Identification of signature genes for rapid and specific characterization of Yersinia pestis

Polymerase chain reaction (PCR) amplification of DNA-based unique markers, the signature sequences, is ideal for rapid detection and identification of pathogens. We described the discovery of twenty-eight signature genes of Yersinia pestis by DNA microarray-based comparative genome hybridization in conjunction with PCR validation. Three pairs of Y. pestis-specific primers designed from signature genes were demonstrated to have the expected specificity to this target bacterium, without cross-reaction with the closely related Y. pseudotuberculosis or a large collection of genomic DNAs from other organisms.     

Automated bacterial genome analysis and annotation

More than 300 bacterial genome sequences are publicly available, and many more are scheduled to be completed and released in the near future. Converting this raw sequence information into a better understanding of the biology of bacteria involves the identification and annotation of genes, proteins and pathways. This processing is typically done using sequence annotation pipelines comprised of a variety of software modules and, in some cases, human experts. The reference databases, computational methods and knowledge that form the basis of these pipelines are constantly evolving, and thus there is a need to reprocess genome annotations on a regular basis. The combined challenge of revising existing annotations and extracting useful information from the flood of new genome sequences will necessitate more reliance on completely automated systems.     

Evaluation of annotation strategies using an entire genome sequence

MOTIVATION: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. RESULTS: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences.     

Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary

MOTIVATION: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. RESULTS: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.     

Accessing the SEED Genome Databases via Web Services API: Tools for Programmers

Background  

The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups.     

Validating divergent ORF annotation of the Mycobacterium leprae genome through a full translation data set and peptide identification by tandem mass spectrometry

Mycobacterium leprae has undergone extensive degenerative evolution, with a large number of pseudogenes. It is also the organism with the greatest divergence between gene annotations from independent institutes. Therefore, M. leprae is a good model to verify the currently predicted coding sequence regions between different annotations, to identify new ones and to investigate the expression of pseudogenes. We submitted a total extract of the bacteria isolated from Armadillo to Gel‐LC‐MS/MS using a linear quadrupole ion trap‐Orbitrap mass spectrometer. Spectra were analyzed using the Leproma (1614 genes and 1133 pseudogenes) and TIGR (5446 genes) databases and a database containing the full genome translation. We identified a total of 1046 proteins, including five proteins encoded by previously predicted pseudogenes, which upon closer inspection appeared to be proper genes. Only 11 of the additional annotations by TIGR were verified. We also identified six tryptic peptides from five proteins from regions not considered to be coding sequences, in addition to peptides from two unannotated gene candidates that overlap with other genes. Our data show that the Leproma annotation of M. leprae is quite accurate, and there were no peptide observations corresponding to true pseudogenes, except for a new gene candidate, overlapping with an essential enolase on the complementary strand.     

Consistent over-estimation of gene number in complex plant genomes

The first comprehensive comparison of gene content between higher plant species provided the unexpected conclusions that rice contained about twice as many genes as Arabidopsis, and that about half of the rice genes had no obvious homologs in any other organism. Our subsequent analyses indicate that most of these "extra, novel" rice genes are mis-annotated segments of transposable elements, especially retrotransposons. Aggressive annotation of a randomly selected subset of the rice genome suggests that the gene number is less than 40000. The five fantasies of automated plant gene discovery are described and a protocol is provided to minimize (or at least predict) the inaccuracy of future plant genome annotations.     

Complete reannotation of the Arabidopsis genome: methods, tools, protocols and the final release

Background

Since the initial publication of its complete genome sequence, Arabidopsis thaliana has become more important than ever as a model for plant research. However, the initial genome annotation was submitted by multiple centers using inconsistent methods, making the data difficult to use for many applications.

Results

Over the course of three years, TIGR has completed its effort to standardize the structural and functional annotation of the Arabidopsis genome. Using both manual and automated methods, Arabidopsis gene structures were refined and gene products were renamed and assigned to Gene Ontology categories. We present an overview of the methods employed, tools developed, and protocols followed, summarizing the contents of each data release with special emphasis on our final annotation release (version 5).

Conclusion

Over the entire period, several thousand new genes and pseudogenes were added to the annotation. Approximately one third of the originally annotated gene models were significantly refined yielding improved gene structure annotations, and every protein-coding gene was manually inspected and classified using Gene Ontology terms.     

The Génolevures database

The Génolevures online database (URL: http://www.genolevures.org) stores and provides the data and results obtained by the Génolevures Consortium through several campaigns of genome annotation of the yeasts in the Saccharomycotina subphylum (hemiascomycetes). This database is dedicated to large-scale comparison of these genomes, storing not only the different chromosomal elements detected in the sequences, but also the logical relations between them. The database is divided into a public part, accessible to anyone through Internet, and a private part where the Consortium members make genome annotations with our Magus annotation system; this system is used to annotate several related genomes in parallel. The public database is widely consulted and offers structured data, organized using a REST web site architecture that allows for automated requests. The implementation of the database, as well as its associated tools and methods, is evolving to cope with the influx of genome sequences produced by Next Generation Sequencing (NGS).     

Prediction of functionally related proteins by comparative genomics in silico

The review considers the computational prediction of functionally related proteins by comparative genomics. Growing possibilities of biotechnology for genome sequencing lead to generation of sequences for millions of genes. However, functions of majority of these genes remain unknown, and can be determined experimentally only for a few of them. Therefore, accurate and robust methods for in silico prediction (annotation) of gene functions are needed. We describe here the main techniques of comparative genomics, including the standard method based on transferring functions between homologous sequences and also context-based methods, including phylogenetic profiles and gene-neighbor approaches. Modern methods of comparative genomics allow obtaining correct functional annotations for more than a half of all organism proteins.     

BEACON: automated tool for Bacterial GEnome Annotation ComparisON

Background

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).

Results

The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.

Conclusions

We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.     

Proteogenomics: needs and roles to be filled by proteomics in genome annotation

While genome sequencing efforts reveal the basic building blocksof life, a genome sequence alone is insufficient for elucidatingbiological function. Genome annotation—the process ofidentifying genes and assigning function to each gene in a genomesequence—provides the means to elucidate biological functionfrom sequence. Current state-of-the-art high-throughput genomeannotation uses a combination of comparative (sequence similaritydata) and non-comparative (ab initio gene prediction algorithms)methods to identify protein-coding genes in genome sequences.Because approaches used to validate the presence of predictedprotein-coding genes are typically based on expressed RNA sequences,they cannot independently and unequivocally determine whethera predicted protein-coding gene is translated into a protein.With the ability to directly measure peptides arising from expressedproteins, high-throughput liquid chromatography-tandem massspectrometry-based proteomics approaches can be used to verifycoding regions of a genomic sequence. Here, we highlight severalways in which high-throughput tandem mass spectrometry-basedproteomics can improve the quality of genome annotations andsuggest that it could be efficiently applied during the genecalling process so that the improvements are propagated throughthe subsequent functional annotation process.      

REGANOR

With >1,000 prokaryotic genome sequencing projects ongoing or already finished, comprehensive comparative analysis of the gene content of these genomes has become viable. To allow for a meaningful comparative analysis, gene prediction of the various genomes should be as accurate as possible. It is clear that improving the state of genome annotation requires automated gene identification methods to cope with the influence of artifacts, such as genomic GC content. There is currently still room for improvement in the state of annotations. We present a web server and a database of high-quality gene predictions. The web server is a resource for gene identification in prokaryote genome sequences. It implements our previously described, accurate gene finding method REGANOR. We also provide novel gene predictions for 241 complete, or almost complete, prokaryotic genomes. We demonstrate how this resource can easily be utilised to identify promising candidates for currently missing genes from genome annotations with several examples. All data sets are available online. AVAILABILITY: The gene finding server is accessible via https://www.cebitec.uni-bielefeld.de/groups/brf/software/reganor/cgi-bin/reganor_upload.cgi. The server software is available with the GenDB genome annotation system (version 2.2.1 onwards) under the GNU general public license. The software can be downloaded from https://sourceforge.net/projects/gendb/. More information on installing GenDB and REGANOR and the system requirements can be found on the GenDB project page http://www.cebitec.uni-bielefeld.de/groups/brf/software/wiki/GenDBWiki/AdministratorDocumentation/GenDBInstallation     

The IGS Standard Operating Procedure for Automated Prokaryotic Annotation

The Institute for Genome Sciences (IGS) has developed a prokaryotic annotation pipeline that is used for coding gene/RNA prediction and functional annotation of Bacteria and Archaea. The fully automated pipeline accepts one or many genomic sequences as input and produces output in a variety of standard formats. Functional annotation is primarily based on similarity searches and motif finding combined with a hierarchical rule based annotation system. The output annotations can also be loaded into a relational database and accessed through visualization tools.     

RAPYD--rapid annotation platform for yeast data

Lower eukaryotes of the kingdom Fungi include a variety of biotechnologically important yeast species that are in the focus of genome research for more than a decade. Due to the rapid progress in ultra-fast sequencing technologies, the amount of available yeast genome data increases steadily. Thus, an efficient bioinformatics platform is required that covers genome assembly, eukaryotic gene prediction, genome annotation, comparative yeast genomics, and metabolic pathway reconstruction. Here, we present a bioinformatics platform for yeast genomics named RAPYD addressing the key requirements of extensive yeast sequence data analysis. The first step is a comprehensive regional and functional annotation of a yeast genome. A region prediction pipeline was implemented to obtain reliable and high-quality predictions of coding sequences and further genome features. Functions of coding sequences are automatically determined using a configurable prediction pipeline. Based on the resulting functional annotations, a metabolic pathway reconstruction module can be utilized to rapidly generate an overview of organism-specific features and metabolic blueprints. In a final analysis step shared and divergent features of closely related yeast strains can be explored using the comparative genomics module. An in-depth application example of the yeast Meyerozyma guilliermondii illustrates the functionality of RAPYD. A user-friendly web interface is available at https://rapyd.cebitec.uni-bielefeld.de.