Cecilia Van Cauwenberghe from Frost & Sullivan’s TechCasting Group, provides a portrait of a ground-breaking technology, next-generation sequencing, starting with a brief snapshot
Emerging technologies empowering next-generation sequencing (NGS) of DNA are evolving speedily. This evolution has significant implications for life science research and clinical practice (Adams and Eng, 2018). NGS today is significantly more time and cost-efficient than the former Sanger sequencing technology, hence improving sequencing output. In particular, short-read sequencing approaches, including sequencing by synthesis, ion semiconductor sequencing, and nano ball sequencing, have dramatically enhanced these outputs. Third-generation long-read sequencing is a step forward, allowing overcoming certain limitations of short-read sequencings, such as reliability to solve repeated sequences and large genomic rearrangements. The combination of adjacent technologies, such as nanotechnology –nanopore-, and complementary methods, including in situ nucleic acid sequencing, and microscopy-based sequencing, was crucial to driving massively parallel DNA sequencing (Kumar et al., 2019). This fact facilitated the introduction of NGS technologies to unveil complex biological contexts around disease mechanisms. Their utilisation for genetic diagnosis purposes is currently receiving extraordinary attention.
Unprecedented technology evolution across three prominent waves
The discovery of the DNA double helix structure in 1953 was the initial step for the emerging DNA sequencing methods in 1977 and automated sequencing, using polymerase chain reaction (PCR), in 1988. The broad impact of DNA sequencing in the advancements in the field of biology converged into a considerable effort to decode the human genome (the code of life) to the complete sequence of a clone or genome, with an accuracy level of at least 99.999%, with no gaps, since 1990 to 2003. This breakthrough innovation provided scientists with powerful insights into the conceptual foundations of biological and biomedical sciences. However, the implementation of DNA sequencing approaches using Sanger’s capillary electrophoresis method (first-generation sequencing) in large-scale projects was still labour-intensive, time-consuming, and costly.
Next-generation sequencing (NGS), also called second-generation sequencing or high throughput sequencing, involves two principal methods, sequencing by ligation (SBL) and sequencing by synthesis (SBS). These innovative technologies have enabled the sequencing of many samples in parallel at unprecedented speed and low cost. Indeed, during the last decade, the introduction of NGS technology exhibited impressive progress in life science research and clinical diagnostics (Azim et al., 2018). Today, the entire human genome is sequenced within a single day. A fundamental development in the NGS platforms has been paired-end (PE) sequencing. PE sequencing enables the DNA molecule to be read and scrutinised in both directions (forward and reverse) and generates twice as much evidence for the same expenditure and time from each mined genetic fragment. As a result, the entire sequencing process, from sample extraction, genetic library building, quantification, cluster generation, fragment sequencing, data analysis, and pipeline and data visualisation, was significantly improved.
The next wave consists of third-generation DNA sequencing technologies, which provide significant advantages over existing sequencing technologies, such as higher yield, faster turnaround time, longer read rates, lower cost (Lokuge and Ganegoda, 2018). Among the prevailing third-generation sequencing techniques, the most relevant are Single Molecule Sequencing (SMS), Single-stranded DNA Molecule Real-Time (SMRT), Zero Mode Waveguides (ZMW), and Nanopore Based Technologies.
Precision oncology as the main application field
NGS technologies have strongly influenced molecular diagnostics due to the dramatic diminution of costs through a high throughput approach in DNA sequencing. By enabling the rapid and accurate sequencing of many genes at once, DNA sequencing technology-based strategies have paved the way for the molecular prediction of human diseases with a precision medicine approach. Precision medicine pursues the use of genomic data to provide the correct treatment to the right patient at the right time. This trend is becoming more common in cancer therapy through precision oncology (Morash et al., 2018). NGS technologies have enabled the systematic analysis of whole-genome sequence (WGS) and whole-exome sequence (WES) of tumours. Indeed, NGS has empowered precision oncology by accelerating targeted genome profiles and transcriptome sequencing in diverse types of tumours, hence unveiling the existence of gene alterations and mutation carriers that may lead to cancer onset. In other words, NGS helps to draw a consistent molecular representation of a broad spectrum of cancers in a time and cost-effective manner (Azim et al., 2018).
Beyond cancer, NGS has emerged as one of the most powerful technologies for wide-scale detection of infection-causing pathogens, especially in the characterisation of those that cannot be grown using culture media and other conventional approaches (Kahn et al., 2020). This fact has led to its extensive use in clinical diagnostics, biopharmaceutical development, and vaccine production. A step further, an oriented analysis of NGS data unknots vital hints in the search for the treatment of life-threatening diseases (Tripathi et al., 2019).
Future directions based on data analysis and interpretation
Future directions in precision medicine consist of the empowerment of NGS technologies with parallel approaches, including artificial intelligence (AI), machine learning (ML) methods and deep learning (DL) methods. Whereas DL sequencing approaches allow parallel reading of numerous individual DNA segments, ML sequencing approaches facilitate assessing enormous sets of genomic information aiming to recognise new gene functions and gene regulation regions. New bioinformatics tools are developed to address current limitations in the treatment of genomic data and improve the overall performance of data analysis toward the acquisition of new knowledge around the interpretation of rare genomic variants (Yohe and Thyagarajan, 2017).
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