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 .     

An application of MeSH enrichment analysis in livestock

An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.     

Novel search method for the discovery of functional relationships

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.     

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).     

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.     

A fast structural multiple alignment method for long RNA sequences

Background

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.

Results

In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.

Conclusion

GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.     

The Gene Ontology's Reference Genome Project: A Unified Framework for Functional Annotation across Species

The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.     

单细胞基因组学分析的技术前沿

基因组学已经深刻地改变了生命科学的诸多领域的面貌。目前它的主要内容是新的全基因组碱基序列的测定和在全基因组范围内鉴定那些在不同水平上影响生命活动的基因群的功能和相互作用。为达此要求,近年出现的第二代测序(深度测序)技术和基因芯片技术发挥了关键作用,但是两者都需要足够的高质量的核酸样品。所以,在只有或只能用单细胞或极少量细胞的情况下,如果没有特殊手段,上述分析往往不能常规、方便地进行。文章以DNA扩增为主线,综合阐述了目前在单细胞(特别是微生物)全基因组测序和大基因组的靶向重测序,以及对单细胞或微量细胞进行的基于深度测序或芯片杂交的功能基因组分析,如转录组、ChIP和DNA的CpG甲基化分析等的最新策略和技术,评价了单细胞基因组测序和功能基因组学各技术的特点并对发展前景进行了展望。     

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.].     

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.     

Functional modelling of an equine bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences

The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort.     

Fuzzy measures on the Gene Ontology for gene product similarity

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.     

The what, where, how and why of gene ontology--a primer for bioinformaticians

With high-throughput technologies providing vast amounts of data, it has become more important to provide systematic, quality annotations. The Gene Ontology (GO) project is the largest resource for cataloguing gene function. Nonetheless, its use is not yet ubiquitous and is still fraught with pitfalls. In this review, we provide a short primer to the GO for bioinformaticians. We summarize important aspects of the structure of the ontology, describe sources and types of functional annotations, survey measures of GO annotation similarity, review typical uses of GO and discuss other important considerations pertaining to the use of GO in bioinformatics applications.     

The translation factors of Drosophila melanogaster

Synthesis of polypeptides from mRNA (translation) is a fundamental cellular process that is coordinated and catalyzed by a set of canonical ‘translation factors’. Surprisingly, the translation factors of Drosophila melanogaster have not yet been systematically identified, leading to inconsistencies in their nomenclature and shortcomings in functional (Gene Ontology, GO) annotations. Here, we describe the complete set of translation factors in D. melanogaster, applying nomenclature already in widespread use in other species, and revising their functional annotation. The collection comprises 43 initiation factors, 12 elongation factors, 3 release factors and 6 recycling factors, totaling 64 of which 55 are cytoplasmic and 9 are mitochondrial. We also provide an overview of notable findings and particular insights derived from Drosophila about these factors. This catalog, together with the incorporation of the improved nomenclature and GO annotation into FlyBase, will greatly facilitate access to information about the functional roles of these important proteins.     

Information theory applied to the sparse gene ontology annotation network to predict novel gene function

MOTIVATION: Despite advances in the gene annotation process, the functions of a large portion of gene products remain insufficiently characterized. In addition, the in silico prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or functional genomic approaches. To our knowledge, no prediction method has been demonstrated to be highly accurate for sparsely annotated GO terms (those associated to fewer than 10 genes). RESULTS: We propose a novel approach, information theory-based semantic similarity (ITSS), to automatically predict molecular functions of genes based on existing GO annotations. Using a 10-fold cross-validation, we demonstrate that the ITSS algorithm obtains prediction accuracies (precision 97%, recall 77%) comparable to other machine learning algorithms when compared in similar conditions over densely annotated portions of the GO datasets. This method is able to generate highly accurate predictions in sparsely annotated portions of GO, where previous algorithms have failed. As a result, our technique generates an order of magnitude more functional predictions than previous methods. A 10-fold cross validation demonstrated a precision of 90% at a recall of 36% for the algorithm over sparsely annotated networks of the recent GO annotations (about 1400 GO terms and 11,000 genes in Homo sapiens). To our knowledge, this article presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions than more widely used cross-validation approaches. By manually assessing a random sample of 100 predictions conducted in a historical rollback evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43-58%) can be achieved for the human GO Annotation file dated 2003. AVAILABILITY: The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset and other supplementary information is available at http://phenos.bsd.uchicago.edu/ITSS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Ontology annotation: mapping genomic regions to biological function

With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.