Get ready to GO! A biologist's guide to the Gene Ontology

The Gene Ontology (GO) project provides a controlled vocabulary to facilitate high-quality functional gene annotation for all species. Genes in biological databases are linked to GO terms, allowing biologists to ask questions about gene function in a manner independent of species. This tutorial provides an introduction for biologists to the GO resources and covers three of the most common methods of querying GO: by individual gene, by gene function and by using a list of genes. [For the sake of brevity, the term 'gene' is used throughout this paper to refer to genes and their products (proteins and RNAs). GO annotations are always based on the characteristics of gene products, even though it may be the gene that is cited in the annotation.].     

GOChase: correcting errors from Gene Ontology-based annotations for gene products

SUMMARY: The Gene Ontology (GO) is a controlled biological vocabulary that provides three structured networks of terms to describe biological processes, cellular components and molecular functions. Many databases of gene products are annotated using the GO vocabularies. We found that some GO-updating operations are not easily traceable by the current biological databases and GO browsers. Consequently, numerous annotation errors arise and are propagated throughout biological databases and GO-based high-level analyses. GOChase is a set of web-based utilities to detect and correct the errors in GO-based annotations.     

Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data

Next‐generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan ‘BIN’ ontology, which is tailored for functional annotation of plant ‘omics’ data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan‐to‐GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator .     

The Zebrafish Information Network (ZFIN): the zebrafish model organism database

The Zebrafish Information Network (ZFIN) is a web based community resource that serves as a centralized location for the curation and integration of zebrafish genetic, genomic and developmental data. ZFIN is publicly accessible at http://zfin.org. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community contact data. Recent enhancements to ZFIN include: (i) an anatomical dictionary that provides a controlled vocabulary of anatomical terms, grouped by developmental stages, that may be used to annotate and query gene expression data; (ii) gene expression data; (iii) expanded support for genome sequence; (iv) gene annotation using the standardized vocabulary of Gene Ontology (GO) terms that can be used to elucidate relationships between gene products in zebrafish and other organisms; and (v) collaborations with other databases (NCBI, Sanger Institute and SWISS-PROT) to provide standardization and interconnections based on shared curation.     

SimHap GUI: An intuitive graphical user interface for genetic association analysis

Background

The expressed sequence tag (EST) methodology is an attractive option for the generation of sequence data for species for which no completely sequenced genome is available. The annotation and comparative analysis of such datasets poses a formidable challenge for research groups that do not have the bioinformatics infrastructure of major genome sequencing centres. Therefore, there is a need for user-friendly tools to facilitate the annotation of non-model species EST datasets with well-defined ontologies that enable meaningful cross-species comparisons. To address this, we have developed annot8r, a platform for the rapid annotation of EST datasets with GO-terms, EC-numbers and KEGG-pathways.

Results

annot8r automatically downloads all files relevant for the annotation process and generates a reference database that stores UniProt entries, their associated Gene Ontology (GO), Enzyme Commission (EC) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotation and additional relevant data. For each of GO, EC and KEGG, annot8r extracts a specific sequence subset from the UniProt dataset based on the information stored in the reference database. These three subsets are then formatted for BLAST searches. The user provides the protein or nucleotide sequences to be annotated and annot8r runs BLAST searches against these three subsets. The BLAST results are parsed and the corresponding annotations retrieved from the reference database. The annotations are saved both as flat files and also in a relational postgreSQL results database to facilitate more advanced searches within the results. annot8r is integrated with the PartiGene suite of EST analysis tools.

Conclusion

annot8r is a tool that assigns GO, EC and KEGG annotations for data sets resulting from EST sequencing projects both rapidly and efficiently. The benefits of an underlying relational database, flexibility and the ease of use of the program make it ideally suited for non-model species EST-sequencing projects.     

Length constraints of multi-domain proteins in metazoans

The increasing number of annotated genome sequences in public databases has made it possible to study the length distributions and domain composition of proteins at unprecedented resolution. To identify factors that influence protein length in metazoans, we performed an analysis of all domain-annotated proteins from a total of 49 animal species from Ensembl (v.56) or EnsemblMetazoa (v.3). Our results indicate that protein length constraints are not fixed as a linear function of domain count and can vary based on domain content. The presence of repeating domains was associated with relaxation of the constraints that govern protein length. Conversely, for proteins with unique domains, length constraints were generally maintained with increased domain counts. It is clear that mean (and median) protein length and domain composition vary significantly between metazoans and other kingdoms; however, the connections between function, domain content, and length are unclear. We incorporated Gene Ontology (GO) annotation to identify biological processes, cellular components, or molecular functions that favor the incorporation of multi-domain proteins. Using this approach, we identified multiple GO terms that favor the incorporation of multi-domain proteins; interestingly, several of the GO terms with elevated domain counts were not restricted to a single gene family. The findings presented here represent an important step in resolving the complex relationship between protein length, function, and domain content. The comparison of the data presented in this work to data from other kingdoms is likely to reveal additional differences in the regulation of protein length.     

TheViral MetaGenome Annotation Pipeline(VMGAP):an automated tool for the functional annotation of viral Metagenomic shotgun sequencing data

In the past few years, the field of metagenomics has been growing at an accelerated pace, particularly in response to advancements in new sequencing technologies. The large volume of sequence data from novel organisms generated by metagenomic projects has triggered the development of specialized databases and tools focused on particular groups of organisms or data types. Here we describe a pipeline for the functional annotation of viral metagenomic sequence data. The Viral MetaGenome Annotation Pipeline (VMGAP) pipeline takes advantage of a number of specialized databases, such as collections of mobile genetic elements and environmental metagenomes to improve the classification and functional prediction of viral gene products. The pipeline assigns a functional term to each predicted protein sequence following a suite of comprehensive analyses whose results are ranked according to a priority rules hierarchy. Additional annotation is provided in the form of enzyme commission (EC) numbers, GO/MeGO terms and Hidden Markov Models together with supporting evidence.     

GenBank.

The GenBank sequence database incorporates DNA sequences from all available public sources, primarily through the direct submission of sequence data from authors and from large-scale sequencing projects. Data exchange with the EMBL Data Library and the DNA Data Bank of Japan helps ensure comprehensive coverage. GenBank continues to focus on quality control and annotation while expanding data coverage and retrieval services. An integrated retrieval system, known asEntrez, incorporates data from the major DNA and protein sequence databases, along with genome maps and protein structure information. MEDLINE abstracts from published articles describing the sequences are also included as an additional source of biological annotation. Sequence similarity searching is offered through the BLAST family of programs. All of NCBI's services are offered through the World Wide Web. In addition, there are specialized server/client versions as well as FTP and e-mail server access.     

A novel neural response algorithm for protein function prediction

Background

Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.

Results

We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.

Conclusions

The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.

     

Bioverse: Functional,structural and contextual annotation of proteins and proteomes

Functional annotation is routinely performed for large-scale genomics projects and databases. Researchers working on more specific problems, for instance on an individual pathway or complex, also need to be able to quickly, completely and accurately annotate sequences. The Bioverse sequence annotation server (http://bioverse.compbio.washington.edu) provides a web-based interface to allow users to submit protein sequences to the Bioverse framework. Sequences are functionally and structurally annotated and potential contextual annotations are provided. Researchers can also submit candidate genomes for annotation of all proteins encoded by the genome (proteome).     

QAUST: Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.     

Annotating eukaryote genomes

The Genome Annotation Assessment Project tested current methods of gene identification, including a critical assessment of the accuracy of different methods. Two new databases have provided new resources for gene annotation: these are the InterPro database of protein domains and motifs, and the Gene Ontology database for terms that describe the molecular functions and biological roles of gene products. Efforts in genome annotation are most often based upon advances in computer systems that are specifically designed to deal with the tremendous amounts of data being generated by current sequencing projects. These efforts in analysis are being linked to new ways of visualizing computationally annotated genomes.     

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.     

Predicting protein subnuclear localization using GO-amino-acid composition features

The nucleus guides life processes of cells. Many of the nuclear proteins participating in the life processes tend to concentrate on subnuclear compartments. The subnuclear localization of nuclear proteins is hence important for deeply understanding the construction and functions of the nucleus. Recently, Gene Ontology (GO) annotation has been used for prediction of subnuclear localization. However, the effective use of GO terms in solving sequence-based prediction problems remains challenging, especially when query protein sequences have no accession number or annotated GO term. This study obtains homologies of query proteins with known accession numbers using BLAST to retrieve GO terms for sequence-based subnuclear localization prediction. A prediction method PGAC, which involves mining informative GO terms associated with amino acid composition features, is proposed to design a support vector machine-based classifier. PGAC yields 55 informative GO terms with training and test accuracies of 85.7% and 76.3%, respectively, using a data set SNL_35 (561 proteins in 9 localizations) with 35% sequence identity. Upon comparison with Nuc-PLoc, which combines amphiphilic pseudo amino acid composition of a protein with its position-specific scoring matrix, PGAC using the data set SNL_80 yields a leave-one-out cross-validation accuracy of 81.1%, which is better than that of Nuc-PLoc, 67.4%. Experimental results show that the set of informative GO terms are effective features for protein subnuclear localization. The prediction server based on PGAC has been implemented at http://iclab.life.nctu.edu.tw/prolocgac.     

The EBI SRS server-new features

MOTIVATION: Here we report on recent developments at the EBI SRS server (http://srs.ebi.ac.uk). SRS has become an integration system for both data retrieval and sequence analysis applications. The EBI SRS server is a primary gateway to major databases in the field of molecular biology produced and supported at EBI as well as European public access point to the MEDLINE database provided by US National Library of Medicine (NLM). It is a reference server for latest developments in data and application integration. The new additions include: concept of virtual databases, integration of XML databases like the Integrated Resource of Protein Domains and Functional Sites (InterPro), Gene Ontology (GO), MEDLINE, Metabolic pathways, etc., user friendly data representation in 'Nice views', SRSQuickSearch bookmarklets. AVAILABILITY: SRS6 is a licensed product of LION Bioscience AG freely available for academics. The EBI SRS server (http://srs.ebi.ac.uk) is a free central resource for molecular biology data as well as a reference server for the latest developments in data integration.