Multiplex pyrosequencing quantitative detection combined with universal primer-multiplex-PCR for genetically modified organisms

A rapid and high-throughput system for the detection of transgenic products based on LAMP-CRISPR-Cas12a

With the increasing acreage of genetically modified crops worldwide, rapid and efficient detection technologies have become very important for the regulation and screening of GM organisms. We constructed a method based on loop-mediated isothermal amplification (LAMP), CRISPR-Cas12a and lateral flow assay (LAMP-CRISPR-Cas12a-LFA). It is an intuitive, sensitive and specific fluorescence detection and test strip system to detect CP4-EPSPS and Cry1Ab/Ac genes in field screening. The LAMP-CRISPR-Cas12a-LFA method has a limit of detection (LOD) of 100 copies based on lateral flow test strips after optimization of the conditions with screened specific primers, and the entire detection process can be completed within 1 h at 61 °C. The system was used to evaluate field test samples and showed high reproducibility after testing products containing CP4-EPSPS and Cry1Ab/Ac genes, and both were detectable. The LAMP-CRISPR-Cas12a-LFA method established in this paper functions as a rapid field detection method. It requires only one portable thermostatic instrument, which renders it compatible with the rapid detection of field samples and useable at experimental workstations, in law enforcement field work, and in local inspection and quarantine departments.

Advances in Electrochemical Techniques for the Detection and Analysis of Genetically Modified Organisms: An Analysis Based on Bibliometrics

Since the first successful transgenic plants obtained in 1983, dozens of plants have been tested. On the one hand, genetically modified plants solve the problems of agricultural production. However, due to exogenous genes of transgenic plants, such as its seeds or pollen drift, diffusion between populations will likely lead to superweeds or affect the original traits. The detection technology of transgenic plants and their products have received considerable attention. Electrochemical sensing technology is a fast, low-cost, and portable analysis technology. This review interprets the application of electrochemical technology in the analysis and detection of transgenic products through bibliometrics. A total of 83 research articles were analyzed, spanning 2001 to 2021. We described the different stages in the development history of the subject and the contributions of countries and institutions to the topic. Although there were more annual publications in some years, there was no explosive growth in any period. The lack of breakthroughs in this technology is a significant factor in the lack of experts from other fields cross-examining the subject. Through keyword co-occurrence analysis, different research directions on this topic were discussed. The use of nanomaterials with excellent electrical conductivity allows for more sensitive detection of GM crops by electrochemical sensors. Furthermore, co-citation analysis was used to interpret the most popular reports on the topic. In the end, we predict the future development of this topic according to the analysis results.

Visualization of copper nanoclusters for SARS-CoV-2 Delta variant detection based on rational primers design

Here, based on the design of rational primers and copper nanoclusters (CuNCs), we present a method for the accurate detection of the SARS-CoV-2 Delta variant, which is capable of distinguishing the Delta variant with its single nucleotide polymorphism from the 'wild type' coronavirus (NC_045512.2), and realizing visualization signal out. Specifically, we show that dual priming oligonucleotide (DPO) primers and AT primers can be used to distinguish between wild types and mutations of this virus by polymerase chain reaction (PCR) analysis and that visualization can be achieved via the red fluorescence of CuNCs in ultraviolet radiation. Among the results, it was found that the R-1-down (DPO)-6I and F-1-30 AT, with the single nucleotide deletion site designed at the 3' end of the downstream primer, showed the best specificity towards the Delta variant. Moreover, the use of AT primers increased the AT contents of the PCR products, thus meeting the template requirements generated by the CuNCs. It was also found that the AT primers could assist with improving detection specificity. Finally, we demonstrate that the visualization of the CuNCs-based detection assay exhibited a linear relationship in 0.5 pg μL^-1-50 ng μL^-1, with a limit of quantitation (LOQ) of 0.5 pg μL^-1.

Genetically Modified Soybean Detection Using a Biosensor Electrode with a Self-Assembled Monolayer of Gold Nanoparticles

In this study, we proposed a genosensor that can qualitatively and quantitatively detect genetically modified soybeans using a simple electrode with evenly distributed single layer gold nanoparticles. The DNA sensing electrode is made by sputtering a gold film on the substrate, and then sequentially depositing 1,6-hexanedithiol and gold nanoparticles with sulfur groups on the substrate. Then, the complementary to the CaMV 35S promoter (P35S) was used as the capture probe. The target DNA directly extracted from the genetically modified soybeans rather than the synthesized DNA segments was used to construct the detection standard curve. The experimental results showed that our genosensor could directly detect genetically modified genes extracted from soybeans. We obtained two percentage calibration curves. The calibration curve corresponding to the lower percentage range (1-6%) exhibits a sensitivity of 2.36 Ω/% with R2 = 0.9983, while the calibration curve corresponding to the higher percentage range (6-40%) possesses a sensitivity of 0.1 Ω/% with R2 = 0.9928. The limit of detection would be 1%. The recovery rates for the 4% and 5.7% GMS DNA were measured to be 104.1% and 102.49% with RSD at 6.24% and 2.54%. The gold nanoparticle sensing electrode developed in this research is suitable for qualitative and quantitative detection of genetically modified soybeans and can be further applied to the detection of other genetically modified crops in the future.

Hardware Acceleration of Genomics Data Analysis: Challenges and Opportunities

The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.