scholarly journals Microbiome Data Science: Understanding Our Microbial Planet

2016 ◽  
Vol 24 (6) ◽  
pp. 425-427 ◽  
Author(s):  
Nikos C. Kyrpides ◽  
Emiley A. Eloe-Fadrosh ◽  
Natalia N. Ivanova
Keyword(s):  
Data Science ◽  
Science Understanding ◽  
Microbiome Data

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.

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

2019 ◽  
Vol 37 (8) ◽  
pp. 852-857 ◽  
Author(s):  
Evan Bolyen ◽  
Jai Ram Rideout ◽  
Matthew R. Dillon ◽  
Nicholas A. Bokulich ◽  
Christian C. Abnet ◽  
...  
Keyword(s):  
Data Science ◽  
Microbiome Data

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.

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

2019 ◽  
Vol 37 (9) ◽  
pp. 1091-1091 ◽  
Author(s):  
Evan Bolyen ◽  
Jai Ram Rideout ◽  
Matthew R. Dillon ◽  
Nicholas A. Bokulich ◽  
Christian C. Abnet ◽  
...  
Keyword(s):  
Data Science ◽  
Microbiome Data

scholarly journals Using data science for medical decision making case: role of gut microbiome in multiple sclerosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jasminka Hasic Telalovic ◽  
Azra Music
Keyword(s):  
Multiple Sclerosis ◽  
Gut Microbiome ◽  
Data Science ◽  
Classification Model ◽  
Medical Decision ◽  
Hierarchical Level ◽  
Kegg Pathways ◽  
Genus Level ◽  
Microbiome Data ◽  
Predictive Functions

Abstract Background A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data. Methods This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways. Results The highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant. Conclusions Our results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

scholarly journals Uncovering host-microbiome interactions in global systems with collaborative programming: a novel approach integrating social and data sciences

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1478
Author(s):  
Jenna Oberstaller ◽  
Swamy Rakesh Adapa ◽  
Guy W. Dayhoff II ◽  
Justin Gibbons ◽  
Thomas E. Keller ◽  
...  
Keyword(s):  
Data Science ◽  
South Florida ◽  
Technological Advancement ◽  
Real World Data ◽  
Domain Experts ◽  
Novel Approach ◽  
Microbiome Research ◽  
Data Output ◽  
Microbiome Data ◽  
Global Systems

Microbiome data are undergoing exponential growth powered by rapid technological advancement. As the scope and depth of microbiome research increases, cross-disciplinary research is urgently needed for interpreting and harnessing the unprecedented data output. However, conventional research settings pose challenges to much-needed interdisciplinary research efforts due to barriers in scientific terminologies, methodology and research-culture. To breach these barriers, our University of South Florida OneHealth Codeathon was designed to be an interactive, hands-on event that solves real-world data problems. The format brought together students, postdocs, faculty, researchers, and clinicians in a uniquely cross-disciplinary, team-focused setting. Teams were formed to encourage equitable distribution of diverse domain-experts and proficient programmers, with beginners to experts on each team. To unify the intellectual framework, we set the focus on the topics of microbiome interactions at different scales from clinical to environmental sciences, leveraging local expertise in the fields of genetics, genomics, clinical data, and social and geospatial sciences. As a result, teams developed working methods and pipelines to face major challenges in current microbiome research, including data integration, experimental power calculations, geospatial mapping, and machine-learning classifiers. This broad, transdisciplinary and efficient workflow will be an example for future workshops to deliver useful data-science products.

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.

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