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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
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Bioinformatics tools for discovery and functional analysis of single nucleotide polymorphisms
1State Key Laboratory of Microbial Metabolism, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Advances in Experimental Medicine and Biology
|November 13, 2014
Summary
This review explores bioinformatics tools and resources for discovering and analyzing single nucleotide polymorphisms (SNPs), crucial genetic variations. It highlights computational methods for predicting SNP effects and provides examples, aiding disease research.
Area of Science:
- Genomics
- Bioinformatics
- Human Genetics
Background:
- Genome sequencing advances fuel exponential growth in genetic variation data.
- Single nucleotide polymorphisms (SNPs) are key genetic variations for understanding genome function and disease.
- Bioinformatics is vital for analyzing genetic changes and their impact.
Purpose of the Study:
- To systematically review resources and methods for SNP discovery and analysis.
- To present computational approaches for predicting SNP functions and effects.
- To provide a comprehensive overview of available tools and online resources for SNP research.
Main Methods:
- Literature review of bioinformatics techniques for SNP analysis.
- Computational prediction of SNPs in human cytochrome P450 genes.
- Annotation and functional prediction of genetic variants.
Main Results:
- Identification of key bioinformatics resources and methods for SNP discovery.
- Demonstration of DNA sequence-based SNP prediction in a specific gene.
- Compilation of tools and online resources for SNP analysis.
Conclusions:
- Computational methods are essential for SNP selection, prediction, and understanding genetic variation.
- The review offers a valuable guide to SNP analysis resources for researchers.
- Predictive bioinformatics approaches enhance the study of genetic variations and disease.
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