ProTISA: a comprehensive resource for translation initiation site annotation in prokaryotic genomes

Correct annotation of translation initiation site (TIS) is essential for both experiments and bioinformatics studies of prokaryotic translation initiation mechanism as well as understanding of gene regulation and gene structure. Here we describe a comprehensive database ProTISA, which collects TIS confirmed through a variety of available evidences for prokaryotic genomes, including Swiss-Prot experiments record, literature, conserved domain hits and sequence alignment between orthologous genes. Moreover, by combining the predictions from our recently developed TIS post-processor, ProTISA provides a refined annotation for the public database RefSeq. Furthermore, the database annotates the potential regulatory signals associated with translation initiation at the TIS upstream region. As of July 2007, ProTISA includes 440 microbial genomes with more than 390 000 confirmed TISs. The database is available at http://mech.ctb.pku.edu.cn/protis

Prodigal: prokaryotic gene recognition and translation initiation site identification

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

Ribosome signatures aid bacterial translation initiation site identification

Background: While methods for annotation of genes are increasingly reliable, the exact identification of translation initiation sites remains a challenging problem. Since the N-termini of proteins often contain regulatory and targeting information, developing a robust method for start site identification is crucial. Ribosome profiling reads show distinct patterns of read length distributions around translation initiation sites. These patterns are typically lost in standard ribosome profiling analysis pipelines, when reads from footprints are adjusted to determine the specific codon being translated. Results: Utilising these signatures in combination with nucleotide sequence information, we build a model capable of predicting translation initiation sites and demonstrate its high accuracy using N-terminal proteomics. Applying this to prokaryotic translatomes, we re-annotate translation initiation sites and provide evidence of N-terminal truncations and extensions of previously annotated coding sequences. These re-annotations are supported by the presence of structural and sequence-based features next to N-terminal peptide evidence. Finally, our model identifies 61 novel genes previously undiscovered in the Salmonella enterica genome. Conclusions: Signatures within ribosome profiling read length distributions can be used in combination with nucleotide sequence information to provide accurate genome-wide identification of translation initiation sites

TICO: a tool for postprocessing the predictions of prokaryotic translation initiation sites

Exact localization of the translation initiation sites (TIS) in prokaryotic genomes is difficult to achieve using conventional gene finders. We recently introduced the program TICO for postprocessing TIS predictions based on a completely unsupervised learning algorithm. The program can be utilized through our web interface at and it is also freely available as a commandline version for Linux and Windows. The latest version of our program provides a tool for visualization of the resulting TIS model. Although the underlying method is not based on any specific assumptions about characteristic sequence features of prokaryotic TIS the prediction rates of our tool are competitive on experimentally verified test data

Computational evaluation of TIS annotation for prokaryotic genomes

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, <it>i.e</it>. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.</p> <p>Conclusion</p> <p>Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.</p

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. RESULTS: We introduce a clustering algorithm for completely unsupervised scoring of potential TIS, based on positionally smoothed probability matrices. The algorithm requires an initial gene prediction and the genomic sequence of the organism to perform the reannotation. As compared with other methods for improving predictions of gene starts in bacterial genomes, our approach is not based on any specific assumptions about prokaryotic TIS. Despite the generality of the underlying algorithm, the prediction rate of our method is competitive on experimentally verified test data from E. coli and B. subtilis. Regarding genomes with high G+C content, in contrast to some previously proposed methods, our algorithm also provides good performance on P. aeruginosa, B. pseudomallei and R. solanacearum. CONCLUSION: On reliable test data we showed that our method provides good results in post-processing the predictions of the widely-used program GLIMMER. The underlying clustering algorithm is robust with respect to variations in the initial TIS annotation and does not require specific assumptions about prokaryotic gene starts. These features are particularly useful on genomes with high G+C content. The algorithm has been implemented in the tool »TICO«(TIs COrrector) which is publicly available from our web site

METHODS FOR HIGH-THROUGHPUT COMPARATIVE GENOMICS AND DISTRIBUTED SEQUENCE ANALYSIS

High-throughput sequencing has accelerated applications of genomics throughout the world. The increased production and decentralization of sequencing has also created bottlenecks in computational analysis. In this dissertation, I provide novel computational methods to improve analysis throughput in three areas: whole genome multiple alignment, pan-genome annotation, and bioinformatics workflows. To aid in the study of populations, tools are needed that can quickly compare multiple genome sequences, millions of nucleotides in length. I present a new multiple alignment tool for whole genomes, named Mugsy, that implements a novel method for identifying syntenic regions. Mugsy is computationally efficient, does not require a reference genome, and is robust in identifying a rich complement of genetic variation including duplications, rearrangements, and large-scale gain and loss of sequence in mixtures of draft and completed genome data. Mugsy is evaluated on the alignment of several dozen bacterial chromosomes on a single computer and was the fastest program evaluated for the alignment of assembled human chromosome sequences from four individuals. A distributed version of the algorithm is also described and provides increased processing throughput using multiple CPUs. Numerous individual genomes are sequenced to study diversity, evolution and classify pan-genomes. Pan-genome annotations contain inconsistencies and errors that hinder comparative analysis, even within a single species. I introduce a new tool, Mugsy-Annotator, that identifies orthologs and anomalous gene structure across a pan-genome using whole genome multiple alignments. Identified anomalies include inconsistently located translation initiation sites and disrupted genes due to draft genome sequencing or pseudogenes. An evaluation of pan-genomes indicates that such anomalies are common and alternative annotations suggested by the tool can improve annotation consistency and quality. Finally, I describe the Cloud Virtual Resource, CloVR, a desktop application for automated sequence analysis that improves usability and accessibility of bioinformatics software and cloud computing resources. CloVR is installed on a personal computer as a virtual machine and requires minimal installation, addressing challenges in deploying bioinformatics workflows. CloVR also seamlessly accesses remote cloud computing resources for improved processing throughput. In a case study, I demonstrate the portability and scalability of CloVR and evaluate the costs and resources for microbial sequence analysis

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. RESULTS: This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. CONCLUSION: Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation

Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering

Environmental shotgun sequencing (or metagenomics) is widely used to survey the communities of microbial organisms that live in many diverse ecosystems, such as the human body. Finding the protein-coding genes within the sequences is an important step for assessing the functional capacity of a metagenome. In this work, we developed a metagenomics gene prediction system Glimmer-MG that achieves significantly greater accuracy than previous systems via novel approaches to a number of important prediction subtasks. First, we introduce the use of phylogenetic classifications of the sequences to model parameterization. We also cluster the sequences, grouping together those that likely originated from the same organism. Analogous to iterative schemes that are useful for whole genomes, we retrain our models within each cluster on the initial gene predictions before making final predictions. Finally, we model both insertion/deletion and substitution sequencing errors using a different approach than previous software, allowing Glimmer-MG to change coding frame or pass through stop codons by predicting an error. In a comparison among multiple gene finding methods, Glimmer-MG makes the most sensitive and precise predictions on simulated and real metagenomes for all read lengths and error rates tested

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

<p>Abstract</p> <p>Background</p> <p>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 <it>in silico </it>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>Our analysis reveals that leaderless genes are totally widespread, although not dominant, in a variety of bacteria. Especially for <it>Actinobacteria </it>and <it>Deinococcus-Thermus</it>, 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.</p