The Scope of NGS Technology in Clinical Environment
Using a specific technology in clinical setting needs to be adequately decided and regulated in a bid to stop its misuse. To facilitate that, several official guidelines have been implemented which have been overseen and decided by several associations. These guidelines serve as a systematic procedure for experiments to proceed without any hindrance.
The application of NGS for clinical laboratories in the current scenario can either be a targeted analysis or a whole genome/exome/transcriptome sequencing analysis. In case of targeted genes, it deals with an approximate of a few thousand base cells, whereas whole genome sequencing (WGS) can manage the analysis of around
3.3 billion bases, and WES tackles around 40-50 million bases. The difference between targeted gene analysis and WES/WGS lies in the fact that the latter can identify mutations in the genome along with the unknown variants; in contrast targeted gene analysis works on detecting specific genes. Targeted gene analysis also provides an exon coverage which is greater than WGS and WES applications. For clinical purposes, targeted gene analysis is the preferred option. WGS and WES are mainly used for research studies (Qin, 2019). However, there are studies being conducted where the possibility of WES being regarded as a clinical analysis process is being explored, which can impact medical management and treatment (Niguidula et al., 2018). Interestingly, another candidate for NGS in clinical settings would be whole transcriptome sequencing. Though not yet quite common as compared to its other counterparts, the use of microarrays and RNA-sequencing (RNA-seq) are slowly being incorporated in clinical environments for identification of novel disease markers and diagnosis at a molecular level. When dealing with diseases like cancer, a lot of the information can be found in its genetic study. Microarrays and RNA-seq experiments allow for the same. The genes which are found to not perform their usual task and they appear as a result of some mutations are dubbed as dysregulated genes. The identification of these genes can often help us narrow down and explore important and novel biomarkers. Transcriptomic sequencing paired with transcrip- tomic analysis can open uncharted territories which have been made possible owing to the advancement in technology with the advent of robust and more powerful computational resources (Casamassimi et al., 2017). The data generated by microarray or RNA-seq experiments are compiled as raw data and then analyzed following distinct RNA-seq or microarray data analysis workflows coupled with machine learning algorithms, which point us in the direction of our desired objectivefCasamassimi et ah, 2017; Jabeen et ah, 2018). There have been several projects highlighting its use in cancer research studies ranging from identification of protein coding genes to detection and analysis of non-coding RNAs, all of which has seen much success. The effect of noncoding RNAs has been seen at an epigenetic level along with novel genetic mutations which results in oncogenesis and cancer progression. NGS-based research has made it possible and has provided an answer for diagnosis at a molecular level for a life-threatening disease like cancer and hopefully concentrate on providing precision treatment to patients (Figure 2.1).