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 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 Principles of Different X-ray Phase-Contrast Imaging: A Review

2021 ◽  
Vol 11 (7) ◽  
pp. 2971
Author(s):  
Siwei Tao ◽  
Congxiao He ◽  
Xiang Hao ◽  
Cuifang Kuang ◽  
Xu Liu
Keyword(s):  
Biological Samples ◽  
Phase Contrast ◽  
Recent Progress ◽  
Phase Contrast Imaging ◽  
Contrast Imaging ◽  
X Ray ◽  
X Ray Imaging ◽  
Internal Structures ◽  
X Ray Absorption ◽  
Made In

Numerous advances have been made in X-ray technology in recent years. X-ray imaging plays an important role in the nondestructive exploration of the internal structures of objects. However, the contrast of X-ray absorption images remains low, especially for materials with low atomic numbers, such as biological samples. X-ray phase-contrast images have an intrinsically higher contrast than absorption images. In this review, the principles, milestones, and recent progress of X-ray phase-contrast imaging methods are demonstrated. In addition, prospective applications are presented.

scholarly journals H3ABioNet genomic medicine and microbiome data portals hackathon proceedings

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Faisal M Fadlelmola ◽  
Kais Ghedira ◽  
Yosr Hamdi ◽  
Mariem Hanachi ◽  
Fouzia Radouani ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Genomic Medicine ◽  
Lessons Learned ◽  
Team Work ◽  
Significant Progress ◽  
Team Members ◽  
Pan African ◽  
Scientific Expertise ◽  
Microbiome Data ◽  
Made In

Abstract African genomic medicine and microbiome datasets are usually not well characterized in terms of their origin, making it difficult to find and extract data for specific African ethnic groups or even countries. The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for developing data portals for African genomic medicine and African microbiomes to address this and ran a hackathon to initiate their development. The two portals were designed and significant progress was made in their development during the hackathon. All the participants worked in a very synergistic and collaborative atmosphere in order to achieve the hackathon's goals. The participants were divided into content and technical teams and worked over a period of 6 days. In response to one of the survey questions of what the participants liked the most during the hackathon, 55% of the hackathon participants highlighted the familial and friendly atmosphere, the team work and the diversity of team members and their expertise. This paper describes the preparations for the portals hackathon and the interaction between the participants and reflects upon the lessons learned about its impact on successfully developing the two data portals as well as building scientific expertise of younger African researchers. Database URL: The code for developing the two portals was made publicly available in GitHub repositories: [https://github.com/codemeleon/Database; https://github.com/codemeleon/AfricanMicrobiomePortal].

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 Sex as a Prognostic Factor in Systematic Reviews: Challenges and Lessons Learned

2021 ◽  
Vol 11 (6) ◽  
pp. 441
Author(s):  
Elena Stallings ◽  
Alba Antequera ◽  
Jesús López-Alcalde ◽  
Miguel García-Martín ◽  
Gerard Urrútia ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Systematic Reviews ◽  
Lessons Learned ◽  
Disease State ◽  
Sex And Gender ◽  
Funding Agencies ◽  
And Gender ◽  
Baseline Factor ◽  
Clinical Domain ◽  
Made In

Sex is a common baseline factor collected in studies that has the potential to be a prognostic factor (PF) in several clinical areas. In recent years, research on sex as a PF has increased; however, this influx of new studies frequently shows conflicting results across the same treatment or disease state. Thus, systematic reviews (SRs) addressing sex as a PF may help us to better understand diseases and further personalize healthcare. We wrote this article to offer insights into the challenges we encountered when conducting SRs on sex as a PF and suggestions on how to overcome these obstacles, regardless of the clinical domain. When carrying out a PF SR with sex as the index factor, it is important to keep in mind the modifications that must be made in various SR stages, such as modifying the PF section of CHARMS-PF, adjusting certain sections of QUIPS and extracting data on the sex and gender terms used throughout the studies. In this paper, we provide an overview of the lessons learned from carrying out our reviews on sex as a PF in different disciplines and now call on researchers, funding agencies and journals to realize the importance of studying sex as a PF.

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 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.

Peer Review or Lottery? A Critical Analysis of Two Different Forms of Decision-making Mechanisms for Allocation of Research Grants

2019 ◽  
Vol 44 (6) ◽  
pp. 994-1019 ◽  
Author(s):  
Lambros Roumbanis
Keyword(s):  
Decision Making ◽  
Peer Review ◽  
Review Process ◽  
Selection Process ◽  
Peer Review Process ◽  
Dynamic Selection ◽  
Funding Agencies ◽  
Epistemic Diversity ◽  
Alternative Decision ◽  
Made In

At present, peer review is the most common method used by funding agencies to make decisions about resource allocation. But how reliable, efficient, and fair is it in practice? The ex ante evaluation of scientific novelty is a fundamentally uncertain endeavor; bias and chance are embedded in the final outcome. In the current study, I will examine some of the most central problems of peer review and highlight the possible benefits of using a lottery as an alternative decision-making mechanism. Lotteries are driven by chance, not reason. The argument made in the study is that the epistemic landscape could benefit in several respects by using a lottery, thus avoiding all types of bias, disagreement, and other limitations associated with the peer review process. Funding agencies could form a pool of funding applicants who have minimal qualification levels and then select randomly within that pool. The benefits of a lottery would not only be that it saves time and resources, but also that it contributes to a more dynamic selection process and increases the epistemic diversity, fairness, and impartiality within academia.

scholarly journals Advancing the Microbiome Research Community

Cell ◽  
2014 ◽  
Vol 159 (2) ◽  
pp. 227-230 ◽  
Author(s):  
Curtis Huttenhower ◽  
Rob Knight ◽  
C. Titus Brown ◽  
J. Gregory Caporaso ◽  
Jose C. Clemente ◽  
...  
Keyword(s):  
Research Community ◽  
Microbiome Research

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.

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