scholarly journals Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 601
Author(s):  
Justin Wagner ◽  
Jayaram Kancherla ◽  
Domenick Braccia ◽  
James Matsumara ◽  
Victor Felix ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Disease Status ◽  
Human Microbiome Project ◽  
Second Phase ◽  
Bioconductor Package ◽  
The Rich ◽  
Rich Data ◽  
Project Data ◽  
Microbiome Data

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

scholarly journals Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 601
Author(s):  
Justin Wagner ◽  
Jayaram Kancherla ◽  
Domenick Braccia ◽  
James Matsumara ◽  
Victor Felix ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Disease Status ◽  
Human Microbiome Project ◽  
Second Phase ◽  
Bioconductor Package ◽  
The Rich ◽  
Rich Data ◽  
Project Data ◽  
Microbiome Data

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

Human Microbiome Project, Data Analysis, and Coordination Center

2015 ◽  
pp. 229-230
Author(s):  
Olukemi O. Abolude ◽  
Heather H. Creasy ◽  
Anup A. Mahurkar ◽  
Owen White ◽  
Michelle G. Giglio
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Coordination Center ◽  
Project Data

Human Microbiome Project, Data Analysis, and Coordination Center

2013 ◽  
pp. 1-2
Author(s):  
Olukemi O. Abolude ◽  
Heather H. Creasy ◽  
Anup A. Mahurkar ◽  
Owen White ◽  
Michelle G. Giglio
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Coordination Center ◽  
Project Data

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

scholarly journals phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

2019 ◽  
Vol 36 (4) ◽  
pp. 1289-1290
Author(s):  
Patrick H Bradley ◽  
Katherine S Pollard
Keyword(s):  
Community Composition ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Shotgun Sequencing ◽  
Supplementary Information ◽  
Phylogenetic Comparative Methods ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Phylogenetic Regression ◽  
Project Data

Abstract Summary Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A data analysis and coordination center for the human microbiome project

2010 ◽  
Vol 11 (Suppl 1) ◽  
pp. O13 ◽  
Author(s):  
Jennifer Wortman ◽  
Michelle Giglio ◽  
Heather Creasy ◽  
Amy Chen ◽  
Konstantinos Liolios ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Coordination Center

scholarly journals THE IMPACTS OF THE EU ETS ON NORWEGIAN PLANTS’ ENVIRONMENTAL AND ECONOMIC PERFORMANCE

2020 ◽  
Vol 11 (01) ◽  
pp. 2050006
Author(s):  
MARIT KLEMETSEN ◽  
KNUT EINAR ROSENDAHL ◽  
ANJA LUND JAKOBSEN
Keyword(s):  
Economic Performance ◽  
Value Added ◽  
Second Phase ◽  
Trading System ◽  
Marginal Costs ◽  
Plant Behavior ◽  
Positive Effects ◽  
The Rich ◽  
Rich Data ◽  
The Eu

This paper examines the impacts of the EU Emissions Trading System (ETS) on the environmental and economic performance of Norwegian plants. The ETS is regarded as the cornerstone climate policy in the EU and Norway, but there has been considerable debate regarding its effects due to low quota prices and substantial allocation of free allowances. The rich data allow us to investigate potential effects of the ETS on several important aspects of plant behavior. The results indicate a weak tendency of emissions reductions among Norwegian plants in the second phase of the ETS, but not in the other phases. We find no significant effects on emissions intensity in any of the phases, but positive effects on value added and productivity in the second phase. These positive effects may be due to the large amounts of free allowances, and that plants may have passed on additional marginal costs to consumers.

scholarly journals Metabolome–Microbiome Crosstalk and Human Disease

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 181 ◽  
Author(s):  
Kathleen A. Lee-Sarwar ◽  
Jessica Lasky-Su ◽  
Rachel S. Kelly ◽  
Augusto A. Litonjua ◽  
Scott T. Weiss
Keyword(s):  
Human Body ◽  
Disease Risk ◽  
Human Microbiome ◽  
Treatment Strategies ◽  
Human Microbiome Project ◽  
Combined Use ◽  
Microbiome Data ◽  
The Impact ◽  
Active Investigation ◽  
Human Intestinal Tract

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these “omics” are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.

scholarly journals Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data

2021 ◽  
Vol 11 (2) ◽  
pp. 128
Author(s):  
Eunchong Huang ◽  
Sarah Kim ◽  
TaeJin Ahn
Keyword(s):  
Insulin Resistance ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Integrated Analysis ◽  
Omics Data ◽  
Technological Advances ◽  
Interpretation Algorithm ◽  
Next Generation Sequencing Ngs ◽  
Microbiome Data ◽  
The Impact

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.

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