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

Simmonds, S. E.; Ly, L.; Lim, R.; Morse, T.; Thakku, S. G.; Rosario Cora, K.; Caballero Perez, J.; Puschnik, A. S.; Mwakibete, L.; Hickey, S.; Beaulaurier, J.; Tato, C.; CZ ID Team, ; Kalantar, K.

2024-03-02 bioinformatics
10.1101/2024.02.29.579666 bioRxiv
Show abstract

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.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.