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Computational resources associating diseases with genotypes, phenotypes and exposures.

Wenliang Zhang1, Haiyue Zhang1, Huan Yang1

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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.

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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.