next-generation sequencing
Definition
Next-generation sequencing (NGS) encompasses high-throughput DNA and RNA sequencing technologies that enable parallel sequencing of millions to billions of nucleotide fragments simultaneously. Unlike traditional Sanger sequencing, NGS platforms generate massive datasets at reduced cost and time, revolutionizing genomics, transcriptomics, epigenomics, and metagenomics research. Key NGS applications include whole-genome sequencing, RNA-seq for transcriptome profiling, ChIP-seq for protein-DNA interactions, and targeted sequencing panels. The technology relies on library preparation, cluster generation, sequencing-by-synthesis or ligation, and computational analysis pipelines. NGS has transformed personalized medicine, cancer genomics, rare disease diagnosis, and microbial community analysis by providing unprecedented resolution of genetic variation, gene expression patterns, and molecular interactions across biological systems.
Visualize next-generation sequencing in Nodes Bio
Researchers can visualize NGS-derived data as multi-layered networks in Nodes Bio, connecting differentially expressed genes to regulatory pathways, protein interactions, and phenotypic outcomes. RNA-seq results can be mapped onto gene regulatory networks to identify master regulators, while variant calling data can be integrated with protein-protein interaction networks to predict functional consequences of mutations and prioritize therapeutic targets.
Visualization Ideas:
- Differentially expressed gene networks from RNA-seq mapped to biological pathways
- Variant-to-phenotype networks connecting genomic mutations to protein interactions and disease outcomes
- Multi-omics integration networks combining NGS transcriptomics with proteomics and metabolomics data
Example Use Case
A cancer research team performs RNA-seq on tumor samples from responders and non-responders to immunotherapy. They identify 500 differentially expressed genes and use Nodes Bio to map these genes onto immune signaling pathways and cytokine networks. The visualization reveals that responders show upregulation of interferon-gamma signaling nodes and increased connectivity in antigen presentation pathways. This network-based analysis identifies three hub genes as potential biomarkers for treatment response prediction.