IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico

Background  

Internal ribosomal entry sites (IRESs) provide alternative, cap-independent translation initiation sites in eukaryotic cells. IRES elements are important factors in viral genomes and are also useful tools for bi-cistronic expression vectors. Most existing RNA structure prediction programs are unable to deal with IRES elements.     

An unsupervised classification scheme for improving predictions of prokaryotic TIS

Background  

Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes.     

Systematic integration of experimental data and models in systems biology

Background

The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals.

Results

With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives.

Conclusion

We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.     

Bioinformatic analyses of mammalian 5'-UTR sequence properties of mRNAs predicts alternative translation initiation sites

Background  

Utilization of alternative initiation sites for protein translation directed by non-AUG codons in mammalian mRNAs is observed with increasing frequency. Alternative initiation sites are utilized for the synthesis of important regulatory proteins that control distinct biological functions. It is, therefore, of high significance to define the parameters that allow accurate bioinformatic prediction of alternative translation initiation sites (aTIS). This study has investigated 5'-UTR regions of mRNAs to define consensus sequence properties and structural features that allow identification of alternative initiation sites for protein translation.     

MicroRNA targets in Drosophila

Background  

The recent discoveries of microRNA (miRNA) genes and characterization of the first few target genes regulated by miRNAs in Caenorhabditis elegans and Drosophila melanogaster have set the stage for elucidation of a novel network of regulatory control. We present a computational method for whole-genome prediction of miRNA target genes. The method is validated using known examples. For each miRNA, target genes are selected on the basis of three properties: sequence complementarity using a position-weighted local alignment algorithm, free energies of RNA-RNA duplexes, and conservation of target sites in related genomes. Application to the D. melanogaster, Drosophila pseudoobscura and Anopheles gambiae genomes identifies several hundred target genes potentially regulated by one or more known miRNAs.     

Comparison of computational methods for identifying translation initiation sites in EST data

Background

Expressed Sequence Tag (EST) sequences are generally single-strand, single-pass sequences, only 200–600 nucleotides long, contain errors resulting in frame shifts, and represent different parts of their parent cDNA. If the cDNAs contain translation initiation sites, they may be suitable for functional genomics studies. We have compared five methods to predict translation initiation sites in EST data: first-ATG, ESTScan, Diogenes, Netstart, and ATGpr.

Results

A dataset of 100 EST sequences, 50 with and 50 without, translation initiation sites, was created. Based on analysis of this dataset, ATGpr is found to be the most accurate for predicting the presence versus absence of translation initiation sites. With a maximum accuracy of 76%, ATGpr more accurately predicts the position or absence of translation initiation sites than NetStart (57%) or Diogenes (50%). ATGpr similarly excels when start sites are known to be present (90%), whereas NetStart achieves only 60% overall accuracy. As a baseline for comparison, choosing the first ATG correctly identifies the translation initiation site in 74% of the sequences. ESTScan and Diogenes, consistent with their intended use, are able to identify open reading frames, but are unable to determine the precise position of translation initiation sites.

Conclusions

ATGpr demonstrates high sensitivity, specificity, and overall accuracy in identifying start sites while also rejecting incomplete sequences. A database of EST sequences suitable for validating programs for translation initiation site prediction is now available. These tools and materials may open an avenue for future improvements in start site prediction and EST analysis.

     

FITBAR: a web tool for the robust prediction of prokaryotic regulons

Background  

The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.     

MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

Background  

Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes.     

Leaderless genes in bacteria: clue to the evolution of translation initiation mechanisms in prokaryotes

Background

Shine-Dalgarno (SD) signal has long been viewed as the dominant translation initiation signal in prokaryotes. Recently, leaderless genes, which lack 5'-untranslated regions (5'-UTR) on their mRNAs, have been shown abundant in archaea. However, current large-scale in silico analyses on initiation mechanisms in bacteria are mainly based on the SD-led initiation way, other than the leaderless one. The study of leaderless genes in bacteria remains open, which causes uncertain understanding of translation initiation mechanisms for prokaryotes.

Results

Here, we study signals in translation initiation regions of all genes over 953 bacterial and 72 archaeal genomes, then make an effort to construct an evolutionary scenario in view of leaderless genes in bacteria. With an algorithm designed to identify multi-signal in upstream regions of genes for a genome, we classify all genes into SD-led, TA-led and atypical genes according to the category of the most probable signal in their upstream sequences. Particularly, occurrence of TA-like signals about 10 bp upstream to translation initiation site (TIS) in bacteria most probably means leaderless genes.

Conclusions

Our analysis reveals that leaderless genes are totally widespread, although not dominant, in a variety of bacteria. Especially for Actinobacteria and Deinococcus-Thermus, more than twenty percent of genes are leaderless. Analyzed in closely related bacterial genomes, our results imply that the change of translation initiation mechanisms, which happens between the genes deriving from a common ancestor, is linearly dependent on the phylogenetic relationship. Analysis on the macroevolution of leaderless genes further shows that the proportion of leaderless genes in bacteria has a decreasing trend in evolution.

     

Re-annotation of genome microbial CoDing-Sequences: finding new genes and inaccurately annotated genes

Background  

Analysis of any newly sequenced bacterial genome starts with the identification of protein-coding genes. Despite the accumulation of multiple complete genome sequences, which provide useful comparisons with close relatives among other organisms during the annotation process, accurate gene prediction remains quite difficult. A major reason for this situation is that genes are tightly packed in prokaryotes, resulting in frequent overlap. Thus, detection of translation initiation sites and/or selection of the correct coding regions remain difficult unless appropriate biological knowledge (about the structure of a gene) is imbedded in the approach.     

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.     

Mutation and selection on the wobble nucleotide in tRNA anticodons in marine bivalve mitochondrial genomes

Background

Animal mitochondrial genomes typically encode one tRNA for each synonymous codon family, so that each tRNA anticodon essentially has to wobble to recognize two or four synonymous codons. Several factors have been hypothesized to determine the nucleotide at the wobble site of a tRNA anticodon in mitochondrial genomes, such as the codon-anticodon adaptation hypothesis, the wobble versatility hypothesis, the translation initiation and elongation conflict hypothesis, and the wobble cost hypothesis.

Principal Findings

In this study, we analyzed codon usage and tRNA anticodon wobble sites of 29 marine bivalve mitochondrial genomes to evaluate features of the wobble nucleotides in tRNA anticodons. The strand-specific mutation bias favors G and T on the H strand in all the 29 marine bivalve mitochondrial genomes. A bias favoring G and T is also visible in the third codon positions of protein-coding genes and the wobble sites of anticodons, rejecting that codon usage bias drives the wobble sites of tRNA anticodons or tRNA anticodon bias drives the evolution of codon usage. Almost all codon families (98.9%) from marine bivalve mitogenomes support the wobble versatility hypothesis. There are a few interesting exceptions involving tRNA^Trp with an anticodon CCA fixed in Pectinoida species, tRNA^Ser with a GCU anticodon fixed in Mytiloida mitogenomes, and the uniform anticodon CAU of tRNA^Met translating the AUR codon family.

Conclusions/Significance

These results demonstrate that most of the nucleotides at the wobble sites of tRNA anticodons in marine bivalve mitogenomes are determined by wobble versatility. Other factors such as the translation initiation and elongation conflict, and the cost of wobble translation may contribute to the determination of the wobble nucleotide in tRNA anticodons. The finding presented here provides valuable insights into the previous hypotheses of the wobble nucleotide in tRNA anticodons by adding some new evidence.     

Computational evaluation of TIS annotation for prokaryotic genomes

Background  

Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.     

PoGO: Prediction of Gene Ontology terms for fungal proteins

Background  

Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to one species, are not available for high-volume data processing, or require the use of data derived by experiments such as microarray analysis. To meet the increasing need for high throughput, automated annotation of fungal genomes, we have developed a tool for annotating fungal protein sequences with terms from the Gene Ontology.     

AT excursion: a new approach to predict replication origins in viral genomes by locating AT-rich regions

Background  

Replication origins are considered important sites for understanding the molecular mechanisms involved in DNA replication. Many computational methods have been developed for predicting their locations in archaeal, bacterial and eukaryotic genomes. However, a prediction method designed for a particular kind of genomes might not work well for another. In this paper, we propose the AT excursion method, which is a score-based approach, to quantify local AT abundance in genomic sequences and use the identified high scoring segments for predicting replication origins. This method has the advantages of requiring no preset window size and having rigorous criteria to evaluate statistical significance of high scoring segments.     

A database of phylogenetically atypical genes in archaeal and bacterial genomes,identified using the <Emphasis Type="Italic">DarkHorse</Emphasis> algorithm

Background  

The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not.     

Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

Background  

MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.     

Selective prediction of interaction sites in protein structures with THEMATICS

Background  

Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS), a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites.