DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis

2019 ◽  
Author(s):  
Radoslav Davidović ◽  
Vladimir Perovic ◽  
Branislava Gemovic ◽  
Nevena Veljkovic
Keyword(s):  
Gene Ontology ◽  
Source Code ◽  
Enrichment Analysis ◽  
Human Phenotype Ontology ◽  
Supplementary Information ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Human Phenotype ◽  
Term Enrichment Analysis ◽  
Term Enrichment

Abstract Summary Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. Availability and Implementation DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transcriptome analysis of sevoflurane exposure effects at the different brain regions

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0236771
Author(s):  
Hiroto Yamamoto ◽  
Yutaro Uchida ◽  
Tomoki Chiba ◽  
Ryota Kurimoto ◽  
Takahide Matsushima ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Rna Seq ◽  
Ontology Term ◽  
Binding Motifs ◽  
Term Enrichment Analysis ◽  
Term Enrichment ◽  
Differently Expressed Genes

Backgrounds Sevoflurane is a most frequently used volatile anesthetics, but its molecular mechanisms of action remain unclear. We hypothesized that specific genes play regulatory roles in brain exposed to sevoflurane. Thus, we aimed to evaluate the effects of sevoflurane inhalation and identify potential regulatory genes by RNA-seq analysis. Methods Eight-week old mice were exposed to sevoflurane. RNA from medial prefrontal cortex, striatum, hypothalamus, and hippocampus were analysed using RNA-seq. Differently expressed genes were extracted and their gene ontology terms were analysed using Metascape. These our anesthetized mouse data and the transcriptome array data of the cerebral cortex of sleeping mice were compared. Finally, the activities of transcription factors were evaluated using a weighted parametric gene set analysis (wPGSA). JASPAR was used to confirm the existence of binding motifs in the upstream sequences of the differently expressed genes. Results The gene ontology term enrichment analysis result suggests that sevoflurane inhalation upregulated angiogenesis and downregulated neural differentiation in each region of brain. The comparison with the brains of sleeping mice showed that the gene expression changes were specific to anesthetized mice. Focusing on individual genes, sevoflurane induced Klf4 upregulation in all sampled parts of brain. wPGSA supported the function of KLF4 as a transcription factor, and KLF4-binding motifs were present in many regulatory regions of the differentially expressed genes. Conclusions Klf4 was upregulated by sevoflurane inhalation in the mouse brain. The roles of KLF4 might be key to elucidating the mechanisms of sevoflurane induced functional modification in the brain.

scholarly journals PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 259 ◽  
Author(s):  
Indika Kahanda ◽  
Christopher Funk ◽  
Karin Verspoor ◽  
Asa Ben-Hur
Keyword(s):  
Large Scale ◽  
Heterogeneous Data ◽  
Human Phenotype Ontology ◽  
Data Sources ◽  
Literature Mining ◽  
Phenotype Ontology ◽  
Ontology Term ◽  
Protein Coding ◽  
Human Phenotype ◽  
Heterogeneous Data Sources

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

scholarly journals PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology

2021 ◽  
Author(s):  
Ling Luo ◽  
Shankai Yan ◽  
Po-Ting Lai ◽  
Daniel Veltri ◽  
Andrew Oler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Human Phenotype Ontology ◽  
Training Data ◽  
Supplementary Information ◽  
Training Dataset ◽  
Biomedical Text ◽  
Phenotype Ontology ◽  
Concept Recognition ◽  
Human Phenotype

Abstract Motivation Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation. Results In this article, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep learning model is trained to classify each candidate phrase (n-gram from input sentence) into a corresponding concept label. Finally, the dictionary and machine learning-based prediction results are combined for improved performance. Our method is validated with two HPO corpora, and the results show that PhenoTagger compares favorably to previous methods. In addition, to demonstrate the generalizability of our method, we retrained PhenoTagger using the disease ontology MEDIC for disease concept recognition to investigate the effect of training on different ontologies. Experimental results on the NCBI disease corpus show that PhenoTagger without requiring manually annotated training data achieves competitive performance as compared with state-of-the-art supervised methods. Availabilityand implementation The source code, API information and data for PhenoTagger are freely available at https://github.com/ncbi-nlp/PhenoTagger. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rasmus Magnusson ◽  
Zelmina Lubovac-Pilav
Keyword(s):  
Gene Ontology ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Ontology Term ◽  
Term Enrichment ◽  
Transcription Factor Target ◽  
Downstream Processes ◽  
Biological Context

Abstract Background Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes. Results We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription. Conclusions and availability The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at https://github.com/rasma774/Tftenricher, under a GNU GPL license and with minimal dependencies.

Predicting genes from phenotypes using Human Phenotype Ontology (HPO) terms

2021 ◽  
Vol 132 ◽  
pp. S149
Author(s):  
Anne Slavotinek ◽  
Hannah Prasad ◽  
Hannah Hoban ◽  
Tiffany Yip ◽  
Shannon Rego ◽  
...  
Keyword(s):  
Human Phenotype Ontology ◽  
Phenotype Ontology ◽  
Human Phenotype
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