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Related Experiment Video

Updated: Jun 4, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Meet me halfway: when genomics meets structural bioinformatics.

Sungsam Gong¹, Catherine L Worth, Tammy M K Cheng

¹Biocomputing Group, Department of Biochemistry, University of Cambridge, UK. s.gong@rbht.nhs.uk

Journal of Cardiovascular Translational Research

|February 26, 2011

Summary

Next-generation sequencing (NGS) advances genetic disease research. Integrating omics data with functional genomics and systems biology aids understanding complex genotype-phenotype relationships for diseases like diabetes and cancer.

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Last Updated: Jun 4, 2026

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Area of Science:

Genomics
Systems Biology
Functional Genomics

Background:

Frederick Sanger's DNA sequencing revolutionized comparative genetics.
Next-generation sequencing (NGS) provides ultra-fast, high-throughput genomic data.
NGS enables studying genetic diseases by identifying sequence variants linked to phenotypes.

Purpose of the Study:

To review approaches for identifying genetic variants in complex diseases.
To integrate omics data with protein structure/function and network/pathway knowledge.
To enhance understanding of genotype-phenotype relationships in genetic diseases.

Main Methods:

Describing experimental and computational techniques for variant assessment.
Utilizing functional genomics and systems biology approaches.
Reviewing NGS-driven genetic studies of variations and disease etiology.

Main Results:

NGS technology accelerates the identification of genetic variations.
Complex diseases like diabetes and cancer involve multiple genes and alleles.
Integrating diverse data types is crucial for understanding genotype-phenotype links.

Conclusions:

A 'bottom-up' approach integrating omics, protein data, and systems biology is valuable.
Understanding deleterious effects of genetic variants on protein structure/function is key.
This integrated approach improves the study of genetic variations and disease etiology.