Sequence variation in genes and genomic DNA: methods for large-scale analysis.
1Department of Biochemistry, University of Oxford, Oxford OX1 3QU, United Kingdom. Kalim@bioch.ox.ac.uk
Annual Review of Genomics and Human Genetics
|November 10, 2001
Summary
Current technologies face challenges in large-scale DNA sequence variation analysis. This review examines current and emerging methods, focusing on DNA chip/array platforms for discovering single-nucleotide polymorphisms.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Large-scale analysis of genetic sequence variation is crucial for understanding diseases.
- Existing technologies may lack the sensitivity, robustness, or scalability required for comprehensive genomic studies.
Purpose of the Study:
- To review and assess current technologies for large-scale DNA sequence variation typing.
- To evaluate the suitability of different platforms, with a focus on DNA chip/array technologies.
- To identify emerging technologies for future genomic variation analysis.
Main Methods:
- Survey of current platform technologies including mass spectrometry, homogeneous assays, and solid-phase/array-based assays.
- Assessment of techniques for discovering and typing genetic variation, particularly single-nucleotide polymorphisms.
- In-depth examination of DNA chip/array platforms and relevant large-scale studies.
Main Results:
- Current technologies present limitations in sensitivity, robustness, and scalability for large-scale genomic variation analysis.
- DNA chip/array platforms show promise for large-scale single-nucleotide polymorphism discovery and typing.
- Large-scale amplification remains a challenge, necessitating the development of new approaches.
Conclusions:
- Further technological advancements are needed to meet the demands of large-scale genomic variation analysis.
- DNA chip/array technologies are a key focus for current and future large-scale genetic studies.
- Emerging technologies are essential for addressing the challenges in genomic data generation and analysis.
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