GOAT

Understanding the composition of gene lists that result from high-throughput experiments requires elaborate processing of gene annotation lists. In this article we present GOAT (Gene Ontology Analysis Tool), a tool based on the statistical software 'R' for analysing Gene Ontologytrade mark (GO) term enrichment in gene lists. Given a gene list, GOAT calculates the enrichment and statistical significance of every GO term and generates graphical presentations of significantly enriched terms. GOAT works for any organism with a genome-scale GO annotation and allows easy updates of ontologies and annotations. AVAILABILITY: GOAT is freely available from http://dictygenome.org/software/GOAT/ CONTACT: Gad Shaulsky (gadi@bcm.tmc.edu).     

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.     

基因本体论的蟾蜍bHLH转录因子功能富集分布

基因本体论是国际上标准的基因和蛋白质功能知识词汇.利用基因本体论的功能富集分布比较和分析了两种蟾蜍bHLH基因分子功能分布特点.结果发现,两种蟾蜍的bHLH基因均有显著富集分布的GO注释语句,其中转录调控活性( GO:0030528)、转录调控(GO:0045449)、DNA结合(GO:0003677)、RNA代谢过程调控(G0:0051252)、DNA依赖的转录调控(GO:0006355)、转录(G0:0006350)和转录因子活性(GO:0003700)等频率很高,表明这些GO注释是蟾蜍bHLH基因常见的功能;此外,蟾蜍bHLH基因在肌肉器官发育、神经管和眼发育等一些重要的发育或生理过程的基因表达调控中发挥着重要的作用.     

Gene expression alterations in Rocky Mountain elk infected with chronic wasting disease

Chronic wasting disease (CWD) is an invariably fatal neurologic disease that naturally infects mule deer, white tailed deer and elk. The understanding of CWD neurodegeneration at a molecular level is very limited. In this study, microarray analysis was performed to determine changes in the gene expression profiles in six different tissues including brain, midbrain, thalamus, spleen, RPLN and tonsil of CWD-infected elk in comparison to non-infected healthy elk, using 24,000 bovine specific oligo probes. In total, 329 genes were found to be differentially expressed (> 2.0-fold) between CWD negative and positive brain tissues, with 132 genes upregulated and 197 genes downregulated. There were 249 DE genes in the spleen (168 up- and 81 downregulated), 30 DE genes in the retropharyngeal lymph node (RPLN) (18 up- and 12 downregulated), and 55 DE genes in the tonsil (21 up- and 34 downregulated). Using Gene Ontology (GO), the DE genes were assigned to functional groups associated with cellular process, biological regulation, metabolic process, and regulation of biological process. For all brain tissues, the highest ranking networks for DE genes identified by Ingenuity Pathway Analysis (IPA) were associated with neurological disease, cell morphology, cellular assembly and organization. Quantitative real-time PCR (qRT-PCR) validated the expression of DE genes primarily involved in different regulatory pathways, including neuronal signaling and synapse function, calcium signaling, apoptosis and cell death and immune cell trafficking and inflammatory response. This is the first study to evaluate altered gene expression in multiple organs including brain from orally infected elk and the results will improve our understanding of CWD neurodegeneration at the molecular level.     

Gene expression alterations in Rocky Mountain elk infected with chronic wasting disease

Chronic wasting disease (CWD) is an invariably fatal neurologic disease that naturally infects mule deer, white tailed deer and elk. The understanding of CWD neurodegeneration at a molecular level is very limited. In this study, microarray analysis was performed to determine changes in the gene expression profiles in six different tissues including brain, midbrain, thalamus, spleen, RPLN and tonsil of CWD-infected elk in comparison to non-infected healthy elk, using 24,000 bovine specific oligo probes. In total, 329 genes were found to be differentially expressed (> 2.0-fold) between CWD negative and positive brain tissues, with 132 genes upregulated and 197 genes downregulated. There were 249 DE genes in the spleen (168 up- and 81 downregulated), 30 DE genes in the retropharyngeal lymph node (RPLN) (18 up- and 12 downregulated), and 55 DE genes in the tonsil (21 up- and 34 downregulated). Using Gene Ontology (GO), the DE genes were assigned to functional groups associated with cellular process, biological regulation, metabolic process, and regulation of biological process. For all brain tissues, the highest ranking networks for DE genes identified by Ingenuity Pathway Analysis (IPA) were associated with neurological disease, cell morphology, cellular assembly and organization. Quantitative real-time PCR (qRT-PCR) validated the expression of DE genes primarily involved in different regulatory pathways, including neuronal signaling and synapse function, calcium signaling, apoptosis and cell death and immune cell trafficking and inflammatory response. This is the first study to evaluate altered gene expression in multiple organs including brain from orally infected elk and the results will improve our understanding of CWD neurodegeneration at the molecular level.     

Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature

Gene function annotation remains a key challenge in modern biology. This is especially true for high-throughput techniques such as gene expression experiments. Vital information about genes is available electronically from biomedical literature in the form of full texts and abstracts. In addition, various publicly available databases (such as GenBank, Gene Ontology and Entrez) provide access to gene-related information at different levels of biological organization, granularity and data format. This information is being used to assess and interpret the results from high-throughput experiments. To improve keyword extraction for annotational clustering and other types of analyses, we have developed a novel text mining approach, which is based on keywords identified at the level of gene annotation sentences (in particular sentences characterizing biological function) instead of entire abstracts. Further, to improve the expressiveness and usefulness of gene annotation terms, we investigated the combination of sentence-level keywords with terms from the Medical Subject Headings (MeSH) and Gene Ontology (GO) resources. We find that sentence-level keywords combined with MeSH terms outperforms the typical 'baseline' set-up (term frequencies at the level of abstracts) by a significant margin, whereas the addition of GO terms improves matters only marginally. We validated our approach on the basis of a manually annotated corpus of 200 abstracts generated on the basis of 2 cancer categories and 10 genes per category. We applied the method in the context of three sets of differentially expressed genes obtained from pediatric brain tumor samples. This analysis suggests novel interpretations of discovered gene expression patterns.     

A graph-based semantic similarity measure for the gene ontology

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.     

How to decide which are the most pertinent overly-represented features during gene set enrichment analysis

Background  

The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.     

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.     

Finding biological process modifications in cancer tissues by mining gene expression correlations

Background  

Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This technology yields a global view of gene expression that can be used in several ways. Functional insight into expression profiles is routinely obtained by using Gene Ontology terms associated to the cellular genes. In this paper, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). By using this "functional correlations comparison" we explore all possible pairs of genes identifying the affected biological processes by analyzing in a pair-wise manner gene expression patterns and linking correlated pairs with Gene Ontology terms.     

Zebrafish information network,the knowledgebase for Danio rerio research

The Zebrafish Information Network (zfin.org) is the central repository for Danio rerio genetic and genomic data. The Zebrafish Information Network has served the zebrafish research community since 1994, expertly curating, integrating, and displaying zebrafish data. Key data types available at the Zebrafish Information Network include, but are not limited to, genes, alleles, human disease models, gene expression, phenotype, and gene function. The Zebrafish Information Network makes zebrafish research data Findable, Accessible, Interoperable, and Reusable through nomenclature, curatorial and annotation activities, web interfaces, and data downloads. Recently, the Zebrafish Information Network and 6 other model organism knowledgebases have collaborated to form the Alliance of Genome Resources, aiming to develop sustainable genome information resources that enable the use of model organisms to understand the genetic and genomic basis of human biology and disease. Here, we provide an overview of the data available at the Zebrafish Information Network including recent updates to the gene page to provide access to single-cell RNA sequencing data, links to Alliance web pages, ribbon diagrams to summarize the biological systems and Gene Ontology terms that have annotations, and data integration with the Alliance of Genome Resources.     

Hepatic Oxidoreduction-Related Genes Are Upregulated by Administration of Hydrogen-Saturated Drinking Water

The effects of the administration of molecular hydrogen-saturated drinking water (hydrogen water) on hepatic gene expression were investigated in rats. Using DNA microarrays, 548 upregulated and 695 downregulated genes were detected in the liver after 4 weeks of administration of hydrogen water. Gene Ontology analysis revealed that genes for oxidoreduction-related proteins, including hydroxymethylglutaryl CoA reductase, were significantly enriched in the upregulated genes.     

Hepatic oxidoreduction-related genes are upregulated by administration of hydrogen-saturated drinking water

The effects of the administration of molecular hydrogen-saturated drinking water (hydrogen water) on hepatic gene expression were investigated in rats. Using DNA microarrays, 548 upregulated and 695 downregulated genes were detected in the liver after 4 weeks of administration of hydrogen water. Gene Ontology analysis revealed that genes for oxidoreduction-related proteins, including hydroxymethylglutaryl CoA reductase, were significantly enriched in the upregulated genes.     

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.     

基于基因本体论的生物信息个人数据库平台

论述了一个基于基因本体论(geneontology)的生物信息个人数据库平台BIO.该数据库平台根据自身研究的需要,用基因本体论中的相关的规范术语来对基因序列信息进行注释,从而可以让用户从基因本体论的角度对生物信息序列进行查询.由于因特网上生物数据库中大量的关于基因序列信息的术语不统一、不规范,存在大量的信息冗余,用此方式可最大限度地精确所要查找的结果.文中详细论述了该数据库平台的研究背景、查询功能以及维护.     

The Sequence Ontology: a tool for the unification of genome annotations

The Sequence Ontology (SO) is a structured controlled vocabulary for the parts of a genomic annotation. SO provides a common set of terms and definitions that will facilitate the exchange, analysis and management of genomic data. Because SO treats part-whole relationships rigorously, data described with it can become substrates for automated reasoning, and instances of sequence features described by the SO can be subjected to a group of logical operations termed extensional mereology operators.