Next-Generation Sequencing Methodologies

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

“…82–85 The two most promising TGS technologies, single-molecule real-time (SMRT) sequencing of Pacific Biosciences (PacBio) and nanopore sequencing of Oxford Nanopore Technologies, offer distinct advantages and have been applied in single-cell transcriptome analysis. 86 In this section, we present the progress of single-cell transcriptome analysis using SMRT sequencing and nanopore sequencing while discussing their working principles, advantages, and disadvantages.…”

<strong></strong>Can AI-Powered Whole Genome Sequencing Be Used Routinely for Personalized Decision Support in Surgical Oncology? – A Scoping Review

“…While Sanger sequencing remained prominent for single or low-throughput DNA sequencing over three decades, challenges arose in speeding up the analysis for complex genomes, such as those of plant species, while still being expensive and time-consuming [9]. In contrast, Maxam-Gilbert sequencing, another first-generation method known as the chemical degradation method, relies on the chemical cleaving of nucleotides, which is particularly effective for small nucleotide polymers [10,11]. This method, performed without DNA cloning, generates marked fragments separated by electrophoresis.…”

Identification of single nucleotide genetic polymorphism sites using machine learning methods

“…Genetic processes are studied using genomic sequencing experiments, which observe information on the composition of DNA and RNA molecules and their coding fragment expressions [1]. Complete genome sequencing or sequencing of only functionally significant regions of the human genome allows simultaneously identifying multiple sites of single nucleotide polymorphism (SNP), having diagnostic or prognostic significance for many human diseases [2, 3].…”

“…Statistical methods of binomial distribution, entropy-based, Fisher’s exact tests and machine learning models are used for identifying the SNPs [2, 4]. These methods are quite universal and simple for program implementation, however, are computationally expensive and difficult to be effectively applied in the analysis of experimental data with a high noise level and various experimental distortions, which are sources of gaps, repetitions, and other anomalous values [1]. Practical experimental studies use simulation modelling to select the most optimal SNP identification algorithm, test competing pipelines of analysis, and evaluate the performance of specific experimental designs for studying biophysical systems [5].…”