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.     

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.     

Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis

MOTIVATION: High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. RESULTS: We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.     

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

Background

Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent–child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.

Results

We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington’s disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.

Conclusion

Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.     

Clustering of PubMed abstracts using nearer terms of the domain

Literature search is a process in which external developers provide alternative representations for efficient data mining of biomedical literature such as ranking search results, displaying summarized knowledge of semantics and clustering results into topics. In clustering search results, prominent vocabularies, such as GO (Gene Ontology), MeSH(Medical Subject Headings) and frequent terms extracted from retrieved PubMed abstracts have been used as topics for grouping. In this study, we have proposed FNeTD (Frequent Nearer Terms of the Domain) method for PubMed abstracts clustering. This is achieved through a two-step process viz; i) identifying frequent words or phrases in the abstracts through the frequent multi-word extraction algorithm and ii) identifying nearer terms of the domain from the extracted frequent phrases using the nearest neighbors search. The efficiency of the clustering of PubMed abstracts using nearer terms of the domain was measured using F-score. The present study suggests that nearer terms of the domain can be used for clustering the search results.     

EBIMed--text crunching to gather facts for proteins from Medline

To allow efficient and systematic retrieval of statements from Medline we have developed EBIMed, a service that combines document retrieval with co-occurrence-based analysis of Medline abstracts. Upon keyword query, EBIMed retrieves the abstracts from EMBL-EBI's installation of Medline and filters for sentences that contain biomedical terminology maintained in public bioinformatics resources. The extracted sentences and terminology are used to generate an overview table on proteins, Gene Ontology (GO) annotations, drugs and species used in the same biological context. All terms in retrieved abstracts and extracted sentences are linked to their entries in biomedical databases. We assessed the quality of the identification of terms and relations in the retrieved sentences. More than 90% of the protein names found indeed represented a protein. According to the analysis of four protein-protein pairs from the Wnt pathway we estimated that 37% of the statements containing such a pair mentioned a meaningful interaction and clarified the interaction of Dkk with LRP. We conclude that EBIMed improves access to information where proteins and drugs are involved in the same biological process, e.g. statements with GO annotations of proteins, protein-protein interactions and effects of drugs on proteins. AVAILABILITY: Available at http://www.ebi.ac.uk/Rebholz-srv/ebimed     

REVIGO summarizes and visualizes long lists of gene ontology terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.     

goSTAG: gene ontology subtrees to tag and annotate genes within a set

Background

Over-representation analysis (ORA) detects enrichment of genes within biological categories. Gene Ontology (GO) domains are commonly used for gene/gene-product annotation. When ORA is employed, often times there are hundreds of statistically significant GO terms per gene set. Comparing enriched categories between a large number of analyses and identifying the term within the GO hierarchy with the most connections is challenging. Furthermore, ascertaining biological themes representative of the samples can be highly subjective from the interpretation of the enriched categories.

Results

We developed goSTAG for utilizing GO Subtrees to Tag and Annotate Genes that are part of a set. Given gene lists from microarray, RNA sequencing (RNA-Seq) or other genomic high-throughput technologies, goSTAG performs GO enrichment analysis and clusters the GO terms based on the p-values from the significance tests. GO subtrees are constructed for each cluster, and the term that has the most paths to the root within the subtree is used to tag and annotate the cluster as the biological theme. We tested goSTAG on a microarray gene expression data set of samples acquired from the bone marrow of rats exposed to cancer therapeutic drugs to determine whether the combination or the order of administration influenced bone marrow toxicity at the level of gene expression. Several clusters were labeled with GO biological processes (BPs) from the subtrees that are indicative of some of the prominent pathways modulated in bone marrow from animals treated with an oxaliplatin/topotecan combination. In particular, negative regulation of MAP kinase activity was the biological theme exclusively in the cluster associated with enrichment at 6 h after treatment with oxaliplatin followed by control. However, nucleoside triphosphate catabolic process was the GO BP labeled exclusively at 6 h after treatment with topotecan followed by control.

Conclusions

goSTAG converts gene lists from genomic analyses into biological themes by enriching biological categories and constructing GO subtrees from over-represented terms in the clusters. The terms with the most paths to the root in the subtree are used to represent the biological themes. goSTAG is developed in R as a Bioconductor package and is available at https://bioconductor.org/packages/goSTAG

     

Chapter 9: Analyses Using Disease Ontologies

Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of “significant genes.” One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene-set, and is widely used to makes sense of the results of high-throughput experiments. The canonical example of enrichment analysis is when the output dataset is a list of genes differentially expressed in some condition. To determine the biological relevance of a lengthy gene list, the usual solution is to perform enrichment analysis with the GO. We can aggregate the annotating GO concepts for each gene in this list, and arrive at a profile of the biological processes or mechanisms affected by the condition under study. While GO has been the principal target for enrichment analysis, the methods of enrichment analysis are generalizable. We can conduct the same sort of profiling along other ontologies of interest. Just as scientists can ask “Which biological process is over-represented in my set of interesting genes or proteins?” we can also ask “Which disease (or class of diseases) is over-represented in my set of interesting genes or proteins?“. For example, by annotating known protein mutations with disease terms from the ontologies in BioPortal, Mort et al. recently identified a class of diseases—blood coagulation disorders—that were associated with a 14-fold depletion in substitutions at O-linked glycosylation sites. With the availability of tools for automatic annotation of datasets with terms from disease ontologies, there is no reason to restrict enrichment analyses to the GO. In this chapter, we will discuss methods to perform enrichment analysis using any ontology available in the biomedical domain. We will review the general methodology of enrichment analysis, the associated challenges, and discuss the novel translational analyses enabled by the existence of public, national computational infrastructure and by the use of disease ontologies in such analyses.

What to Learn in This Chapter

• Review the commonly used approach of Gene Ontology based enrichment analysis
• Understand the pitfalls associated with current approaches
• Understand the national infrastructure available for using alternative ontologies for enrichment analysis
• Learn about a generalized enrichment analysis workflow and its application using disease ontologies

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Text-based over-representation analysis of microarray gene lists with annotation bias

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone.     

CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology

SUMMARY: Analysis of microarray data most often produces lists of genes with similar expression patterns, which are then subdivided into functional categories for biological interpretation. Such functional categorization is most commonly accomplished using Gene Ontology (GO) categories. Although there are several programs that identify and analyze functional categories for human, mouse and yeast genes, none of them accept Arabidopsis thaliana data. In order to address this need for A.thaliana community, we have developed a program that retrieves GO annotations for A.thaliana genes and performs functional category analysis for lists of genes selected by the user. AVAILABILITY: http://www.personal.psu.edu/nhs109/Clench     

Microarray analysis provides insight into the early steps of pathophysiology of mouse endometriosis model induced by autotransplantation of endometrium

AimsTo characterize the biochemical alterations that occur in the peritoneal tissue of the mouse endometriosis model during early development of the lesion using microarray analysis.Main methodsThe endometriosis model was induced by autotransplantation of endometrium in 8-week-old female ICR mice. Peritoneum only (excluding the transplant) was obtained 24, 48, and 96 h after the autotransplantation and subjected to microarray analysis. To interpret the large amounts of data generated and to enable a functional analysis, genes were classified using Gene Ontology (GO) and Medical Subject Heading (MeSH) terms, and the results were compared with previous reports on endometriosis.Key findingsOf the upregulated genes, those involved in the inflammatory response, cell adhesion, extracellular matrix, wound healing, hormones, and leukocytes were significantly enriched 24 and 48 h after autotransplantation. Those of cytokines, antibody-producing cells, dendritic cells, inflammation, and infertility were enriched after 96 h. Analysis using GO and MeSH provided different information. Particularly, MeSH showed a link between an anatomical and diseased phenotype with common genes found to be upregulated.SignificanceThe factors occurring during early development of endometriosis induced by endometrium autotransplantation are increase in adhesion molecules and inflammatory responses rather than angiogenesis. Data presented herein may reveal a novel therapeutic gene targets and will contribute to knowledge for the treatment of this currently incurable disease.     

MeSHer: identifying biological concepts in microarray assays based on PubMed references and MeSH terms

SUMMARY: MeSHer uses a simple statistical approach to identify biological concepts in the form of Medical Subject Headings (MeSH terms) obtained from the PubMed database that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying DNA microarray platform. As a demonstration, we apply this approach to gene lists acquired from a published study of the effects of angiotensin II (Ang II) treatment on cardiac gene expression and demonstrate that this approach can aid in the interpretation of the resulting 'significant' gene set. AVAILABILITY: The software is available at http://www.tm4.org. SUPPLEMENTARY INFORMATION: Results from the analysis of significant genes from the published Ang II study.