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Computational resources associating diseases with genotypes, phenotypes and exposures.
Wenliang Zhang1, Haiyue Zhang1, Huan Yang1
1Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
Briefings in Bioinformatics
|August 14, 2018
Summary
Identifying disease causes requires integrating genetic and environmental data. This review aids researchers by cataloging computational tools for analyzing genotype, phenotype, and exposure data to understand complex human diseases.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Disease etiology involves complex interactions between genotypes, phenotypes, environmental exposures, and chemical factors.
- Distinguishing disease-relevant factors from neutral ones is a significant challenge in biomedical research.
- Numerous computational resources have been developed to integrate omics data and identify disease associations.
Purpose of the Study:
- To systematically review public computational resources for human disease research.
- To assist researchers and clinicians in selecting appropriate tools for analyzing disease-related factors.
- To provide an overview of resources linking genotypes, phenotypes, environmental factors, drugs, and chemical exposures to diseases.
Main Methods:
- Systematic literature review of public computational resources.
- Categorization of resources based on data types (genotype, phenotype, environment, drugs, chemicals).
- Description of the development history and functionalities of relevant databases and software tools.
Main Results:
- A comprehensive catalog of computational resources for disease factor analysis is presented.
- Resources cover diverse aspects including genetic, phenotypic, environmental, and chemical influences on diseases.
- The review details the evolution and application of these bioinformatics tools.
Conclusions:
- Researchers and clinicians face challenges in choosing from numerous available computational resources.
- This review offers a structured guide to facilitate the selection of appropriate tools for disease-related factor analysis.
- Future opportunities lie in further developing and integrating these resources to advance our understanding of human diseases.
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