A fast machine-learning-guided primer design pipeline for selective whole genome amplification

Nucleic acid detection with single-base specificity integrating isothermal amplification and light-up aptamer probes

We report a novel platform for label-free nucleic acid detection using isothermal amplification and light-up aptamer probes. This assay converts double-stranded amplicons into single-stranded targets to enable sequence-specific hybridization with split dapoxyl aptamer probes, offering attomolar sensitivity and single-base specificity.

A novel isothermal whole genome sequencing approach for Monkeypox Virus

Monkeypox virus (MPXV) is the zoonotic agent responsible for mpox, an often-self-limiting pox-like disease. Since May 2022, an outbreak characterized by increased human-to-human transmission was detected outside the endemic regions. Whole genome sequencing (WGS) has been successfully used to keep track of viral evolution during outbreaks or for surveillance of multiple pathogens of public health significance. Current WGS protocols for MPXV are either based on metagenomic sequencing or tiled-PCR amplification. The latter allows multiplexing due to the efficient enrichment of the viral DNA, however, mutations or the presence of different clades can negatively influence genome coverage yield. Here, we present the establishment of a novel isothermal WGS method for MPXV based on Phi29 DNA polymerase-based multiple displacement amplification (MDA) properties making use of only 6 primers. This approach yielded from 88% up to 100% genome coverage using either alkaline denatured extracted DNA or clinical material as starting material, with the highest coverage generated by clinical material. We demonstrate that this novel isothermal WGS protocol is suitable for monitoring viral evolution during MPXV outbreaks and surveillance in any conventional laboratory setting.

Turning the needle into the haystack: Culture-independent amplification of complex microbial genomes directly from their native environment

High-throughput sequencing (HTS) has revolutionized microbiology, but many microbes exist at low abundance in their natural environment and/or are difficult, if not impossible, to culture in the laboratory. This makes it challenging to use HTS to study the genomes of many important microbes and pathogens. In this review, we discuss the development and application of selective whole genome amplification (SWGA) to allow whole or partial genomes to be sequenced for low abundance microbes directly from complex biological samples. We highlight ways in which genomic data generated by SWGA have been used to elucidate the population dynamics of important human pathogens and monitor development of antimicrobial resistance and the emergence of potential outbreaks. We also describe the limitations of this method and propose some potential innovations that could be used to improve the quality of SWGA and lower the barriers to using this method across a wider range of infectious pathogens.

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction

BACKGROUND

DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity.

RESULTS

In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation.

CONCLUSIONS

Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.

MultiPrime: A reliable and efficient tool for targeted next-generation sequencing

We present multiPrime, a novel tool that automatically designs minimal primer sets for targeted next-generation sequencing, tailored to specific microbiomes or genes. MultiPrime enhances primer coverage by designing primers with mismatch tolerance and ensures both high compatibility and specificity. We evaluated the performance of multiPrime using a data set of 43,016 sequences from eight viruses. Our results demonstrated that multiPrime outperformed conventional tools, and the primer set designed by multiPrime successfully amplified the target amplicons. Furthermore, we expanded the application of multiPrime to 30 types of viruses and validated the work efficacy of multiPrime-designed primers in 80 clinical specimens. The subsequent sequencing outcomes from these primers indicated a sensitivity of 94% and a specificity of 89%.