Capable of generating gigabytes of sequence data per day, NGS has fueled an explosion in our genomic knowledge base, adding substantially more breadth and depth to our understanding of health and disease in the process.
Initially NGS DNA sequencing (DNA-Seq) methods were applied to improve the scale and speed of whole genome sequencing (WGS). The power and practical utility of NGS have continued to grow as the technology has been extended to enable longer reads and include more modalities.
NGS technology platforms can now be used for:
- Whole genome sequencing (WGS)
- Targeted panel testing (amplicon- and capture-based approaches)
- SNP calling and targeted genotyping by sequencing (tGBS)
- Whole exome sequencing (WES)
- RNA sequencing (RNA-Seq, miRNA-Seq)
- Chromatin immunoprecipitation (ChIP-Seq)
- Methylation sequencing (Methyl-Seq)
- Regulatory analysis (CLIP-Seq)
- Metagenomics and microbiomics
- Liquid biopsy
- Single-cell sequencing
NGS-based WGS and WES methods have already starting to transform the way various cancers are treated, and have enabled diagnosis of novel genetic disorders. Information from large-scale genome sequencing projects is expanding known genetic profiles across more populations, paving the way for more practical clinical application of genome-wide analyses in disease diagnosis and personalized medicine.
Compared to microarray approaches for gene expression analysis, NGS RNA-Seq offers greater resolution and overcomes array ‘design bias’. NGS can also provide a more complete genomic picture in applications such as SNP genotyping and methylation analysis, rather than being limited by the capacity of the microarray.
In the context of the clinic, NGS offers a significant advantages over conventional gene-by-gene sequencing approaches, particularly when applied to identifying mosaicism in disease diagnosis or elucidating the genetic basis of complex phenotypes, such as those manifested in autoinflammatory diseases, which can be caused by many different genes. NGS methods also offer the potential of higher resolution in cytogenetic testing, although microarrays are currently more cost-efficient and therefore more widely used for such applications.