AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata

Liang, Xiao; Akers, Kyle; Keenum, Ishi; Wind, Lauren; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S.

doi:10.1186/s12859-021-04035-5

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Although it is not surprising that the resistome characterized from each environmental sample type (amendment, soil, lettuce surface) was different, the magnitude of these differences was surprising, and suggests a need for future efforts to examine these inconsistencies at a more robust biological level. Overall the differences in sequencing platforms, coverage and assembly across the pre-harvest vegetable production chain points should be acknowledged and accounted for in future when comparing critical control points and environmental studies (Liang et al, 2021).…”

Section: Limitationsmentioning

confidence: 99%

Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

et al. 2021

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An integrated understanding of factors influencing the occurrence, distribution, and fate of antibiotic resistance genes (ARGs) in vegetable production systems is needed to inform the design and development of strategies for mitigating the potential for antibiotic resistance propagation in the food chain. The goal of the present study was to holistically track antibiotic resistance and associated microbiomes at three distinct pre-harvest control points in an agroecosystem in order to identify the potential impacts of key agricultural management strategies. Samples were collected over the course of a single growing season (67 days) from field-scale plots amended with various organic and inorganic amendments at agronomic rates. Dairy-derived manure and compost amendment samples (n = 14), soil samples (n = 27), and lettuce samples (n = 12) were analyzed via shotgun metagenomics to assess multiple pre-harvest factors as hypothetical control points that shape lettuce resistomes. Pre-harvest factors of interest included manure collection during/post antibiotic use, manure composting, and soil amended with organic (stockpiled manure/compost) versus chemical fertilizer. Microbial community resistome and taxonomic compositions were unique from amendment to soil to lettuce surface according to dissimilarity analysis. The highest resistome alpha diversity (i.e., unique ARGs, n = 642) was detected in amendment samples prior to soil application, while the composted manure had the lowest total ARG relative abundance (i.e., 16S rRNA gene-normalized). Regardless of amendment type, soils acted as an apparent ecological buffer, i.e., soil resistome and taxonomic profiles returned to background conditions 67 d-post amendment application. Effects of amendment conditions surprisingly re-emerged in lettuce phyllosphere resistomes, with the highest total ARG relative abundances recovered on the surface of lettuce plants grown in organically-fertilized soils (i.e., compost- and manure-amended soils). Co-occurrence analysis identified 55 unique ARGs found both in the soil amendments and on lettuce surfaces. Among these, arnA and pmrF were the most abundant ARGs co-occurring with mobile genetic elements (MGE). Other prominent ARG-MGE co-occurrences throughout this pre-harvest lettuce production chain included: TetM to transposon (Clostridiodies difficile) in the manure amendment and TriC to plasmid (Ralstonia solanacearum) on the lettuce surfaces. This suggests that, even with imposing manure management and post-amendment wait periods in agricultural systems, ARGs originating from manure can still be found on crop surfaces. This study demonstrates a comprehensive approach to identifying key control points for the propagation of ARGs in vegetable production systems, identifying potential ARG-MGE combinations that could inform future surveillance. The findings suggest that additional pre-harvest and potentially post-harvest interventions may be warranted to minimize risk of propagating antibiotic resistance in the food chain.

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Section: Limitationsmentioning

confidence: 99%

Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

et al. 2021

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“…The ARGem pipeline consists of multiple computational components arranged primarily in a linear sequence, with built-in detection of certain error cases that serve to halt the pipeline early and send out an email notification of the error. We integrated the ARGem pipeline as a key component in the web-based platform AgroSeek ( Liang et al, 2021 ). ARGem can also be deployed in other systems that incorporates a relational database management system, as detailed in Section Relational Database .…”

Section: Resultsmentioning

confidence: 99%

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

Liang,

Zhang,

Kim

et al. 2023

Front. Genet.

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Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user’s configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

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“…It can break through the geographical restrictions of interconnection and noninterconnection, and local ISPs, so as to achieve network connection on a global scale and expand IDC resources. It can rationally develop through CDN at central nodes across the country, and distribute storage to the edge to achieve network coverage [23].…”

Section: A Iot Related Technologies For Agricultural Resource Managementmentioning

confidence: 99%

The Application of Agricultural Resource Management Information System Based on Internet of Things and Data Mining

Gao¹

2021

IEEE Access

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To promote the progression of information management of agricultural resources in China, the present situation of agricultural management model in China is introduced, and an information agricultural tracking model based on Internet of Things (IoT) is proposed according to the characteristics of production flow of agricultural products. Then, the progression level of agricultural mechanization in China is analyzed based on data mining, and some suggestions are given. The research results show that in the four links of production, processing, transportation, and sales, the agricultural products tracking system in this study can simultaneously deal with more than 800 events per second when conducting information query. When the number of users reaches 20, the response time is 0.021 seconds. When processing messages sent by 100 users simultaneously, the response time is only about 0.12 seconds. When 160 users are sent to the system at the same time, the response time is less than 0.2 seconds. As the number of users sending messages at the same time increases, the response time remains within 0.3 seconds, although delayed. This system can effectively complete the agricultural commodity information tracking, with good performance, query speed, and high accuracy. The agricultural products tracking model in this study is very powerful to complete the information tracking of agricultural commodities, with good performance, fast query speed, and high accuracy. In summary, IoT and data mining are applied to agricultural resource information management, and the analysis and experiment are carried out. The results provide some ideas and references for the research of related technologies in agricultural management and progression.

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AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata

Cited by 6 publications

References 22 publications

Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

The Application of Agricultural Resource Management Information System Based on Internet of Things and Data Mining

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