Next Generation Sequencing (NGS)

- Next Generation Sequencing (NGS)

Next Generation Sequencing (NGS) technologies offer high-throughput, rapid and accurate methods of determining the precise order of nucleotides within DNA/RNA molecules. NGS, massively parallel or deep sequencing are related terms that describe a DNA sequencing technology which has revolutionised genomic research. 

Since the advent of modern sequencing techniques, identification of nucleic acid sequences has become a ubiquitous and essential tool across all areas of biological science. Correspondingly, the field of bioinformatics is central to the interpretation and application of this biological data. 

Using mathematical and statistical methods implemented by a wide range of programmatic languages, bioinformatics tools organise, analyse and interpret biological information at the molecular, cellular and genomic level. The combined power of NGS and bioinformatics is vital for diagnostics, medical treatment and epidemiological research.

NGS, massively parallel or deep sequencing are related terms that describe a DNA sequencing technology which has revolutionised genomic research. Using NGS an entire human genome can be sequenced within a single day. In contrast, the previous Sanger sequencing technology, used to decipher the human genome, required over a decade to deliver the final draft. Although in genome research NGS has mostly superseded conventional Sanger sequencing, it has not yet translated into routine clinical practice. 

Next-generation sequencing (NGS) technology provides a high-throughput, rapid and accurate method to determine the precise sequence of nucleotides in DNA/RNA molecules. NGS, massively parallel or deep sequencing are related terms to describe the DNA sequencing technology that has revolutionized genomic research. 

Since the advent of modern sequencing technologies, the identification of nucleic acid sequences has become a ubiquitous and indispensable tool in all fields of biological sciences. Accordingly, the field of bioinformatics is central to the interpretation and application of these biological data. 

Using mathematical and statistical methods implemented by various programming languages, bioinformatics tools organize, analyze, and interpret biological information at the molecular, cellular, and genomic levels. The combination of NGS and bioinformatics is critical for diagnostic, medical, and epidemiological research. 

NGS, massively parallel or deep sequencing are related terms to describe the DNA sequencing technology that has revolutionized genomic research. The entire human genome can be sequenced in one day using NGS. By contrast, the Sanger sequencing technique previously used to decipher the human genome took more than a decade to provide a final draft. Although NGS has largely replaced traditional Sanger sequencing in genomic research, it has yet to translate into routine clinical practice. 

- Multi-omics Data for Deep Learning

High-throughput next-generation sequencing can now generate large amounts of multi-omics data for a variety of applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms underlying disease development. 

Recently, deep learning (DL) algorithms have emerged as one of the most promising approaches in multi-omics data analysis due to their ability to predict performance and capture nonlinear and hierarchical features. Although the integration and translation of multi-omics data into useful functional insights remains the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis into biomedical research to help explain complex relationships between molecular layers. 

Multi-omics data can help improve prevention, early detection, and prediction; monitor progression; interpret patterns and endotyping; and design individualized treatments.

- Barriers to Next Generation Sequencing (NGS)

The value of using NGS to inform oncology care decisions is increasingly apparent, but many challenges remain that may hinder the routine adoption of NGS in clinical care. NGS is becoming an increasingly larger part of genomics research, but the technical footprint and expertise involved make it difficult to implement in many laboratories.