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Journal articles on the topic 'Gene ontology enrichment'

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Published: 10 December 2022
Last updated: 10 March 2023

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1

Shameer, Khader, Mahantesha Bn Naika, Oommen K. Mathew, and Ramanathan Sowdhamini. "POEAS: Automated Plant Phenomic Analysis Using Plant Ontology." Bioinformatics and Biology Insights 8 (January 2014): BBI.S19057. http://dx.doi.org/10.4137/bbi.s19057.

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Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas .
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2

Dalmer, Timothy R. A., and Robin D. Clugston. "Gene ontology enrichment analysis of congenital diaphragmatic hernia-associated genes." Pediatric Research 85, no. 1 (September 25, 2018): 13–19. http://dx.doi.org/10.1038/s41390-018-0192-8.

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3

Jupiter, Daniel, Jessica Şahutoğlu, and Vincent VanBuren. "TreeHugger: A New Test for Enrichment of Gene Ontology Terms." INFORMS Journal on Computing 22, no. 2 (May 2010): 210–21. http://dx.doi.org/10.1287/ijoc.1090.0356.

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4

Raza, Khalid. "Reconstruction, Topological and Gene Ontology Enrichment Analysis of Cancerous Gene Regulatory Network Modules." Current Bioinformatics 11, no. 2 (April 1, 2016): 243–58. http://dx.doi.org/10.2174/1574893611666160115212806.

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5

Zheng, Qi, and Xiu-Jie Wang. "GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis." Nucleic Acids Research 36, suppl_2 (May 16, 2008): W358—W363. http://dx.doi.org/10.1093/nar/gkn276.

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6

Dalmer, Timothy R. A., and Robin D. Clugston. "Correction: Gene ontology enrichment analysis of congenital diaphragmatic hernia-associated genes." Pediatric Research 86, no. 5 (August 14, 2019): 676. http://dx.doi.org/10.1038/s41390-019-0536-z.

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7

Mi, Gu, Yanming Di, Sarah Emerson, Jason S. Cumbie, and Jeff H. Chang. "Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression." PLoS ONE 7, no. 10 (October 2, 2012): e46128. http://dx.doi.org/10.1371/journal.pone.0046128.

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8

Hinderer, Eugene W., Robert M. Flight, Rashmi Dubey, James N. MacLeod, and Hunter N. B. Moseley. "Advances in gene ontology utilization improve statistical power of annotation enrichment." PLOS ONE 14, no. 8 (August 15, 2019): e0220728. http://dx.doi.org/10.1371/journal.pone.0220728.

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9

Shah, N. H., and N. V. Fedoroff. "CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology." Bioinformatics 20, no. 7 (February 5, 2004): 1196–97. http://dx.doi.org/10.1093/bioinformatics/bth056.

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10

Chittenden, Thomas W., Eleanor A. Howe, Jennifer M. Taylor, Jessica C. Mar, Martin J. Aryee, Harold Gómez, Razvan Sultana, et al. "nEASE: a method for gene ontology subclassification of high-throughput gene expression data." Bioinformatics 28, no. 5 (January 13, 2012): 726–28. http://dx.doi.org/10.1093/bioinformatics/bts011.

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Abstract Summary: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. Availability: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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11

Hummel, Manuela, Klaus H. Metzeler, Christian Buske, Stefan K. Bohlander, and Ulrich Mansmann. "Association between a Prognostic Gene Signature and Functional Gene Sets." Bioinformatics and Biology Insights 2 (January 2008): BBI.S1018. http://dx.doi.org/10.4137/bbi.s1018.

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Background The development of expression-based gene signatures for predicting prognosis or class membership is a popular and challenging task. Besides their stringent validation, signatures need a functional interpretation and must be placed in a biological context. Popular tools such as Gene Set Enrichment have drawbacks because they are restricted to annotated genes and are unable to capture the information hidden in the signature's non-annotated genes. Methodology We propose concepts to relate a signature with functional gene sets like pathways or Gene Ontology categories. The connection between single signature genes and a specific pathway is explored by hierarchical variable selection and gene association networks. The risk score derived from an individual patient's signature is related to expression patterns of pathways and Gene Ontology categories. Global tests are useful for these tasks, and they adjust for other factors. GlobalAncova is used to explore the effect on gene expression in specific functional groups from the interaction of the score and selected mutations in the patient's genome. Results We apply the proposed methods to an expression data set and a corresponding gene signature for predicting survival in Acute Myeloid Leukemia (AML). The example demonstrates strong relations between the signature and cancer-related pathways. The signature-based risk score was found to be associated with development-related biological processes. Conclusions Many authors interpret the functional aspects of a gene signature by linking signature genes to pathways or relevant functional gene groups. The method of gene set enrichment is preferred to annotating signature genes to specific Gene Ontology categories. The strategies proposed in this paper go beyond the restriction of annotation and deepen the insights into the biological mechanisms reflected in the information given by a signature.
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12

Gong, Xiu-Jun, Hua Yu, Chun-Bai Yang, and Yuan-Fang Li. "Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights." Journal of Integrative Bioinformatics 9, no. 2 (June 1, 2012): 28–39. http://dx.doi.org/10.1515/jib-2012-194.

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Summary The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS) and housekeeping (HK) genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA) and Hypergeometric Analysis (HA) against three biological ontologies: Gene Ontology (GO), KEGG pathways and Disease Ontology (DO) respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease) are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM)” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC)” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO) for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.
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13

Sobczyk, M. K., T. R. Gaunt, and L. Paternoster. "MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes." Bioinformatics 37, no. 1 (January 1, 2021): 1–8. http://dx.doi.org/10.1093/bioinformatics/btaa1096.

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Abstract Motivation Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization. Results We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis. Availability and implementation MendelVar is freely available at https://mendelvar.mrcieu.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.
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14

Quesada-Martínez, Manuel, Eleni Mikroyannidi, Jesualdo Tomás Fernández-Breis, and Robert Stevens. "Approaching the axiomatic enrichment of the Gene Ontology from a lexical perspective." Artificial Intelligence in Medicine 65, no. 1 (September 2015): 35–48. http://dx.doi.org/10.1016/j.artmed.2014.09.003.

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15

Xu, Tao, JianLei Gu, Yan Zhou, and LinFang Du. "Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology." BMC Bioinformatics 10, no. 1 (2009): 240. http://dx.doi.org/10.1186/1471-2105-10-240.

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16

Zhang, Jian, ZhiHao Xing, Mingming Ma, Ning Wang, Yu-Dong Cai, Lei Chen, and Xun Xu. "Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/450386.

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Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.
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17

Fernández-Breis, J. T., R. Stevens, E. Mikroyannidi, and M. Quesada-Martínez. "Prioritising Lexical Patterns to Increase Axiomatisation in Biomedical Ontologies." Methods of Information in Medicine 54, no. 01 (2015): 56–64. http://dx.doi.org/10.3414/me13-02-0026.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point. Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns.Methods: We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT.Results: The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies.Conclusions: The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.
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18

Zúñiga-León, Eduardo, Ulises Carrasco-Navarro, and Francisco Fierro. "NeVOmics: An Enrichment Tool for Gene Ontology and Functional Network Analysis and Visualization of Data from OMICs Technologies." Genes 9, no. 12 (November 23, 2018): 569. http://dx.doi.org/10.3390/genes9120569.

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The increasing number of OMICs studies demands bioinformatic tools that aid in the analysis of large sets of genes or proteins to understand their roles in the cell and establish functional networks and pathways. In the last decade, over-representation or enrichment tools have played a successful role in the functional analysis of large gene/protein lists, which is evidenced by thousands of publications citing these tools. However, in most cases the results of these analyses are long lists of biological terms associated to proteins that are difficult to digest and interpret. Here we present NeVOmics, Network-based Visualization for Omics, a functional enrichment analysis tool that identifies statistically over-represented biological terms within a given gene/protein set. This tool provides a hypergeometric distribution test to calculate significantly enriched biological terms, and facilitates analysis on cluster distribution and relationship of proteins to processes and pathways. NeVOmics is adapted to use updated information from the two main annotation databases: Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). NeVOmics compares favorably to other Gene Ontology and enrichment tools regarding coverage in the identification of biological terms. NeVOmics can also build different network-based graphical representations from the enrichment results, which makes it an integrative tool that greatly facilitates interpretation of results obtained by OMICs approaches. NeVOmics is freely accessible at https://github.com/bioinfproject/bioinfo/.
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19

Filippi, Alexandru, and Maria-Magdalena Mocanu. "Mining TCGA Database for Genes with Prognostic Value in Breast Cancer." International Journal of Molecular Sciences 24, no. 2 (January 13, 2023): 1622. http://dx.doi.org/10.3390/ijms24021622.

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The aim of the study was to use transcriptomics data to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes. We used the publicly available The Cancer Genome Atlas (TCGA) database to obtain RNA sequence data from patients with less than five years survival (Poor Prognosis, n = 101), patients with greater than five years survival (Good Prognosis, n = 200), as well as unpaired normal tissue data (normal, n = 105). The data analyses performed included differential expression between groups and selection of subsets of genes, gene ontology, cell enrichment analysis, and survival analyses. Gene ontology results showed significantly reduced enrichment in gene sets related to tumor immune microenvironment in Poor Prognosis and cell enrichment analysis confirmed significantly reduced numbers of macrophages M1, CD8 T cells, plasma cells and dendritic cells in samples in the Poor Prognosis samples compared with Good Prognosis. A subset of 742 genes derived from differential expression analysis as well as genes coding for immune checkpoint molecules was evaluated for their effect on overall survival. In conclusion, this study may contribute to the better understanding of breast cancer transcriptomics and provide possible targets for further research and eventual therapeutic interventions.
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20

Komljenovic, Andrea, Julien Roux, Marc Robinson-Rechavi, and Frederic B. Bastian. "BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests." F1000Research 5 (November 23, 2016): 2748. http://dx.doi.org/10.12688/f1000research.9973.1.

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BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and reprocessed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures (“TopAnat”). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: http://www.bioconductor.org/packages/BgeeDB/
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Komljenovic, Andrea, Julien Roux, Julien Wollbrett, Marc Robinson-Rechavi, and Frederic B. Bastian. "BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests." F1000Research 5 (August 7, 2018): 2748. http://dx.doi.org/10.12688/f1000research.9973.2.

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BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and re-processed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures (“TopAnat”). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: https://www.bioconductor.org/packages/BgeeDB/
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22

Nixon, Scott E., Dianelys González-Peña, Marcus A. Lawson, Robert H. McCusker, Alvaro G. Hernandez, Jason C. O'Connor, Robert Dantzer, Keith W. Kelley, and Sandra L. Rodriguez-Zas. "Analytical workflow profiling gene expression in murine macrophages." Journal of Bioinformatics and Computational Biology 13, no. 02 (April 2015): 1550010. http://dx.doi.org/10.1142/s0219720015500109.

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Comprehensive and simultaneous analysis of all genes in a biological sample is a capability of RNA-Seq technology. Analysis of the entire transcriptome benefits from summarization of genes at the functional level. As a cellular response of interest not previously explored with RNA-Seq, peritoneal macrophages from mice under two conditions (control and immunologically challenged) were analyzed for gene expression differences. Quantification of individual transcripts modeled RNA-Seq read distribution and uncertainty (using a Beta Negative Binomial distribution), then tested for differential transcript expression (False Discovery Rate-adjusted p-value < 0.05). Enrichment of functional categories utilized the list of differentially expressed genes. A total of 2079 differentially expressed transcripts representing 1884 genes were detected. Enrichment of 92 categories from Gene Ontology Biological Processes and Molecular Functions, and KEGG pathways were grouped into 6 clusters. Clusters included defense and inflammatory response (Enrichment Score = 11.24) and ribosomal activity (Enrichment Score = 17.89). Our work provides a context to the fine detail of individual gene expression differences in murine peritoneal macrophages during immunological challenge with high throughput RNA-Seq.
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23

Zhou, Xin, and Zhen Su. "EasyGO: Gene Ontology-based annotation and functional enrichment analysis tool for agronomical species." BMC Genomics 8, no. 1 (2007): 246. http://dx.doi.org/10.1186/1471-2164-8-246.

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24

Fruzangohar, Mario, Esmaeil Ebrahimie, and David L. Adelson. "A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data." PLOS ONE 12, no. 2 (February 15, 2017): e0170486. http://dx.doi.org/10.1371/journal.pone.0170486.

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25

Li, Zhen, Bi-Qing Li, Min Jiang, Lei Chen, Jian Zhang, Lin Liu, and Tao Huang. "Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/304029.

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One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.
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Yang, Xiu-Yan, Yu-jie Gu, Tian An, Jia-Xian Liu, Yan-Yun Pan, Fang-Fang Mo, Jia-Nan Miao, et al. "Proteomics Analysis of Testis of Rats Fed a High-Fat Diet." Cellular Physiology and Biochemistry 47, no. 1 (2018): 378–89. http://dx.doi.org/10.1159/000489918.

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Background/Aims: The adverse effects of obesity on male fertility have been widely reported. In recent years, the relationship between the differential expression of proteins and long non-coding RNAs with male reproductive disease has been reported. However, the exact mechanism in underlying obesity-induced decreased male fertility remains unclear. Methods: We used isobaric tags for relative and absolute quantification to identify differential protein expression patterns in the testis of rats fed a high-fat diet and normal diet. A microarray-based gene expression analysis protocol was used to compare the differences in long non-coding RNAs in high-fat diet-fed and normal diet-fed rats. Five obviously upregulated or downregulated proteins were examined using western blot to verify the accuracy of their expression. Then, we carried out functional enrichment analysis of the differentially expressed proteins using gene ontology and pathway analysis. Finally, the metabolic Gene Ontology terms and pathways involved in the differential metabolites were analyzed using the MetaboAnalyst 2.0 software to explore the co-expression relationship between long non-coding RNAs and proteins. Results: We found 107 proteins and 263 long non-coding RNAs differentially expressed between rats fed a high-fat diet and normal diet. The Gene Ontology term enrichment analysis showed that the protein function most highly enriched was related to negative regulation of reproductive processes. We also found five Gene Ontology terms and two metabolic pathways upregulated or downregulated for both proteins and long non-coding RNAs. Conclusion: The study revealed different expression levels for both proteins and long non-coding RNAs and showed that the function and metabolic pathways of differently expressed proteins were related to reproductive processes. The Gene Ontology terms and metabolic pathways upregulated or downregulated in both proteins and long non-coding RNAs may provide new candidates to explore the mechanisms of obesity-induced male infertility for both protein and epigenetic pathways.
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Wang, Shur-Jen, Kent C. Brodie, Jeffrey L. De Pons, Wendy M. Demos, Adam C. Gibson, G. Thomas Hayman, Morgan L. Hill, et al. "Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal." Genes 13, no. 12 (December 7, 2022): 2304. http://dx.doi.org/10.3390/genes13122304.

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The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD’s COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.
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Ge, Steven Xijin, Dongmin Jung, and Runan Yao. "ShinyGO: a graphical gene-set enrichment tool for animals and plants." Bioinformatics 36, no. 8 (December 27, 2019): 2628–29. http://dx.doi.org/10.1093/bioinformatics/btz931.

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Abstract Motivation Gene lists are routinely produced from various omic studies. Enrichment analysis can link these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases. Results To complement existing tools, we developed ShinyGO based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archeal and 1678 bacterial species. ShinyGO’s novel features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein–protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene-sets. Availability and implementation http://ge-lab.org/go/. Supplementary information Supplementary data are available at Bioinformatics online.
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Köhler, Sebastian, Sandra C. Doelken, Barbara J. Ruef, Sebastian Bauer, Nicole Washington, Monte Westerfield, George Gkoutos, et al. "Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research." F1000Research 2 (January 21, 2014): 30. http://dx.doi.org/10.12688/f1000research.2-30.v2.

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Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species.We have generated a cross-species phenotype ontology for human, mouse and zebrafish that contains classes from the Human Phenotype Ontology, Mammalian Phenotype Ontology, and generated classes for zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases.This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.
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Wang, Bin, Yang Liu, Jianhui Sun, Nailin Zhang, Xiaojia Zheng, and Qiquan Liu. "Exploring the Potential Mechanism of Xiaokui Jiedu Decoction for Ulcerative Colitis Based on Network Pharmacology and Molecular Docking." Journal of Healthcare Engineering 2021 (October 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/1536337.

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Introduction. Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC). Methods. The Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. The Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. Results. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. The hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) showed good binding activities to five of the six hub gene targets. Discussion. The active ingredients of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. This may be a potential mechanism of XJD in the treatment of UC.
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Pan, Xue, Ying Chen, and Song Gao. "Four genes relevant to pathological grade and prognosis in ovarian cancer." Cancer Biomarkers 29, no. 2 (October 9, 2020): 169–78. http://dx.doi.org/10.3233/cbm-191162.

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BACKGROUND: Ovarian cancer is the common tumor in female, the prognostic of which is influenced by a series of factors. In this study, 4 genes relevant to pathological grade in ovarian cancer were screened out by the construction of weighted gene co-expression network analysis. METHODS: GSE9891 with 298 ovarian cancer cases had been used to construct co-expression networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses was used to analyze the possible mechanism of genes involved in the malignant process of ovarian cancer. Hub genes were validated in other independent datasets, such as GSE63885, GSE26193 and GSE30161. Survival analysis based on the hub genes was performed by website of Kaplan Meier-plotter. RESULTS: The result based on weighted gene co-expression network analysis indicated that turquoise module has the highest association with pathological grade. Gene Ontology enrichment analysis revealed that the genes in turquoise module main enrichment in inflammatory response and immune response. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the genes in turquoise module main enrichment in cytokine-cytokine receptor interaction and chemokine signaling pathway. In turquoise module, a total of 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were identified. Then, 4 hub genes were effectively verified in the test datasets (GSE63885, GSE26193 and GSE30161) and tissue samples from Shengjing Hospital of China Medical University. Survival analysis indicated that the 4 hub genes were associated with poor progression-free survival of ovarian cancer. CONCLUSIONS: In conclusion, 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were verified associated with pathological grade of ovarian cancer. Moreover, MS4A4A, CD163, MS4A6A may serve as a surface marker for M2 macrophages. Targeting the 4 hub genes may can improve the prognosis of ovarian cancer.
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Cai, Guiquan, Xuan Yang, Ting Chen, Fangchun Jin, Jing Ding, and Zhenkai Wu. "Integrated bioinformatics analysis of potential pathway biomarkers using abnormal proteins in clubfoot." PeerJ 8 (January 20, 2020): e8422. http://dx.doi.org/10.7717/peerj.8422.

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Background As one of the most common major congenital distal skeletal abnormalities, congenital talipes equinovarus (clubfoot) affects approximately one in one thousandth newborns. Although several etiologies of clubfoot have been proposed and several genes have been identified as susceptible genes, previous studies did not further explore signaling pathways and potential upstream and downstream regulatory networks. Therefore, the aim of the present investigation is to explore abnormal pathways and their interactions in clubfoot using integrated bioinformatics analyses. Methods KEGG, gene ontology (GO), Reactome (REAC), WikiPathways (WP) or human phenotype ontology (HP) enrichment analysis were performed using WebGestalt, g:Profiler and NetworkAnalyst. Results A large number of signaling pathways were enriched e.g. signal transduction, disease, metabolism, gene expression (transcription), immune system, developmental biology, cell cycle, and ECM. Protein-protein interactions (PPIs) and gene regulatory networks (GRNs) analysis results indicated that extensive and complex interactions occur in these proteins, enrichment pathways, and TF-miRNA coregulatory networks. Transcription factors such as SOX9, CTNNB1, GLI3, FHL2, TGFBI and HOXD13, regulated these candidate proteins. Conclusion The results of the present study supported previously proposed hypotheses, such as ECM, genetic, muscle, neurological, skeletal, and vascular abnormalities. More importantly, the enrichment results also indicated cellular or immune responses to external stimuli, and abnormal molecular transport or metabolism may be new potential etiological mechanisms of clubfoot.
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Chen, Lei, Chen Chu, Jing Lu, Xiangyin Kong, Tao Huang, and Yu-Dong Cai. "Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System." PLOS ONE 10, no. 5 (May 7, 2015): e0126492. http://dx.doi.org/10.1371/journal.pone.0126492.

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Elbers, Jean P., and Sabrina S. Taylor. "GO2TR: a gene ontology-based workflow to generate target regions for target enrichment experiments." Conservation Genetics Resources 7, no. 4 (August 20, 2015): 851–57. http://dx.doi.org/10.1007/s12686-015-0487-6.

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Tibaldi, D. S., C. Sposito, P. T. Del Giudice, R. M. Fariello, D. Spaine, and R. Fraietta. "Proteomic profile and functional enrichment of gene ontology terms in men with testicular cancer." Fertility and Sterility 96, no. 3 (September 2011): S206. http://dx.doi.org/10.1016/j.fertnstert.2011.07.798.

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36

Zhao, Yudong, Yu Xia, Gaoyan Kuang, Jihui Cao, Fu Shen, and Mingshuang Zhu. "Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis." Computational and Mathematical Methods in Medicine 2022 (June 23, 2022): 1–21. http://dx.doi.org/10.1155/2022/9043300.

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Background. Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. Methods. GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. Results. In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. Conclusions. CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.
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Zhang, Hao, Xi Chen, and Yufeng Yuan. "Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma." BioMed Research International 2020 (November 12, 2020): 1–19. http://dx.doi.org/10.1155/2020/8726567.

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Purpose. To identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma. Methods. mRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in R language ( R package multiMiR). After that, downregulated miRNAs were matched with target mRNAs which were upregulated, while upregulated miRNAs were paired with downregulated target mRNA using scripts written in Perl. An miRNA-mRNA network was constructed and visualized in Cytoscape software. For miRNAs in the network, survival analysis was performed. And for genes in the network, we did gene ontology (GO) and KEGG pathway enrichment analysis. Results. A total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated. Conclusion. In conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.
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Fattahi, Fahimeh, Jafar Kiani, Mohsen Khosravi, Somayeh Vafaei, Asghar Mohammadi, Zahra Madjd, and Mohammad Najafi. "Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer." Combinatorial Chemistry & High Throughput Screening 22, no. 8 (December 19, 2019): 534–45. http://dx.doi.org/10.2174/1386207321666191010114149.

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Aim and Objective: It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer. Methods: The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs. Results: Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found. Conclusion: The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC.
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Wen, Zhifeng, Fangxi Liu, Xinyu Lin, Shanshan Zhong, Xiuchun Zhang, Zhike Zhou, Jukka Jolkkonen, Chang Liu, and Chuansheng Zhao. "Prognostic Signature for Human Umbilical Cord Mesenchymal Stem Cell Treatment of Ischemic Cerebral Infarction by Integrated Bioinformatic Analysis." BioMed Research International 2022 (December 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/9973232.

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In recent studies, stem cell-based therapy is a potential new approach in the treatment of stroke. The mechanism of human umbilical cord mesenchymal stem cell (hUMSC) transplantation as one of the new approaches in the treatment of ischemic stroke is still unclear. The aim of this study was to determine the traits of immune responses during stroke progression after treatment with human umbilical cord blood MSCs by bioinformatics, to predict potential prognostic biomarkers that could lead to sex differences, and to reveal potential therapeutic targets. The microarray dataset GSE78731 (mRNA profile) of middle cerebral artery occlusion (MCAO) rats was obtained from the Gene Expression Omnibus (GEO) database. First, two potentially expressed genes (DEGs) were screened using the Bioconductor R package. Ultimately, 30 specific DEGs were obtained (22 upregulated and 353 downregulated). Next, bioinformatic analysis was performed on these specific DEGs. We performed a comparison for the differentially expressed genes screened from between the hUMSC and MCAO groups. Gene Ontology enrichment and pathway enrichment analyses were then performed for annotation and visualization. Gene Ontology (GO) functional annotation analysis shows that DEGs are mainly enriched in leukocyte migration, neutrophil activation, neutrophil degranulation, the external side of plasma membrane, cytokine receptor binding, and carbohydrate binding. KEGG pathway enrichment analysis showed that the first 5 enrichment pathways were cytokine-cytokine receptor interaction, chemokine signal pathway, viral protein interaction with cytokine and cytokine receptor, cell adhesion molecules (CAMs), and phagosome. The top 10 key genes of the constructed PPI network were screened, including Cybb, Ccl2, Cd68, Ptprc, C5ar1, Il-1b, Tlr2, Itgb2, Itgax, and Cd44. In summary, hUMSC is likely to be a promising means of treating IS by immunomodulation.
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Kuleshov, Maxim V., Jennifer E. L. Diaz, Zachary N. Flamholz, Alexandra B. Keenan, Alexander Lachmann, Megan L. Wojciechowicz, Ross L. Cagan, and Avi Ma’ayan. "modEnrichr: a suite of gene set enrichment analysis tools for model organisms." Nucleic Acids Research 47, W1 (May 9, 2019): W183—W190. http://dx.doi.org/10.1093/nar/gkz347.

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Abstract High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.
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Sharov, Alexei A., David Schlessinger, and Minoru S. H. Ko. "ExAtlas: An interactive online tool for meta-analysis of gene expression data." Journal of Bioinformatics and Computational Biology 13, no. 06 (December 2015): 1550019. http://dx.doi.org/10.1142/s0219720015500195.

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We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users’ own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher’s methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein–protein interaction) are pre-loaded and can be used for functional annotations.
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Yang, Wentao, Katja Dierking, and Hinrich Schulenburg. "WormExp: a web-based application for a Caenorhabditis elegans-specific gene expression enrichment analysis." Bioinformatics 32, no. 6 (November 11, 2015): 943–45. http://dx.doi.org/10.1093/bioinformatics/btv667.

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Abstract Motivation: A particular challenge of the current omics age is to make sense of the inferred differential expression of genes and proteins. The most common approach is to perform a gene ontology (GO) enrichment analysis, thereby relying on a database that has been extracted from a variety of organisms and that can therefore only yield reliable information on evolutionary conserved functions. Results: We here present a web-based application for a taxon-specific gene set exploration and enrichment analysis, which is expected to yield novel functional insights into newly determined gene sets. The approach is based on the complete collection of curated high-throughput gene expression data sets for the model nematode Caenorhabditis elegans, including 1786 gene sets from more than 350 studies. Availability and implementation: WormExp is available at http://wormexp.zoologie.uni-kiel.de. Contacts: hschulenburg@zoologie.uni-kiel.de Supplementary information: Supplementary data are available at Bioinformatics online.
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Carlevaro-Fita, Joana, Leibo Liu, Yuan Zhou, Shan Zhang, Panagiotis Chouvardas, Rory Johnson, and Jianwei Li. "LnCompare: gene set feature analysis for human long non-coding RNAs." Nucleic Acids Research 47, W1 (May 31, 2019): W523—W529. http://dx.doi.org/10.1093/nar/gkz410.

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Abstract Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets.
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Wiebe, Daniil S., Nadezhda A. Omelyanchuk, Aleksei M. Mukhin, Ivo Grosse, Sergey A. Lashin, Elena V. Zemlyanskaya, and Victoria V. Mironova. "Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups." Genes 11, no. 4 (April 17, 2020): 434. http://dx.doi.org/10.3390/genes11040434.

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Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.
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Hadjieva, Nikol, Elena Apostolova, Vesselin Baev, Galina Yahubyan, and Mariyana Gozmanova. "Transcriptome Analysis Reveals Dynamic Cultivar-Dependent Patterns of Gene Expression in Potato Spindle Tuber Viroid-Infected Pepper." Plants 10, no. 12 (December 7, 2021): 2687. http://dx.doi.org/10.3390/plants10122687.

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Potato spindle tuber viroid (PSTVd) infects various plants. PSTVd pathogenesis is associated with interference with the cellular metabolism and defense signaling pathways via direct interaction with host factors or via the transcriptional or post-transcriptional modulation of gene expression. To better understand host defense mechanisms to PSTVd infection, we analyzed the gene expression in two pepper cultivars, Capsicum annuum Kurtovska kapia (KK) and Djulunska shipka (DS), which exhibit mild symptoms of PSTVd infection. Deep sequencing-based transcriptome analysis revealed differential gene expression upon infection, with some genes displaying contrasting expression patterns in KK and DS plants. More genes were downregulated in DS plants upon infection than in KK plants, which could underlie the more severe symptoms seen in DS plants. Gene ontology enrichment analysis revealed that most of the downregulated differentially expressed genes in both cultivars were enriched in the gene ontology term photosynthesis. The genes upregulated in DS plants fell in the biological process of gene ontology term defense response. We validated the expression of six overlapping differentially expressed genes that are involved in photosynthesis, plant hormone signaling, and defense pathways by quantitative polymerase chain reaction. The observed differences in the responses of the two cultivars to PSTVd infection expand the understanding of the fine-tuning of plant gene expression that is needed to overcome the infection.
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Köhler, Sebastian, Sandra C. Doelken, Barbara J. Ruef, Sebastian Bauer, Nicole Washington, Monte Westerfield, George Gkoutos, et al. "Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research." F1000Research 2 (February 1, 2013): 30. http://dx.doi.org/10.12688/f1000research.2-30.v1.

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Phenotype analyses, e.g. investigating metabolic processes, tissue formation, or organism behavior, are an important element of most biological and medical research activities. Biomedical researchers are making increased use of ontological standards and methods to capture the results of such analyses, with one focus being the comparison and analysis of phenotype information between species.We have generated a cross-species phenotype ontology for human, mouse and zebra fish that contains zebrafish phenotypes. We also provide up-to-date annotation data connecting human genes to phenotype classes from the generated ontology. We have included the data generation pipeline into our continuous integration system ensuring stable and up-to-date releases.This article describes the data generation process and is intended to help interested researchers access both the phenotype annotation data and the associated cross-species phenotype ontology. The resource described here can be used in sophisticated semantic similarity and gene set enrichment analyses for phenotype data across species. The stable releases of this resource can be obtained from http://purl.obolibrary.org/obo/hp/uberpheno/.
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Fan, Rong, Lijin Dong, Ping Li, Xiaoming Wang, and Xuewei Chen. "Integrated bioinformatics analysis and screening of hub genes in papillary thyroid carcinoma." PLOS ONE 16, no. 6 (June 11, 2021): e0251962. http://dx.doi.org/10.1371/journal.pone.0251962.

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Background With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC. Methods GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry. Results We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip. Conclusions In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.
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Varisli, Lokman, and Veysel Tolan. "Silencing of HN1L suppresses the proliferation and migration of cancer cells." Periodicum Biologorum 124, no. 1-2 (November 29, 2022): 55–62. http://dx.doi.org/10.18054/pb.v124i1-2.20098.

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Background and purpose: HN1L is a member of the HN1 gene family and shares about 30% similarity with HN1 which is another member of the family on the primary protein sequence. Since HN1 is an important gene that is involved in various cellular mechanisms and also differentially expressed in carcinogenesis, we investigated the effect of HN1L on some malignant behaviors of various cancer cells.Material and methods: Co-expression analysis, Gene Ontology enrichment, and database searches were performed to predict the cellular roles of HN1, and to investigate its expression in cancers and their corresponding normal tissues. Western blotting and Real-Time PCR were used to compare the expression of HN1L in the normal prostate cells and prostate cancer cells. Cell proliferation and migration assays were used to investigate the effects of HN1L depletion on cell proliferation and migration.Results: The results of co-expression and Gene Ontology enrichment analyses showed that HN1L is co-expressed with DNA replication and DNA damage response/repair associated genes. The database search results revealed that HN1L expression increases in at least 10 diverse cancer types compared to their normal corresponding tissues. This result was confirmed in the prostate cancer cell model, experimentally. Silencing of HN1L inhibited proliferative and migrative behaviors of prostate, breast, colon, and cervix cancer cells.Conclusions: HN1L probably is a novel proto-oncogene that is involved in the DNA metabolism-related mechanisms, and high HN1L level promotes further proliferation and migration in the cancer cells.
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Li, Shaojuan, Changxin Wan, Rongbin Zheng, Jingyu Fan, Xin Dong, Clifford A. Meyer, and X. Shirley Liu. "Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP-seq peaks." Nucleic Acids Research 47, W1 (May 4, 2019): W206—W211. http://dx.doi.org/10.1093/nar/gkz332.

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AbstractCharacterizing the ontologies of genes directly regulated by a transcription factor (TF), can help to elucidate the TF’s biological role. Previously, we developed a widely used method, BETA, to integrate TF ChIP-seq peaks with differential gene expression (DGE) data to infer direct target genes. Here, we provide Cistrome-GO, a website implementation of this method with enhanced features to conduct ontology analyses of gene regulation by TFs in human and mouse. Cistrome-GO has two working modes: solo mode for ChIP-seq peak analysis; and ensemble mode, which integrates ChIP-seq peaks with DGE data. Cistrome-GO is freely available at http://go.cistrome.org/.
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Cai, Lihua, Honglong Wu, and Ke Zhou. "Improved cancer biomarkers identification using network-constrained infinite latent feature selection." PLOS ONE 16, no. 2 (February 11, 2021): e0246668. http://dx.doi.org/10.1371/journal.pone.0246668.

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Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.
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