scholarly journals Community-Driven Metadata Standards for Agricultural Microbiome Research

2020 ◽  
Vol 4 (2) ◽  
pp. 115-121 ◽  
Author(s):  
J. P. Dundore-Arias ◽  
E. A. Eloe-Fadrosh ◽  
L. M. Schriml ◽  
G. A. Beattie ◽  
F. P. Brennan ◽  
...  
Keyword(s):  
Crop Management ◽  
Crop Productivity ◽  
Field Sampling ◽  
Minimum Information ◽  
Metadata Standards ◽  
Starting Point ◽  
Microbiome Research ◽  
Widespread Availability ◽  
Microbiome Data ◽  
Data Variation

Accelerating the pace of microbiome science to enhance crop productivity and agroecosystem health will require transdisciplinary studies, comparisons among datasets, and synthetic analyses of research from diverse crop management contexts. However, despite the widespread availability of crop-associated microbiome data, variation in field sampling and laboratory processing methodologies, as well as metadata collection and reporting, significantly constrains the potential for integrative and comparative analyses. Here we discuss the need for agriculture-specific metadata standards for microbiome research, and propose a list of “required” and “desirable” metadata categories and ontologies essential to be included in a future minimum information metadata standards checklist for describing agricultural microbiome studies. We begin by briefly reviewing existing metadata standards relevant to agricultural microbiome research, and describe ongoing efforts to enhance the potential for integration of data across research studies. Our goal is not to delineate a fixed list of metadata requirements. Instead, we hope to advance the field by providing a starting point for discussion, and inspire researchers to adopt standardized procedures for collecting and reporting consistent and well-annotated metadata for agricultural microbiome research.

scholarly journals Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative’s Workshop and Follow-On Activities

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Pajau Vangay ◽  
Josephine Burgin ◽  
Anjanette Johnston ◽  
Kristen L. Beck ◽  
Daniel C. Berrios ◽  
...  
Keyword(s):  
Biological Samples ◽  
Research Community ◽  
Barriers To Adoption ◽  
Funding Agencies ◽  
Metadata Standards ◽  
Microbiome Research ◽  
Working Groups ◽  
Microbiome Data ◽  
Research Domains ◽  
Made In

ABSTRACT Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

scholarly journals Streaming histogram sketching for rapid microbiome analytics

2018 ◽  
Author(s):  
Will P. M. Rowe ◽  
Anna Paola Carrieri ◽  
Cristina Alcon-Giner ◽  
Shabhonam Caim ◽  
Alex Shaw ◽  
...  
Keyword(s):  
Locality Sensitive Hashing ◽  
Genomic Research ◽  
Compact Representation ◽  
Sample Type ◽  
Sequencing Data ◽  
Similarity Estimation ◽  
Microbiome Research ◽  
Microbiome Data ◽  
Similarity Searches

AbstractMotivationThe growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research; allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time.To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching, and classification of microbiome samples in near real-time.ResultsWe apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can be used to efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we show that histosketches can be used to train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a Random Forest Classifier that could accurately predict whether the neonate had received antibiotic treatment (95% accuracy, precision 97%) and could subsequently be used to classify microbiome data streams in less than 12 seconds.We provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2GB microbiome in 50 seconds on a standard laptop using 4 cores, with the sketch occupying 3000 bytes of disk space.AvailabilityOur implementation (HULK) is written in Go and is available at: https://github.com/will-rowe/hulk (MIT License)

scholarly journals All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019)

2019 ◽  
Author(s):  
Michael David Wilson ◽  
Russell Boag ◽  
Luke Joseph Gough Strickland
Keyword(s):  
Gold Standard ◽  
Cognitive Modeling ◽  
Best Practice ◽  
Cognitive Model ◽  
Cognitive Models ◽  
Reproducible Research ◽  
Minimum Information ◽  
Starting Point ◽  
Bare Minimum ◽  
Practical Recommendations

Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.

scholarly journals QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

2018 ◽  
Author(s):  
Evan Bolyen ◽  
Jai Ram Rideout ◽  
Matthew R Dillon ◽  
Nicholas A Bokulich ◽  
Christian Abnet ◽  
...  
Keyword(s):  
Data Science ◽  
Temporal Analysis ◽  
Data Provenance ◽  
Policy Makers ◽  
Shotgun Metagenomics ◽  
Spatial And Temporal Analysis ◽  
Microbiome Research ◽  
Scientists And Engineers ◽  
Visualization Tools ◽  
Microbiome Data

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Extending Metadata Standards for Historical GIS Research

2016 ◽  
pp. 1243-1265
Author(s):  
Shelley Burleson ◽  
Alberto Giordano
Keyword(s):  
Geographical Information Systems ◽  
Historical Data ◽  
Geographical Information ◽  
Historical Sources ◽  
Historical Gis ◽  
First Case ◽  
Metadata Standards ◽  
Starting Point ◽  
The Holocaust ◽  
Definitive Solution

This chapter proposes a structure for handling commonly observed uncertainties in geo-historical data, using as case studies two historical geographical information systems (HGIS) projects that interweave historical research with the geography of genocide. The first case involves the ghettoization of Budapest's Jews during the Holocaust in the second half of 1944. The more recent work, and the second case, covers the Armenian genocide spanning most of WWI and several years afterwards. The authors suggest using existing metadata standards as one way of handling the inherent uncertainties of geo-historical sources. While not a definitive solution, they argue that such an approach provides a starting point and a platform to conceptually frame the use of geo-historical data in HGIS.

scholarly journals Crop Management System Based on Digital Technologies

2021 ◽  
Vol 37 ◽  
pp. 00188
Author(s):  
A.R. Zakirova ◽  
G.S. Klychova ◽  
A.R. Yusupova ◽  
I.G. Nikitenko ◽  
A.M. Zakirov
Keyword(s):  
Management System ◽  
Production Systems ◽  
Plant Protection ◽  
Crop Management ◽  
Plant Protection Products ◽  
Crop Productivity ◽  
Unmanned Vehicles ◽  
Human Potential ◽  
Material Resources ◽  
Crop Farming

Crop farming is one of the most important branches of agriculture that ensures the food security of the country. For crop farming development, it is necessary to solve the problems of reducing the quantity and quality of the resulting crop due to insufficient nutrition and care, untimely harvesting and improper storage. To increase the efficiency and sustainability of the industry, it is necessary to digitalize both the production systems and decision-making processes at all levels of management. The study considers the stages of a digital crop management system: accounting for acreage with an electronic field map; organization of crop rotation taking into account organizational, soil-climatic and economic aspects of production; monitoring of technological operations; control of agricultural machinery with the formation of primary documents for accounting for completed works; maintaining a subsystem of material resources using the technic of precision farming; filling in technological maps and accounting for actually completed works with a list of works, the composition of agricultural aggregates, the timing of operations, production rates and fuel consumption, the need for seeds, fertilizers, plant protection products, and the cost of resources. The increased attention to the problem of digital agriculture on the part of the state and the highly competitive environment promote the use of new technological mechanisms and methods of work by agricultural producers, which are based on the use of Internet technologies, satellite navigation, robotics, sensors and sensors, and unmanned vehicles. Digitalization of agricultural processes allows increasing crop productivity, the efficiency of using material resources, equipment and human potential.

scholarly journals Modernization of Agrarian Economy in the Republic of Kazakhstan

2021 ◽  
Vol 16 (3) ◽  
pp. 35-50
Author(s):  
G. K. Kurmanova ◽  
B. B. Sukhanberdina ◽  
B. A. Urazova
Keyword(s):  
Agricultural Sector ◽  
Crop Productivity ◽  
Farm Animals ◽  
Modern Concept ◽  
Scientific Article ◽  
Agricultural Economy ◽  
Agrarian Economy ◽  
Starting Point ◽  
New Development ◽  
The Republic

In this article we study what the concept of «modernization» in the agricultural economy entails. Thanks to modernization, the agricultural economy is becoming more efficient based on a new development model. The analysis of the modernization of the agricultural economy is important for understanding the development of the country. And if, in general, the process of modernization in modern Kazakhstan is quite actively studied by specialists, then the chosen aspect-the modernization of the agricultural economy-is poorly studied in domestic science. The study of the agricultural sector in the context of modernization allows us to substantiate conclusions and practical proposals for improving the dynamic development of agriculture and achieving a new quality of life for the rural population. The concept of «modernization of the agricultural economy» is clarified as a multi-faceted process of complex innovations carried out by subjects of both the agricultural economy and industrial subjects, which ensures the transition to a new level of crop productivity and productivity of farm animals. Modernization of the agricultural economy is identical to economic investment. The financial resources in the process of modernization providing and generating are investments. The research logic reflects the author’s view of the modern concept of modernization in the agrarian economy, considering the integration between development and modernization into a new format. The starting point of this scientific article is the determination of the role and place of modernization in agriculture to ensure food security and independence of the Republic of Kazakhstan.

scholarly journals Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

2022 ◽  
Vol 1 ◽  
Author(s):  
Bin Hu ◽  
Shane Canon ◽  
Emiley A. Eloe-Fadrosh ◽  
Anubhav ◽  
Michal Babinski ◽  
...  
Keyword(s):  
Open Access ◽  
Omics Data ◽  
Metabolomic Data ◽  
Or Groups ◽  
Microbiome Research ◽  
Descriptive Approach ◽  
Dynamics And Function ◽  
And Function ◽  
Microbiome Data ◽  
Bioinformatics Workflows

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

scholarly journals SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

2021 ◽  
Author(s):  
Giulia Agostinetto ◽  
Davide Bozzi ◽  
Danilo Porro ◽  
Maurizio Casiraghi ◽  
Massimo Labra ◽  
...  
Keyword(s):  
Data Science ◽  
Data Retrieval ◽  
Amplicon Sequencing ◽  
Full Potential ◽  
Skin Microbiome ◽  
Data Accessibility ◽  
16S Rrna Amplicon Sequencing ◽  
Comprehensive Collection ◽  
Microbiome Research ◽  
Microbiome Data

Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, urges the need for data accessibility and reusability, according to FAIR (Findable, Accessible, Interoperable, and Reusable) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analysis, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field.

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