Abstract
Wastewater monitoring for pathogen detection has greatly advanced over the course of the COVID-19 pandemic. While most wastewater surveillance programs only target specific pathogens using qPCR or amplicon sequencing, untargeted wastewater metatranscriptomic sequencing (W-MTS) offers broader detection capabilities. However, there is a lack of data allowing the comparison of W-MTS with more established detection methods. Here we present a dataset consisting of 13.1 terabases (43B read pairs) of untargeted Illumina W-MTS data, generated from 20 wastewater samples, with 1.4B to 2.8B 150 bp read pairs per sample. Wastewater samples were collected between December 2023 and April 2024 at the Hyperion Water Reclamation Plant (HWRP), Los Angeles, USA, serving a population of approximately 4 million residents. The resulting dataset, one of the largest W-MTS collections to date, contains bacterial, archaeal, eukaryotic, and viral taxa—including human-infecting viruses—and many sequences of unknown origin. Uploaded to the NCBI Sequence Read Archive, we expect this data to spur additional research into the viability of pathogen-agnostic wastewater epidemiology and pathogen early detection.
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Data availability
All raw metatranscriptomic sequencing data generated in this study are deposited in the NCBI Sequence Read Archive (SRA) under BioProject PRJNA119800130. The dataset contains paired-end FASTQ files (2 × 150 bp) for each wastewater sample, organized into BioSamples SAMN45825509–SAMN45825528 and corresponding SRA Experiments SRX27073143–SRX2707316231. Metadata describing sample collection dates, sample type, and sequencing platform are included in each BioSample record. BioSample metadata follows the Genomic Standards Consortium’s “MIMS Environmental/Metagenome” metadata standard. The analysis results can be accessed in the following figshare repository: https://doi.org/10.6084/m9.figshare.28454990.v132.
Code availability
Sequencing data quality and taxonomic composition was assessed using a comprehensive computational pipeline, available under https://github.com/naobservatory/mgs-workflow/tree/2.5.1. The analysis results can be accessed in the following figshare repository: https://doi.org/10.6084/m9.figshare.28454990.v132. Code for figures and tables can be accessed under https://github.com/naobservatory/w-mgs-data-paper.
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Acknowledgements
S.L.G., J.T.K., W.J.B., K.L.W., and J.A.R. were funded for this research project by gifts from Open Philanthropy (to SecureBio). J.A.R was supported by an allocation (#BIO240238) from the National Science Foundation Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS) program. K.L.W. and J.A.R. would like to acknowledge earlier support for wastewater monitoring from the University of California Office of the President (award R00RG2814) and the Hewitt Foundation for Biomedical Research. We thank the City of Los Angeles and the Hyperion Wastewater Reclamation Plant for sampling assistance. We also thank Seung-Ah Chung and Melanie Oaks of the University of California Irvine Genomics Research and Technology Hub (GRT Hub), parts of which are supported by NIH grants to the Comprehensive Cancer Center (P30CA-062203) and the UCI Skin Biology Resource Based Center (P30AR075047) at the University of California, Irvine, as well as to the GRT Hub for instrumentation (1S10OD010794-01 and 1S10OD021718-01).
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J.T.K. and J.A.R. conceived the study; K.L., J.A.S. and J.F.G. collected wastewater samples; J.A.R. ran sequencing experiments; J.T.K. imported sequencing data, with processing and analysis by S.L.G. and W.J.B. S.L.G. wrote the manuscript, with feedback from all authors. K.L.W. provided study design, project management, and oversight along with manuscript edits.
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Grimm, S.L., Rothman, J.A., Bradshaw, W.J. et al. Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024. Sci Data (2025). https://doi.org/10.1038/s41597-025-06475-7
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DOI: https://doi.org/10.1038/s41597-025-06475-7
