Thakku, S. G. | Search bioRxiv

bioRxiv2024

CZ ID: a cloud-based, no-code platform enabling advanced long read metagenomic analysis

Metagenomics has enabled the rapid, unbiased detection of microbes across diverse sample types, leading to exciting discoveries in infectious disease, microbiome, and viral research. However, the analysis of metagenomic data is often complex and computationally resource-intensive. CZ ID is a free, cloud-based genomic analysis platform that enables researchers to detect microbes using metagenomic data, identify antimicrobial resistance genes, and generate viral consensus genomes. With CZ ID, researchers can upload raw sequencing data, find matches in NCBI databases, get per-sample taxon metrics, and perform a variety of analyses and data visualizations. The intuitive interface and interactive visualizations make exploring and interpreting results simple. Here, we describe the expansion of CZ ID with a new long read mNGS pipeline that accepts Oxford Nanopore generated data (czid.org). We report benchmarking of a standard mock microbial community dataset against Kraken2, a widely used tool for metagenomic analysis. We evaluated the ability of this new pipeline to detect divergent viruses using simulated datasets. We also assessed the detection limit of a spiked-in virus to a cell line as a proxy for clinical samples. Lastly, we detected known and novel viruses in previously characterized disease vector (mosquitoes) samples.

bioinformatics↗

bioRxiv2021

Multiplexed detection of bacterial nucleic acids using Cas13 in droplet microarrays

Rapid and accurate diagnosis of infections is fundamental to individual patient care and public health management. Nucleic acid detection methods are critical to this effort, but are limited either in the breadth of pathogens targeted or by the expertise and infrastructure required. We present here a high-throughput system that enables rapid identification of bacterial pathogens, bCARMEN, which utilizes: (1) modular CRISPR-Cas13-based nucleic acid detection with enhanced sensitivity and specificity; and (2) a droplet microfluidic system that enables thousands of simultaneous, spatially multiplexed detection reactions at nanoliter volumes; and (3) a novel pre-amplification strategy that further enhances sensitivity and specificity. We demonstrate bCARMEN is capable of detecting and discriminating 52 clinically relevant bacterial species and several key antibiotic resistance genes. We further develop a proof of principle system for use with stabilized reagents and a simple workflow with optical readout using a cell phone camera, opening up the possibility of a rapid point-of-care multiplexed bacterial pathogen identification and antibiotic susceptibility testing. Significance StatementIn this paper, we use a novel primer design method combined with droplet-based CRISPR Cas13 detection to distinguish 52 clinically relevant bacterial pathogens in a single assay. We also apply the method to detect and distinguish a panel of major antibiotic resistance genes, which is of critical importance in this era of rising antibiotic resistance. Finally, we make key advances towards making our diagnostic assay deployable at the point-of-care, with a simplified emulsion-free assay process that uses mobile phone camera for detection and reduces infrastructure/skilled labor requirements.

microbiology↗