scholarly journals PhenoMiner: quantitative phenotype curation at the rat genome database

Database ◽  
2013 ◽  
Vol 2013 ◽  
Author(s):  
Stanley J F Laulederkind ◽  
Weisong Liu ◽  
Jennifer R Smith ◽  
G Thomas Hayman ◽  
Shur-Jen Wang ◽  
...  
Keyword(s):  
Biological Information ◽  
Ontology Development ◽  
Rat Strains ◽  
Genome Database ◽  
Quantitative Phenotype ◽  
Phenotype Data ◽  
Disease Associations ◽  
Data Objects ◽  
Cellular Components ◽  
Rat Genome

Abstract The Rat Genome Database (RGD) is the premier repository of rat genomic and genetic data and currently houses >40 000 rat gene records as well as human and mouse orthologs, >2000 rat and 1900 human quantitative trait loci (QTLs) records and >2900 rat strain records. Biological information curated for these data objects includes disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components. Recently, a project was initiated at RGD to incorporate quantitative phenotype data for rat strains, in addition to the currently existing qualitative phenotype data for rat strains, QTLs and genes. A specialized curation tool was designed to generate manual annotations with up to six different ontologies/vocabularies used simultaneously to describe a single experimental value from the literature. Concurrently, three of those ontologies needed extensive addition of new terms to move the curation forward. The curation interface development, as well as ontology development, was an ongoing process during the early stages of the PhenoMiner curation project. Database URL: http://rgd.mcw.edu

scholarly journals Rat Genome Database: a unique resource for rat, human, and mouse quantitative trait locus data

2013 ◽  
Vol 45 (18) ◽  
pp. 809-816 ◽  
Author(s):  
Rajni Nigam ◽  
Stanley J. F. Laulederkind ◽  
G. Thomas Hayman ◽  
Jennifer R. Smith ◽  
Shur-Jen Wang ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Disease Model ◽  
Mouse Genome Informatics ◽  
Genome Database ◽  
Gene Sets ◽  
Human Genomes ◽  
Phenotype Data ◽  
Disease Associations ◽  
Trait Locus ◽  
Rat Genome

The rat has been widely used as a disease model in a laboratory setting, resulting in an abundance of genetic and phenotype data from a wide variety of studies. These data can be found at the Rat Genome Database (RGD, http://rgd.mcw.edu/ ), which provides a platform for researchers interested in linking genomic variations to phenotypes. Quantitative trait loci (QTLs) form one of the earliest and core datasets, allowing researchers to identify loci harboring genes associated with disease. These QTLs are not only important for those using the rat to identify genes and regions associated with disease, but also for cross-organism analyses of syntenic regions on the mouse and the human genomes to identify potential regions for study in these organisms. Currently, RGD has data on >1,900 rat QTLs that include details about the methods and animals used to determine the respective QTL along with the genomic positions and markers that define the region. RGD also curates human QTLs (>1,900) and houses >4,000 mouse QTLs (imported from Mouse Genome Informatics). Multiple ontologies are used to standardize traits, phenotypes, diseases, and experimental methods to facilitate queries, analyses, and cross-organism comparisons. QTLs are visualized in tools such as GBrowse and GViewer, with additional tools for analysis of gene sets within QTL regions. The QTL data at RGD provide valuable information for the study of mapped phenotypes and identification of candidate genes for disease associations.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

scholarly journals Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0148521 ◽  
Author(s):  
Hongbo Shi ◽  
Guangde Zhang ◽  
Meng Zhou ◽  
Liang Cheng ◽  
Haixiu Yang ◽  
...  
Keyword(s):  
Novel Mirna ◽  
Phenotype Data ◽  
Disease Associations

scholarly journals The Rat Genome Database 2013--data, tools and users

2013 ◽  
Vol 14 (4) ◽  
pp. 520-526 ◽  
Author(s):  
S. J. F. Laulederkind ◽  
G. T. Hayman ◽  
S.-J. Wang ◽  
J. R. Smith ◽  
T. F. Lowry ◽  
...  
Keyword(s):  
Genome Database ◽  
Rat Genome

scholarly journals Disease pathways at the Rat Genome Database Pathway Portal: genes in context-a network approach to understanding the molecular mechanisms of disease

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Victoria Petri ◽  
G Thomas Hayman ◽  
Marek Tutaj ◽  
Jennifer R Smith ◽  
Stanley JF Laulederkind ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Network Approach ◽  
Genome Database ◽  
Rat Genome ◽  
Disease Pathways

scholarly journals How much do model organism phenotypes contribute to the computational identification of human disease genes?

2021 ◽  
Author(s):  
Sarah M Alghamdi ◽  
Paul N Schofield ◽  
Robert Hoehndorf
Keyword(s):  
Human Gene ◽  
Model Organism ◽  
Model Organisms ◽  
Disease Genes ◽  
Loss Of Function ◽  
Relative Contribution ◽  
Phenotype Data ◽  
Disease Associations ◽  
Disease Associated Genes ◽  
Human Disease Genes

Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype--phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to identifying disease-associated genes using computational approaches is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene--disease associations. We find that only mouse phenotypes can accurately predict human gene--disease associations. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

scholarly journals Tools and strategies for physiological genomics: the Rat Genome Database

2005 ◽  
Vol 23 (2) ◽  
pp. 246-256 ◽  
Author(s):  
Simon N. Twigger ◽  
Dean Pasko ◽  
Jeff Nie ◽  
Mary Shimoyama ◽  
Susan Bromberg ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Computational Biology ◽  
Computational Techniques ◽  
Genome Database ◽  
Computational Tools ◽  
Functional Roles ◽  
Genomics Research ◽  
Human And Mouse ◽  
Rat Genome

The broad goal of physiological genomics research is to link genes to their functions using appropriate experimental and computational techniques. Modern genomics experiments enable the generation of vast quantities of data, and interpretation of this data requires the integration of information derived from many diverse sources. Computational biology and bioinformatics offer the ability to manage and channel this information torrent. The Rat Genome Database (RGD; http://rgd.mcw.edu ) has developed computational tools and strategies specifically supporting the goal of linking genes to their functional roles in rat and, using comparative genomics, to human and mouse. We present an overview of the database with a focus on these unique computational tools and describe strategies for the use of these resources in the area of physiological genomics.

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