Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024

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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-12-24
DOI
https://doi.org/10.1038/s41597-025-06475-7
Citations
1
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
1.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024

John F. Griffith, William J. Bradshaw, Joshua A. Steele, Kylie Langlois et al.
1 citations
Scientific Data
SARS-CoV-2 detection and testing
1.40
article

Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024

John F. Griffith, William J. Bradshaw, Joshua A. Steele, Kylie Langlois, Jason A. Rothman, Katrine Whiteson, Simon Grimm, Jeff T. Kaufman
article en
1 citations

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.

Scientific DataVol. 13(1)
University of California, Riverside (US), Southern California Coastal Water Research Project (US), University of California, Irvine (US), European Biomass Association (BE), Massachusetts Institute of Technology (US)
National Science Foundation, Open Philanthropy Project, National Institutes of Health, University of California, Irvine, Office of the President, University of California, Office of Advanced Cyberinfrastructure
Clean water and sanitation
Openalex Percentile: Top 13%
SARS-CoV-2 detection and testing
1.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.