A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Abstract

Background. Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies. Objective. The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now. Results. The proposed study, based on previous studies and manufacturers’ descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position. Conclusion. The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Mega-scale experimental analysis of protein folding stability in biology and protein design

“…Breakthroughs in deep learning-powered structure prediction have proven the power of these models in protein science, but collecting sufficient thermodynamic data has always been a major obstacle. Due to the scale and efficiency of cDNA display proteolysis, the main limit to measuring stability for millions of small domains is the cost of DNA synthesis (Kosuri and Church, 2014; Kuiper et al, 2022; Sun et al, 2016) and sequencing (Foox et al, 2021; Goodwin et al, 2016) - both of which are rapidly decreasing (Hughes and Ellington, 2017; Levy and Boone, 2019; Pervez et al, 2022; Song et al, 2021). With the flexibility of DNA oligo synthesis, cDNA display proteolysis can assay massive mutational libraries (as shown here) as well as massive libraries of unrelated sequences and structures, which will add essential diversity in training datasets.…”

Analysis of the Toll and Spaetzle Genes Involved in Toll Pathway-Dependent Antimicrobial Gene Induction in the Red Flour Beetle, Tribolium castaneum (Coleoptera; Tenebrionidae)

“…However, we presumed that there might be more spz genes in the T. castaneum genome. Owing to advances in sequencing devices, more accurate genome data can be constructed [ 34 ]. For example, the first genome sequence data of Apis mellifera (Hymenoptera; Apidae, Linnaeus, 1758) were published in 2006, and after several updates, the latest version of the A. mellifera genome is at chromosome level with very high continuity [ 35 , 36 , 37 ].…”

Analysis of the Toll and spaetzle genes involved in Toll pathway-dependent antimicrobial gene induction in the red flour beetle,Tribolium castaneum(Coleoptera: Tenebriodae)

“…However, we presumed that there might be more spz genes in the T. castaneum genome. Owing to advances in sequencing devices, more accurate genome data can be constructed [34]. For example, the first genome sequence data of Apis mellifera were published in 2006, and after several updates, the latest version of the A. mellifera genome is at chromosome level, with very high continuity [35– 37].…”