Additional gene ontology structure for improved biological reasoning

MOTIVATION: The Gene Ontology (GO) is a widely used terminology for gene product characterization in, for example, interpretation of biology underlying microarray experiments. The current GO defines term relationships within each of the independent subontologies: molecular function, biological process and cellular component. However, it is evident that there also exist biological relationships between terms of different subontologies. Our aim was to connect the three subontologies to enable GO to cover more biological knowledge, enable a more consistent use of GO and provide new opportunities for biological reasoning. RESULTS: We propose a new structure, the Second Gene Ontology Layer, capturing biological relations not directly reflected in the present ontology structure. Given molecular functions, these paths identify biological processes where the molecular functions are involved and cellular components where they are active. The current Second Layer contains 6271 validated paths, covering 54% of the molecular functions of GO and can be used to render existing gene annotation sets more complete and consistent. Applying Second Layer paths to a set of 4223 human genes, increased biological process annotations by 24% compared to publicly available annotations and reproduced 30% of them. AVAILABILITY: The Second GO is publicly available through the GO Annotation Toolbox (GOAT.no): http://www.goat.no.     

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.     

Mapping the gene ontology into the unified medical language system

We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine's Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems. After some initial explorations and discussions between the UMLS and GO teams, the GO was integrated with the UMLS. Overall, a total of 23% of the GO terms either matched directly (3%) or linked (20%) to existing UMLS concepts. All GO terms now have a corresponding, official UMLS concept, and the entire vocabulary is available through the web-based UMLS Knowledge Source Server. The mapping of the Gene Ontology, with its focus on structures, processes and functions at the molecular level, to the existing broad coverage UMLS should contribute to linking the language and practices of clinical medicine to the language and practices of genomics.     

Knowledge acquisition,consistency checking and concurrency control for Gene Ontology (GO)

MOTIVATION: A critical element of the computational infrastructure required for functional genomics is a shared language for communicating biological data and knowledge. The Gene Ontology (GO; http://www.geneontology.org) provides a taxonomy of concepts and their attributes for annotating gene products. As GO increases in size, its ongoing construction and maintenance becomes more challenging. In this paper, we assess the applicability of a Knowledge Base Management System (KBMS), Protégé-2000, to the maintenance and development of GO. RESULTS: We transferred GO to Protégé-2000 in order to evaluate its suitability for GO. The graphical user interface supported browsing and editing of GO. Tools for consistency checking identified minor inconsistencies in GO and opportunities to reduce redundancy in its representation. The Protégé Axiom Language proved useful for checking ontological consistency. The PROMPT tool allowed us to track changes to GO. Using Protégé-2000, we tested our ability to make changes and extensions to GO to refine the semantics of attributes and classify more concepts. AVAILABILITY: Gene Ontology in Protégé-2000 and the associated code are located at http://smi.stanford.edu/projects/helix/gokbms/. Protégé-2000 is available from http://protege.stanford.edu.     

The language of gene ontology: a Zipf's law analysis

ABSTRACT: BACKGROUND: Most major genome projects and sequence databases provide a GO annotation of their data,either automatically or through human annotators, creating a large corpus of data written inthe language of GO. Texts written in natural language show a statistical power law behaviour,Zipf's law, the exponent of which can provide useful information on the nature of thelanguage being used. We have therefore explored the hypothesis that collections of GOannotations will show similar statistical behaviours to natural language. RESULTS: Annotations from the Gene Ontology Annotation project were found to follow Zipf's law.Surprisingly, the measured power law exponents were consistently different betweenannotation captured using the three GO sub-ontologies in the corpora (function, process andcomponent). On filtering the corpora using GO evidence codes we found that the value of themeasured power law exponent responded in a predictable way as a function of the evidencecodes used to support the annotation. CONCLUSIONS: Techniques from computational linguistics can provide new insights into the annotationprocess. GO annotations show similar statistical behaviours to those seen in natural languagewith measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent mightprovide a signal regarding the information content of the annotation.     

Ontology development for biological systems: immunology

We recently implemented improvements to the representation of immunology content of the biological process branch of the Gene Ontology (GO). The aims of the revision were to provide a comprehensive representation of immunological processes and to improve the organization of immunology related terms in the GO to match current concepts in the field of immunology. With these improvements, the GO will better reflect current understanding in the field of immunology and thus prove to be a more valuable resource for knowledge representation in gene annotation and analysis in the areas of immunology related to genomics and bioinformatics. AVAILABILITY: http://www.geneontology.org.     

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.     

Combining Evidence, Specificity, and Proximity towards the Normalization of Gene Ontology Terms in Text

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the _cellular component_ subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.     

Improving missing value estimation in microarray data with gene ontology

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.     

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.     

PedGenie: meta genetic association testing in mixed family and case-control designs

Background

With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the analysis of such data often there is the need to further explore the similarity of genes not only with respect to their expression, but also with respect to their functional annotation which can be obtained from Gene Ontology (GO).

Results

We present the freely available software package GOSim, which allows to calculate the functional similarity of genes based on various information theoretic similarity concepts for GO terms. GOSim extends existing tools by providing additional lately developed functional similarity measures for genes. These can e.g. be used to cluster genes according to their biological function. Vice versa, they can also be used to evaluate the homogeneity of a given grouping of genes with respect to their GO annotation. GOSim hence provides the researcher with a flexible and powerful tool to combine knowledge stored in GO with experimental data. It can be seen as complementary to other tools that, for instance, search for significantly overrepresented GO terms within a given group of genes.

Conclusion

GOSim is implemented as a package for the statistical computing environment R and is distributed under GPL within the CRAN project.     

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

ABSTRACT: BACKGROUND: Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. RESULTS: We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. CONCLUSIONS: Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses such data. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.     

Analysis of protein function and its prediction from amino acid sequence

Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in the context of human disease because many conditions arise as a consequence of alterations of protein function. The recent availability of relatively inexpensive sequencing technology has resulted in thousands of complete or partially sequenced genomes with millions of functionally uncharacterized proteins. Such a large volume of data, combined with the lack of high-throughput experimental assays to functionally annotate proteins, attributes to the growing importance of automated function prediction. Here, we study proteins annotated by Gene Ontology (GO) terms and estimate the accuracy of functional transfer from protein sequence only. We find that the transfer of GO terms by pairwise sequence alignments is only moderately accurate, showing a surprisingly small influence of sequence identity (SID) in a broad range (30-100%). We developed and evaluated a new predictor of protein function, functional annotator (FANN), from amino acid sequence. The predictor exploits a multioutput neural network framework which is well suited to simultaneously modeling dependencies between functional terms. Experiments provide evidence that FANN-GO (predictor of GO terms; available from http://www.informatics.indiana.edu/predrag) outperforms standard methods such as transfer by global or local SID as well as GOtcha, a method that incorporates the structure of GO.     

Functional annotation of hierarchical modularity

In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology) and the association of individual genes or proteins with these concepts (e.g., GO terms), our method will assign a Hierarchical Modularity Score (HMS) to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our method using Saccharomyces cerevisiae data from KEGG and MIPS databases and several other computationally derived and curated datasets. The code and additional supplemental files can be obtained from http://code.google.com/p/functional-annotation-of-hierarchical-modularity/ (Accessed 2012 March 13).     

Bringing ontology to the gene ontology

We present an analysis of some considerations involved in expressing the Gene Ontology (GO) as a machine-processible ontology, reflecting principles of formal ontology. GO is a controlled vocabulary that is intended to facilitate communication between biologists by standardizing usage of terms in database annotations. Making such controlled vocabularies maximally useful in support of bioinformatics applications requires explicating in machine-processible form the implicit background information that enables human users to interpret the meaning of the vocabulary terms. In the case of GO, this process would involve rendering the meanings of GO into a formal (logical) language with the help of domain experts, and adding additional information required to support the chosen formalization. A controlled vocabulary augmented in these ways is commonly called an ontology. In this paper, we make a modest exploration to determine the ontological requirements for this extended version of GO. Using the terms within the three GO hierarchies (molecular function, biological process and cellular component), we investigate the facility with which GO concepts can be ontologized, using available tools from the philosophical and ontological engineering literature.     

ADGO: analysis of differentially expressed gene sets using composite GO annotation

MOTIVATION: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. RESULTS: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. AVAILABILITY: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. CONTACT: chu@kribb.re.kr SUPPLEMENTARY INFORMATION: http://array.kobic.re.kr/ADGO.