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Updated: Jan 12, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Next generation DNA sequencing data analysis and its application in clinical genomics
Abhijit Beura1, Gowrang Kasaba Manjunath1, Shweta Mahalingam1
1Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India; Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka, India.
Next-generation sequencing (NGS) revolutionizes genomics for cancer research and rare disease diagnosis. This technology identifies genetic variants crucial for developing targeted therapies and advancing personalized medicine.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Next-generation sequencing (NGS) has become a cornerstone of modern genomics.
- It enables rapid, high-throughput analysis of DNA and RNA.
- NGS drives progress in cancer research, rare disease diagnosis, and personalized medicine.
Purpose of the Study:
- To review the diverse applications of Next-generation sequencing (NGS).
- To highlight the role of NGS in identifying genetic variants for targeted therapies.
- To discuss the workflow, tools, and ethical considerations of NGS.
Main Methods:
- Review of current literature on Next-generation sequencing (NGS) applications.
- Discussion of the NGS workflow, including data quality control, alignment, and variant calling.
- Exploration of cloud-based platforms and essential databases (e.g., dbSNP, COSMIC, TCGA) for data interpretation.
Main Results:
- NGS significantly aids in identifying genetic variants that inform targeted therapy development.
- The NGS workflow is supported by various open-source and commercial tools.
- Cloud platforms enhance the management and processing of large NGS datasets.
Conclusions:
- NGS is pivotal for advancing human health and understanding disease mechanisms.
- Ethical considerations, including data privacy and informed consent, are critical.
- Future directions include integrating multi-omics and single-cell sequencing for deeper biological insights.
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