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.     

Learnability-based further prediction of gene functions in Gene Ontology

Currently the functional annotations of many genes are not specific enough, limiting their further application in biology and medicine. It is necessary to push the gene functional annotations deeper in Gene Ontology (GO), or to predict further annotated genes with more specific GO terms. A framework of learnability-based further prediction of gene functions in GO is proposed in this paper. Local classifiers are constructed in local classification spaces rooted at qualified parent nodes in GO, and their classification performances are evaluated with the averaged Tanimoto index (ATI). Classification spaces with higher ATIs are selected out, and genes annotated only to the parent classes are predicted to child classes. Through learnability-based further predicting, the functional annotations of annotated genes are made more specific. Experiments on the fibroblast serum response dataset reported further functional predictions for several human genes and also gave interesting clues to the varied learnability between classes of different GO ontologies, different levels, and different numbers of child classes.     

Finding New Order in Biological Functions from the Network Structure of Gene Annotations

The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes.     

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.].     

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.     

A sensitive method for computing GO-based functional similarities among genes with 'shallow annotation'

Methods for computing similarities among genes have attracted increasing attention for their applications in gene clustering, gene expression data analysis, protein interaction prediction and evaluation. To address the need for automatically computing functional similarities of genes, an important class of methods that computes functional similarities by comparing Gene Ontology (GO) annotations of genes has been developed. However, all of the currently available methods have some drawbacks; for example, they either ignore the specificity of the GO terms or do not consider the information contained within the GO structure. As a result, the existing methods perform weakly when the genes are annotated with 'shallow annotations'. Here, we propose a new method to compute functional similarities among genes based on their GO annotations and compare it with the widely-used G-SESAME method. The results show that the new method reliably distinguishes functional similarities among genes and demonstrate that the method is especially sensitive to genes with 'shallow annotations'. Moreover, our method has high correlations with sequence and EC similarities.     

BioCompass: a novel functional inference tool that utilizes MeSH hierarchy to analyze groups of genes

Microarray technology has become employed widely for biological researchers to identify genes associated with conditions such as diseases and drugs. To date, many methods have been developed to analyze data covering a large number of genes, but they focus only on statistical significance and cannot decipher the data with biological concepts. Gene Ontology (GO) is utilized to understand the data with biological interpretation; however, it is restricted to specific ontology such as biological process, molecular function, and cellular component. Here, we attempted to apply MeSH (Medical Subject Headings) to interpret groups of genes from biological viewpoint. To assign MeSH terms to genes, in this study, contexts associated with genes are retrieved from full set of MEDLINE data using machine learning, and then extracted MeSH terms from retrieved articles. Utilizing the developed method, we implemented a software called BioCompass. It generates high-scoring lists and hierarchical lists for diseases MeSH terms associated with groups of genes to utilize MeSH and GO tree, and illustrated a wiring diagram by linking genes with extracted association from articles. Researchers can easily retrieve genes and keywords of interest, such as diseases and drugs, associated with groups of genes. Using retrieved MeSH terms and OMIM in conjunction with, we could obtain more disease information associated with target gene. BioCompass helps researchers to interpret groups of genes such as microarray data from a biological viewpoint.     

Methodology for the inference of gene function from phenotype data

Background

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures.

Results

We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function.We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes.

Conclusions

We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0405-z) contains supplementary material, which is available to authorized users.     

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.     

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.     

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.     

The representation of heart development in the gene ontology

An understanding of heart development is critical in any systems biology approach to cardiovascular disease. The interpretation of data generated from high-throughput technologies (such as microarray and proteomics) is also essential to this approach. However, characterizing the role of genes in the processes underlying heart development and cardiovascular disease involves the non-trivial task of data analysis and integration of previous knowledge. The Gene Ontology (GO) Consortium provides structured controlled biological vocabularies that are used to summarize previous functional knowledge for gene products across all species. One aspect of GO describes biological processes, such as development and signaling.In order to support high-throughput cardiovascular research, we have initiated an effort to fully describe heart development in GO; expanding the number of GO terms describing heart development from 12 to over 280. This new ontology describes heart morphogenesis, the differentiation of specific cardiac cell types, and the involvement of signaling pathways in heart development. This work also aligns GO with the current views of the heart development research community and its representation in the literature. This extension of GO allows gene product annotators to comprehensively capture the genetic program leading to the developmental progression of the heart. This will enable users to integrate heart development data across species, resulting in the comprehensive retrieval of information about this subject.The revised GO structure, combined with gene product annotations, should improve the interpretation of data from high-throughput methods in a variety of cardiovascular research areas, including heart development, congenital cardiac disease, and cardiac stem cell research. Additionally, we invite the heart development community to contribute to the expansion of this important dataset for the benefit of future research in this area.     

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

     

Enrichment of Triticum aestivum gene annotations using ortholog cliques and gene ontologies in other plants

Background

While the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided.

Results

DNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation.

Conclusions

Our novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1496-2) contains supplementary material, which is available to authorized users.     

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.     

FungiFun: a web-based application for functional categorization of fungal genes and proteins

FungiFun assigns functional annotations to fungal genes or proteins and performs gene set enrichment analysis. Based on three different classification methods (FunCat, GO and KEGG), FungiFun categorizes genes and proteins for several fungal species on different levels of annotation detail. It is web-based and accessible to users without any programming skills. FungiFun is the first tool offering gene set enrichment analysis including the FunCat categorization. Two biological datasets for Aspergillus fumigatus and Candida albicans were analyzed using FungiFun, providing an overview of the usage and functions of the tool. FungiFun is freely accessible at https://www.omnifung.hki-jena.de/FungiFun/.     

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.