Future tools for association mapping in crop plants

Association mapping currently relies on the identification of genetic markers. Several technologies have been adopted for genetic marker analysis, with single nucleotide polymorphisms (SNPs) being the most popular where a reasonable quantity of genome sequence data are available. We describe several tools we have developed for the discovery, annotation, and visualization of molecular markers for association mapping. These include autoSNPdb for SNP discovery from assembled sequence data; TAGdb for the identification of gene specific paired read Illumina GAII data; CMap3D for the comparison of mapped genetic and physical markers; and BAC and Gene Annotator for the online annotation of genes and genomic sequences.     

GenColors: accelerated comparative analysis and annotation of prokaryotic genomes at various stages of completeness

SUMMARY: GenColors is a new web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes, considering information on related genomes and making extensive use of genome comparison. It offers a seamless integration of data from ongoing sequencing projects and annotated genomic sequences obtained from GenBank. The genome comparison tools determine, for example, best-bidirectional hits, gene conservation, syntenies and gene core sets. Swiss-Prot/TrEMBL hits allow annotations in an effective manner. To further support the annotation base-specific quality data can also be displayed if available. With GenColors dedicated genome browsers containing a group of related genomes can be easily set up and maintained. It has been efficiently used for Borrelia garinii and is currently applied to various ongoing genome projects. AVAILABILITY: Detailed information on GenColors is available at http://gencolors.imb-jena.de. Online usage of GenColors-based genome browsers is the preferred application mode. The system is also available upon request for local installation.     

Genome sequence of Wickerhamomyces anomalus DSM 6766 reveals genetic basis of biotechnologically important antimicrobial activities

The ascomycetous yeast Wickerhamomyces anomalus (formerly Pichia anomala and Hansenula anomala) exhibits antimicrobial activities and flavoring features that are responsible for its frequent association with food, beverage and feed products. However, limited information on the genetic background of this yeast and its multiple capabilities are currently available. Here, we present the draft genome sequence of the neotype strain W.?anomalus DSM 6766. On the basis of pyrosequencing, a de novo assembly of this strain resulted in a draft genome sequence with a total size of 25.47?Mbp. An automatic annotation using RAPYD generated 11?512 protein-coding sequences. This annotation provided the basis to analyse metabolic capabilities, phylogenetic relationships, as well as biotechnologically important features and yielded novel candidate genes of W.?anomalus DSM 6766 coding for proteins participating in antimicrobial activities.     

Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thaliana sequences

MOTIVATION: The annotation of the Arabidopsis thaliana genome remains a problem in terms of time and quality. To improve the annotation process, we want to choose the most appropriate tools to use inside a computer-assisted annotation platform. We therefore need evaluation of prediction programs with Arabidopsis sequences containing multiple genes. RESULTS: We have developed AraSet, a data set of contigs of validated genes, enabling the evaluation of multi-gene models for the Arabidopsis genome. Besides conventional metrics to evaluate gene prediction at the site and the exon levels, new measures were introduced for the prediction at the protein sequence level as well as for the evaluation of gene models. This evaluation method is of general interest and could apply to any new gene prediction software and to any eukaryotic genome. The GeneMark.hmm program appears to be the most accurate software at all three levels for the Arabidopsis genomic sequences. Gene modeling could be further improved by combination of prediction software. AVAILABILITY: The AraSet sequence set, the Perl programs and complementary results and notes are available at http://sphinx.rug.ac.be:8080/biocomp/napav/. CONTACT: Pierre.Rouze@gengenp.rug.ac.be.     

Solving the Problem: Genome Annotation Standards before the Data Deluge

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries.     

Towards multidimensional genome annotation

Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.     

ProFASTA: a pipeline web server for fungal protein scanning with integration of cell surface prediction software

Surface proteins, such as those located in the cell wall of fungi, play an important role in the interaction with the surrounding environment. For instance, they mediate primary host-pathogen interactions and are crucial to the establishment of biofilms and fungal infections. Surface localization of proteins is determined by specific sequence features and can be predicted by combining different freely available web servers. However, user-friendly tools that allow rapid analysis of large datasets (whole proteomes or larger) in subsequent analyses were not yet available. Here, we present the web tool ProFASTA, which integrates multiple tools for rapid scanning of protein sequence properties in large datasets and returns sequences in FASTA format. ProFASTA also allows for pipeline filtering of proteins with cell surface characteristics by analysis of the output created with SignalP, TMHMM and big-PI. In addition, it provides keyword, iso-electric point, composition and pattern scanning. Furthermore, ProFASTA contains all fungal protein sequences present in the NCBI Protein database. As the full fungal NCBI Taxonomy is included, sequence subsets can be selected by supplying a taxon name. The usefulness of ProFASTA is demonstrated here with a few examples; in the recent past, ProFASTA has already been applied successfully to the annotation of covalently-bound fungal wall proteins as part of community-wide genome annotation programs. ProFASTA is available at: http://www.bioinformatics.nl/tools/profasta/.     

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis （EDA） techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences （average Precision=0.88; average Sensitivity=0.86）. Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.     

Plasma membrane localization of Ras requires class C Vps proteins and functional mitochondria in Saccharomyces cerevisiae

Ras proteins are synthesized as cytosolic precursors, but then undergo posttranslational lipid addition, membrane association, and subcellular targeting to the plasma membrane. Although the enzymes responsible for farnesyl and palmitoyl lipid addition have been described, the mechanism by which these modifications contribute to the subcellular localization of Ras is not known. Following addition of the farnesyl group, Ras associates with the endoplasmic reticulum (ER), where palmitoylation occurs in Saccharomyces cerevisiae. The subsequent translocation of Ras from the ER to the plasma membrane does not require the classical secretory pathway or a functional Golgi apparatus. Vesicular and nonvesicular transport pathways for Ras proteins have been proposed, but the pathway is not known. Here we describe a genetic screen designed to identify mutants defective in Ras trafficking in S. cerevisiae. The screen implicates, for the first time, the class C VPS complex in Ras trafficking. Vps proteins are best characterized for their role in endosome and vacuole membrane fusion. However, the role of the class C Vps complex in Ras trafficking is distinct from its role in endosome and vacuole vesicle fusion, as a mitochondrial involvement was uncovered. Disruption of class C VPS genes results in mitochondrial defects and an accumulation of Ras proteins on mitochondrial membranes. Ras also fractionates with mitochondria in wild-type cells, where it is detected on the outer mitochondrial membrane by virtue of its sensitivity to protease treatment. These results point to a previously uncharacterized role of mitochondria in the subcellular trafficking of Ras proteins.     

Poxvirus orthologous clusters: toward defining the minimum essential poxvirus genome

Increasingly complex bioinformatic analysis is necessitated by the plethora of sequence information currently available. A total of 21 poxvirus genomes have now been completely sequenced and annotated, and many more genomes will be available in the next few years. First, we describe the creation of a database of continuously corrected and updated genome sequences and an easy-to-use and extremely powerful suite of software tools for the analysis of genomes, genes, and proteins. These tools are available free to all researchers and, in most cases, alleviate the need for using multiple Internet sites for analysis. Further, we describe the use of these programs to identify conserved families of genes (poxvirus orthologous clusters) and have named the software suite POCs, which is available at www.poxvirus.org. Using POCs, we have identified a set of 49 absolutely conserved gene families-those which are conserved between the highly diverged families of insect-infecting entomopoxviruses and vertebrate-infecting chordopoxviruses. An additional set of 41 gene families conserved in chordopoxviruses was also identified. Thus, 90 genes are completely conserved in chordopoxviruses and comprise the minimum essential genome, and these will make excellent drug, antibody, vaccine, and detection targets. Finally, we describe the use of these tools to identify necessary annotation and sequencing updates in poxvirus genomes. For example, using POCs, we identified 19 genes that were widely conserved in poxviruses but missing from the vaccinia virus strain Tian Tan 1998 GenBank file. We have reannotated and resequenced fragments of this genome and verified that these genes are conserved in Tian Tan. The results for poxvirus genes and genomes are discussed in light of evolutionary processes.     

In Silico Characterization of Proteins: UniProt,InterPro and Integr8

Nucleic acid sequences from genome sequencing projects are submitted as raw data, from which biologists attempt to elucidate the function of the predicted gene products. The protein sequences are stored in public databases, such as the UniProt Knowledgebase (UniProtKB), where curators try to add predicted and experimental functional information. Protein function prediction can be done using sequence similarity searches, but an alternative approach is to use protein signatures, which classify proteins into families and domains. The major protein signature databases are available through the integrated InterPro database, which provides a classification of UniProtKB sequences. As well as characterization of proteins through protein families, many researchers are interested in analyzing the complete set of proteins from a genome (i.e. the proteome), and there are databases and resources that provide non-redundant proteome sets and analyses of proteins from organisms with completely sequenced genomes. This article reviews the tools and resources available on the web for single and large-scale protein characterization and whole proteome analysis.     

Proteogenomic analysis of Mycobacterium tuberculosis by high resolution mass spectrometry

The genome sequencing of H37Rv strain of Mycobacterium tuberculosis was completed in 1998 followed by the whole genome sequencing of a clinical isolate, CDC1551 in 2002. Since then, the genomic sequences of a number of other strains have become available making it one of the better studied pathogenic bacterial species at the genomic level. However, annotation of its genome remains challenging because of high GC content and dissimilarity to other model prokaryotes. To this end, we carried out an in-depth proteogenomic analysis of the M. tuberculosis H37Rv strain using Fourier transform mass spectrometry with high resolution at both MS and tandem MS levels. In all, we identified 3176 proteins from Mycobacterium tuberculosis representing ~80% of its total predicted gene count. In addition to protein database search, we carried out a genome database search, which led to identification of ~250 novel peptides. Based on these novel genome search-specific peptides, we discovered 41 novel protein coding genes in the H37Rv genome. Using peptide evidence and alternative gene prediction tools, we also corrected 79 gene models. Finally, mass spectrometric data from N terminus-derived peptides confirmed 727 existing annotations for translational start sites while correcting those for 33 proteins. We report creation of a high confidence set of protein coding regions in Mycobacterium tuberculosis genome obtained by high resolution tandem mass-spectrometry at both precursor and fragment detection steps for the first time. This proteogenomic approach should be generally applicable to other organisms whose genomes have already been sequenced for obtaining a more accurate catalogue of protein-coding genes.     

Knowledge-based selection of targets for structural genomics

The problem of rational target selection for protein structure determination in structural genomics projects on microbes is addressed. A flexible computational procedure is described that directly incorporates the whole body of annotation available in the PEDANT genome database into the sequence clustering and selection process in order to identify proteins that are likely to possess currently unknown structural domains. Filtering out gene products based on predicted structural features, such as known three-dimensional structures and transmembrane regions, allows one to reduce the complexity of neighbor relationships between sequences and all but eliminates the need for further partitioning of single-linkage clusters into disjoint protein groups corresponding to homologous families. The results of a large-scale computation experiment in which exemplary target selection for 32 prokaryotic genomes was conducted are presented.     

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.     

The COG database: a tool for genome-scale analysis of protein functions and evolution

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.     

Prediction of Certain Well-Characterized Domains of Known Functions within the PE and PPE Proteins of Mycobacteria

The PE and PPE protein family are unique to mycobacteria. Though the complete genome sequences for over 500 M. tuberculosis strains and mycobacterial species are available, few PE and PPE proteins have been structurally and functionally characterized. We have therefore used bioinformatics tools to characterize the structure and function of these proteins. We selected representative members of the PE and PPE protein family by phylogeny analysis and using structure-based sequence annotation identified ten well-characterized protein domains of known function. Some of these domains were observed to be common to all mycobacterial species and some were species specific.     

Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.