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Cheng L, Jiang Y, Ju H, Sun J, Peng J, Zhou M, Hu Y. InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics 2018; 19:919. [PMID: 29363423 PMCID: PMC5780854 DOI: 10.1186/s12864-017-4338-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown. Results We proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations. Conclusions The experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set. Electronic supplementary material The online version of this article (10.1186/s12864-017-4338-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Yue Jiang
- Hospital for Sick Children, Toronto, M5G 1X8, Canada
| | - Hong Ju
- Department of Information Engineering, Heilongjiang Biological Science and Technology Career Academy, Harbin, 150081, People's Republic of China
| | - Jie Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xian, 710072, People's Republic of China
| | - Meng Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
| | - Yang Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150088, People's Republic of China.
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Peng J, Wang H, Lu J, Hui W, Wang Y, Shang X. Identifying term relations cross different gene ontology categories. BMC Bioinformatics 2017; 18:573. [PMID: 29297309 PMCID: PMC5751813 DOI: 10.1186/s12859-017-1959-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information. Results We propose an iterative ranking-based method called CroGO2 to measure the cross-categories GO term similarities by incorporating level information of GO terms with both direct and indirect interactions in the gene co-function network. Conclusions The evaluation test shows that CroGO2 performs better than the existing methods. A genome-specific term association network for yeast is also generated by connecting terms with the high confidence score. The linkages in the term association network could be supported by the literature. Given a gene set, the related terms identified by using the association network have overlap with the related terms identified by GO enrichment analysis.
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Affiliation(s)
- Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Honggang Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Junya Lu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Weiwei Hui
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
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A framework for exploring associations between biomedical terms in PubMed. Oncotarget 2017; 8:103100-103107. [PMID: 29262548 PMCID: PMC5732714 DOI: 10.18632/oncotarget.21532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 09/08/2017] [Indexed: 11/25/2022] Open
Abstract
Co-occurrence relationships in PubMed between terms accelerate the recognition of term associations. The lack of manually curated relationships in vocabularies and the rapid increase of biomedical literatures highlight the importance of co-occurrence relationships. Here we proposed a framework to explore term associations based on a standard procedure that comprises multiple tools of text mining and relationship degree calculation methods. The text of PubMed were segmented into sentences by Apache OpenNLP first, and then terms of sentences were recognized by MGREP. After that two terms occurring in a common sentence were identified as a co-occurrence relationship. The relationship degree is then calculated using Normalized MEDLINE Distance (NMD) or relationship-scaled score (RSS) method. The framework was utilized in exploring associations between terms of Gene Ontology (GO) and Disease Ontology (DO) based on co-occurrence relationship. Results show that pairs of terms with more co-occurrence relationships indicate shared more semantic relationships of ontology and genes. The identified association terms based on co-occurrence relationships were applied in constructing a disease association network (DAN). The small giant component confirms with the observation that diseases in the same class have more linkage than diseases in different classes.
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Peng J, Li Q, Shang X. Investigations on factors influencing HPO-based semantic similarity calculation. J Biomed Semantics 2017; 8:34. [PMID: 29297376 PMCID: PMC5763495 DOI: 10.1186/s13326-017-0144-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with complex phenotypes and high genetic heterogeneity. Recently, calculating Human Phenotype Ontology (HPO)-based phenotype semantic similarity has contributed a lot for completing disease diagnosis. However, factors which affect the accuracy of HPO-based semantic similarity have not been evaluated systematically. Results In this study, we proposed a new framework called HPOFactor to evaluate these factors. Our model includes four components: (1) the size of annotation set, (2) the evidence code of annotations, (3) the quality of annotations and (4) the coverage of annotations respectively. Conclusions HPOFactor analyzes the four factors systematically based on two kinds of experiments: causative gene prediction and disease prediction. Furthermore, semantic similarity measurement could be designed based on the characteristic of these factors.
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Affiliation(s)
- Jiajie Peng
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Qianqian Li
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Xuequn Shang
- Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.
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Falda M, Lavezzo E, Fontana P, Bianco L, Berselli M, Formentin E, Toppo S. Eliciting the Functional Taxonomy from protein annotations and taxa. Sci Rep 2016; 6:31971. [PMID: 27534507 PMCID: PMC4989186 DOI: 10.1038/srep31971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 08/01/2016] [Indexed: 11/30/2022] Open
Abstract
The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules.
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Affiliation(s)
- Marco Falda
- Department of Molecular Medicine, University of Padova, Padova, 35131, Italy
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Padova, 35131, Italy
| | - Paolo Fontana
- Istituto Agrario San Michele all'Adige Research and Innovation Centre, Foundation Edmund Mach, Trento, 38010, Italy
| | - Luca Bianco
- Istituto Agrario San Michele all'Adige Research and Innovation Centre, Foundation Edmund Mach, Trento, 38010, Italy
| | - Michele Berselli
- Department of Molecular Medicine, University of Padova, Padova, 35131, Italy
| | - Elide Formentin
- Department of Biology, University of Padova, Padova, 35131, Italy
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padova, 35131, Italy
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Abu-Jamous B, Fa R, Roberts DJ, Nandi AK. Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis. BMC Bioinformatics 2014; 15:322. [PMID: 25267386 PMCID: PMC4262117 DOI: 10.1186/1471-2105-15-322] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 09/22/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result. RESULTS In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other. CONCLUSIONS The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.
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Affiliation(s)
- Basel Abu-Jamous
- />Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex, UB8 3PH UK
| | - Rui Fa
- />Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex, UB8 3PH UK
| | - David J Roberts
- />National Health Service Blood and Transplant, Oxford, UK
- />Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Asoke K Nandi
- />Department of Electronic and Computer Engineering, Brunel University, Uxbridge, Middlesex, UB8 3PH UK
- />Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland
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Pomyen Y, Segura M, Ebbels TMD, Keun HC. Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets. Bioinformatics 2014; 31:102-8. [DOI: 10.1093/bioinformatics/btu589] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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Cheng L, Li J, Ju P, Peng J, Wang Y. SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association. PLoS One 2014; 9:e99415. [PMID: 24932637 PMCID: PMC4059643 DOI: 10.1371/journal.pone.0099415] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 05/14/2014] [Indexed: 01/20/2023] Open
Abstract
Background Measuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue. Methods SemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity. Results The high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.
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Affiliation(s)
- Liang Cheng
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Jie Li
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Peng Ju
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Jiajie Peng
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yadong Wang
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China
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Abstract
Background In Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents. Results We introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human). Conclusions InteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.
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