# Meta-analysis of public raw sequence data unveils the distribution and dynamics of emerging aquatic pathogens: using Macrobrachium rosenbergii golda virus as a case study

## TL;DR

This study uses a new database to analyze public sequencing data and reveals the global spread and biology of a prawn virus.

## Contribution

The study introduces a novel method using the Logan database to efficiently mine SRA data for emerging aquatic pathogens.

## Key findings

- MrGV is found in southern China, Thailand, and India, expanding its known geographic range.
- MrGV has been circulating in Southern Asian prawn populations since at least 2011.
- MrGV is predominantly associated with larval stages of Macrobrachium rosenbergii.

## Abstract

Searching publicly archived sequence data for emerging aquatic animal pathogens is a powerful but challenging approach for increasing our understanding of newly identified or poorly characterized organisms. However, searching for target sequences within the sequence read archive (SRA) database requires significant time, data storage, and computing power, limiting its accessibility. Utilizing a new database, Logan, we undertook a meta-analysis of SRA data sets to investigate the presence of an emerging virus, Macrobrachium rosenbergii golda virus (MrGV). MrGV was first characterized in M. rosenbergii larvae in 2020, associated with repeated mass mortalities in Bangladesh hatcheries. MrGV has since been detected in two separate reports from the Jiangsu Province of central, coastal China, and during a larval mortality event in India. Here, we discovered that MrGV is present in two additional provinces in southern China, Thailand, and India. We also found molecular evidence to confirm, as previously suspected, the circulation of the virus within Southern Asian populations of M. rosenbergii as far back as 2011, and that, based on relative abundance, MrGV is mostly associated with larvae. Overall, the identification of MrGV sequences in data sets that are largely unpublished within the scientific literature has provided novel insights into the pathogen’s biology, including the prevalence of MrGV globally and the life stages of prawns that should be screened to prevent the spread of the virus. This work illustrates how mining public sequencing data, supported by databases like Logan and standardized metadata submissions, can support cost-effective epidemiological studies of pathogens and strengthen One Health approaches to global disease monitoring.

Searching for target sequences within the sequence read archive (SRA) database requires significant time, data storage, and computing power, limiting its accessibility. This study demonstrates how the Logan database, constructed from an SRA-wide genome assembly, can be utilized to rapidly and efficiently find target sequences within the SRA database, expanding the use of these publicly available data sets outside of their original intended purposes. Here, we searched for an emerging virus, Macrobrachium rosenbergii golda virus, in prawns to reveal insights into its geographic distribution, host range, and relative abundance, without the need for additional sampling. We demonstrate how, with careful application of this approach, alongside improvements in metadata quality and accessibility, sequencing data sets can be used to uncover critical insights into pathogen biology. This type of data mining could add otherwise unknown data to epidemiological studies of emerging, re-emerging, and rare pathogens globally, allowing the determination of the spread of agents within and between populations.