scholarly journals Identification of multidimensional Boolean patterns in microbial communities

Microbiome ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
George Golovko ◽  
Khanipov Kamil ◽  
Levent Albayrak ◽  
Anna M. Nia ◽  
Renato Salomon Arroyo Duarte ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Network Inference ◽  
Statistical Significance ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Specific Method ◽  
Interaction Patterns ◽  
Two Dimensional ◽  
Community Members ◽  
Project Data

Abstract Background Identification of complex multidimensional interaction patterns within microbial communities is the key to understand, modulate, and design beneficial microbiomes. Every community has members that fulfill an essential function affecting multiple other community members through secondary metabolism. Since microbial community members are often simultaneously involved in multiple relations, not all interaction patterns for such microorganisms are expected to exhibit a visually uninterrupted pattern. As a result, such relations cannot be detected using traditional correlation, mutual information, principal coordinate analysis, or covariation-based network inference approaches. Results We present a novel pattern-specific method to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relation patterns between abundance profiles of two organisms as well as extend this approach to allow search and visualize three-, four-, and higher dimensional patterns. The proposed approach has been tested using 2380 microbiome samples from the Human Microbiome Project resulting in body site-specific networks of statistically significant 2D patterns as well as revealed the presence of 3D patterns in the Human Microbiome Project data. Conclusions The presented study suggested that search for Boolean patterns in the microbial abundance data needs to be pattern specific. The reported presence of multidimensional patterns (which cannot be reduced to a combination of two-dimensional patterns) suggests that multidimensional (multi-organism) relations may play important roles in the organization of microbial communities, and their detection (and appropriate visualization) may lead to a deeper understanding of the organization and dynamics of microbial communities.

scholarly journals Identification of Complex Multidimensional Patterns in Microbial Communities

2019 ◽  
Author(s):  
Golovko George ◽  
Khanipov Kamil ◽  
Albayrak Levent ◽  
Fofanov Yuriy
Keyword(s):  
Microbial Communities ◽  
Statistical Significance ◽  
Source Code ◽  
Three Dimensional ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Site Specific ◽  
Information Coefficient ◽  
Complex Relationships ◽  
Project Data

AbstractMotivationIdentification of complex relationships within members of microbial communities is key to understand and guide microbial transplantation and provide personalized anti-microbial and probiotic treatments. Since members of a given microbial community can be simultaneously involved in multiple relations that altogether will determine their abundance, not all significant relations between organisms are expected to be manifested as visually uninterrupted patterns and be detected using traditional correlation nor mutual information coefficient based approaches.ResultsThis manuscript proposes a pattern specific way to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relations patterns between abundance profiles of two organisms which can be extended to three or more dimensional patterns. Presented approach can also be extended by including a variety of physical (pH, temperature, oxygen concentration) and biochemical (antimicrobial susceptibility, nutrient and metabolite concentration) variables into the search for multidimensional patterns. The presented approach has been tested using 2,380 microbiome samples from the Human Microbiome Project resulting in body-site specific networks of statistically significant 2D patterns. We also were able to demonstrate the presence of several 3D patterns in the Human Microbiome Project data.AvailabilityC++ source code for two and three-dimensional patterns, as well as executable files for the Pickle pipeline, are in the attached supplementary [email protected]

scholarly journals Evaluation of Microbiome Alterations Following Consumption of BIOHM, a Novel Probiotic

2021 ◽  
Vol 43 (3) ◽  
pp. 2135-2146
Author(s):  
Mahmoud A. Ghannoum ◽  
Thomas S. McCormick ◽  
Mauricio Retuerto ◽  
Gurkan Bebek ◽  
Susan Cousineau ◽  
...  
Keyword(s):  
Human Subjects ◽  
Statistical Significance ◽  
Gastrointestinal Symptoms ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Saccharomyces Boulardii ◽  
P Value ◽  
Significant Difference ◽  
Bowel Diseases ◽  
Project Data

Gastrointestinal microbiome dysbiosis may result in harmful effects on the host, including those caused by inflammatory bowel diseases (IBD). The novel probiotic BIOHM, consisting of Bifidobacterium breve, Saccharomyces boulardii, Lactobacillus acidophilus, L. rhamnosus, and amylase, was developed to rebalance the bacterial–fungal gut microbiome, with the goal of reducing inflammation and maintaining a healthy gut population. To test the effect of BIOHM on human subjects, we enrolled a cohort of 49 volunteers in collaboration with the Fermentation Festival group (Santa Barbara, CA, USA). The profiles of gut bacterial and fungal communities were assessed via stool samples collected at baseline and following 4 weeks of once-a-day BIOHM consumption. Mycobiome analysis following probiotic consumption revealed an increase in Ascomycota levels in enrolled individuals and a reduction in Zygomycota levels (p value < 0.01). No statistically significant difference in Basidiomycota was detected between pre- and post-BIOHM samples and control abundance profiles (p > 0.05). BIOHM consumption led to a significant reduction in the abundance of Candida genus in tested subjects (p value < 0.013), while the abundance of C. albicans also trended lower than before BIOHM use, albeit not reaching statistical significance. A reduction in the abundance of Firmicutes at the phylum level was observed following BIOHM use, which approached levels reported for control individuals reported in the Human Microbiome Project data. The preliminary results from this clinical study suggest that BIOHM is capable of significantly rebalancing the bacteriome and mycobiome in the gut of healthy individuals, suggesting that further trials examining the utility of the BIOHM probiotic in individuals with gastrointestinal symptoms, where dysbiosis is considered a source driving pathogenesis, are warranted.

scholarly journals Detection of Multidimensional Co-Exclusion Patterns in Microbial Communities

2018 ◽  
Author(s):  
Levent Albayrak ◽  
Kamil Khanipov ◽  
George Golovko ◽  
Yuriy Fofanov
Keyword(s):  
Microbial Communities ◽  
Statistical Significance ◽  
Source Code ◽  
Human Microbiome ◽  
Correlation Coefficients ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
P Value ◽  
Complex Relationships ◽  
And Control

AbstractMotivationIdentification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms’ intolerance to each other’s presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.ResultsThe implemented computational pipeline (CoEx) tested against 2,380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns.AvailabilityC++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.aspContactlealbayr@utmb.eduSupplementary informationSupplementary data are available at biorxiv online.

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.

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

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 ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 103 ◽  
Author(s):  
Subina Mehta ◽  
Marie Crane ◽  
Emma Leith ◽  
Bérénice Batut ◽  
Saskia Hiltemann ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Genetic Material ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Taxonomic Composition ◽  
Sequencing Technologies ◽  
The Galaxy ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

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 HMPDACC: a Human Microbiome Project Multi-omic data resource

2020 ◽  
Vol 49 (D1) ◽  
pp. D734-D742
Author(s):  
Heather Huot Creasy ◽  
Victor Felix ◽  
Jain Aluvathingal ◽  
Jonathan Crabtree ◽  
Olukemi Ifeonu ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Data Repository ◽  
Data Resource ◽  
Faceted Search ◽  
Disease States ◽  
Two Phases ◽  
Project Data ◽  
Data Coordination ◽  
Access Data

Abstract The Human Microbiome Project (HMP) explored microbial communities of the human body in both healthy and disease states. Two phases of the HMP (HMP and iHMP) together generated &gt;48TB of data (public and controlled access) from multiple, varied omics studies of both the microbiome and associated hosts. The Human Microbiome Project Data Coordination Center (HMPDACC) was established to provide a portal to access data and resources produced by the HMP. The HMPDACC provides a unified data repository, multi-faceted search functionality, analysis pipelines and standardized protocols to facilitate community use of HMP data. Recent efforts have been put toward making HMP data more findable, accessible, interoperable and reusable. HMPDACC resources are freely available at www.hmpdacc.org.

Sign in / Sign up

Export Citation Format

Share Document