization of 18S rRNA and mRNA during translation

We introduce a new approach in this article to distinguish protein-coding sequences from non-coding sequences utilizing a period-3, free energy signal that arises from the interactions of the 3′-terminal nucleotides of the 18S rRNA with mRNA. We extracted the special features of the amplitude and the phase of the period-3 signal in protein-coding regions, which is not found in non-coding regions, and used them to distinguish protein-coding sequences from non-coding sequences. We tested on all the experimental genes from Saccharomyces cerevisiae and Schizosaccharomyces pombe. The identification was consistent with the corresponding information from GenBank, and produced better performance compared to existing methods that use a period-3 signal. The primary tests on some fly, mouse and human genes suggests that our method is applicable to higher eukaryotic genes. The tests on pseudogenes indicated that most pseudogenes have no period-3 signal. Some exploration of the 3′-tail of 18S rRNA and pattern analysis of protein-coding sequences supported further our assumption that the 3′-tail of 18S rRNA has a role of synchronization throughout translation elongation process. This, in turn, can be utilized for the identification of protein-coding sequences.     

Identifying novel genes in <Emphasis Type="Italic">C. elegans</Emphasis> using SAGE tags

Background  

Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required.     

New Intraclass Helitrons Classification Using DNA-Image Sequences and Machine Learning Approaches

Helitrons, eukaryotic transposable elements (TEs) transposed by rolling-circle mechanism, have been found in various species with highly variable copy numbers and sometimes with a large portion of their genomes. The impact of helitrons sequences in the genome is to frequently capture host genes during their transposition. Since their discovery, 18 years ago, by computational analysis of whole genome sequences of Arabidopsis thaliana plant and Caenorhabditis elegans (C. elegans) nematode, the identification and classification of these mobile genetic elements remain a challenge due to the fact that the wide majority of their families are non-autonomous. In C. elegans genome, DNA helitrons sequences possess great variability in terms of length that varies between 11 and 8965 base pairs (bps) from one sequence to another. In this work, we develop a new method to predict helitrons DNA-sequences, which is particularly based on Frequency Chaos Game Representation (FCGR) DNA-images. Thus, we introduce an automatic system in order to classify helitrons families in C. elegans genome, based on a combination between machine learning approaches and features extracted from DNA-sequences. Consequently, the new set of helitrons features (the FCGR images and K-mers) are extracted from DNA sequences. These helitrons features consist of the frequency apparition number of K nucleotides pairs (Tandem Repeat) in the DNA sequences. Indeed, three different classifiers are used for the classification of all existing helitrons families. The results have shown potential global score equal to 72.7% due to FCGR images which constitute helitrons features and the pre-trained neural network as a classifier. The two other classifiers demonstrate that their efficiency reaches 68.7% for Support Vector Machine (SVM) and 91.45% for Random Forest (RF) algorithms using the K-mers features corresponding to the genomic sequences.     

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA

The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).