Dataset Information

Analysis of composition of microbiomes: a novel method for studying microbial composition.

ABSTRACT: BACKGROUND:Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data. OBJECTIVE:To develop a methodology that accounts for compositional constraints to reduce false discoveries in detecting differentially abundant taxa at an ecosystem level, while maintaining high statistical power. METHODS:We introduced a novel statistical framework called analysis of composition of microbiomes (ANCOM). ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa. RESULTS:We compared the performance of ANCOM to the standard t-test and a recently published methodology called Zero Inflated Gaussian (ZIG) methodology (1) for drawing inferences on the mean taxa abundance in two or more populations. ANCOM controlled the false discovery rate (FDR) at the desired nominal level while also improving power, whereas the t-test and ZIG had inflated FDRs, in some instances as high as 68% for the t-test and 60% for ZIG. We illustrate the performance of ANCOM using two publicly available microbial datasets in the human gut, demonstrating its general applicability to testing hypotheses about compositional differences in microbial communities. CONCLUSION:Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.

SUBMITTER: Mandal S

PROVIDER: S-EPMC4450248 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of composition of microbiomes: a novel method for studying microbial composition.

Mandal Siddhartha S Van Treuren Will W White Richard A RA Eggesbø Merete M Knight Rob R Peddada Shyamal D SD

Microbial ecology in health and disease 20150529

<h4>Background</h4>Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data.<h4>Objective</h4>To develop a methodology that accounts fo ...[more]

PMID: 26028277

Similar Datasets

Project description:BackgroundMicrobiomes play vital roles in shaping environments and stabilize them based on their compositions and inter-species relationships among its species. Variations in microbial properties have been reported to have significant impact on their host environment. For example, variants in gut microbiomes have been reported to be associated with several chronic conditions, such as inflammatory disease and irritable bowel syndrome. However, how microbial bacteria contribute to pathogenesis still remains unclear and major research questions in this domain remain unanswered.MethodsWe propose a split graph model to represent the composition and interactions of a given microbiome. We used metagenomes from Korean populations in this study. The dataset consists of three different types of samples, viz. mucosal tissue and stool from Crohn's disease patients and stool from healthy individuals. We use the split graph model to analyze the impact of microbial compositions on various host phenotypes. Utilizing the graph model, we have developed a pipeline that integrates genomic information and pathway analysis to characterize both critical informative components of inter-bacterial correlations and associations between bacterial taxa and various metabolic pathways.ResultsThe obtained results highlight the importance of the microbial communities and their inter-relationships and show how these microbial structures are correlated with Crohn's disease. We show that there are significant positive associations between detected taxonomic biomarkers as well as multiple functional modules in the split graph of mucosal tissue samples from CD patients. Bacteria Moraxellaceae and Pseudomonadaceae were detected as taxonomic biomarkers in CD groups. Higher abundance of these bacteria have been reported in previous study and several metabolic pathways associated with these bacteria were characterized in CD samples.ConclusionsThe proposed pipeline provides a new way to approach the analysis of complex microbiomes. The results obtained from this study show great potential in unraveling mechansims in complex biological systems to understand how various components in such complex environments are associated with critical biological functions.

Project description:The microbial communities associated with plants (the plant microbiome) play critical roles in regulating plant health and productivity. Because of this, in recent years, there have been significant increase in studies targeting the plant microbiome. Amplicon sequencing is widely used to investigate the plant microbiome and to develop sustainable microbial agricultural tools. However, performing large microbiome surveys at the regional and global scales pose several logistic challenges. One of these challenges is related with the preservation of plant materials for sequencing aiming to maintain the integrity of the original diversity and community composition of the plant microbiome. Another significant challenge involves the existence of multiple primer sets used in amplicon sequencing that, especially for bacterial communities, hampers the comparability of datasets across studies. Here, we aimed to examine the effect of different preservation approaches (snap freezing, fresh and kept on ice, and air drying) on the bacterial and fungal diversity and community composition on plant leaves, stems and roots from seven plant species from contrasting functional groups (e.g. C3, C4, N-Fixers, etc.). Another major challenge comes when comparing plant to soil microbiomes, as different primers sets are often used for plant vs. soil microbiomes. Thus, we also investigated if widely used 16S rRNA primer set (779F/1193R) for plant microbiome studies provides comparable data to those often used for soil microbiomes (341F/805R) using 86 soil samples. We found that the community composition and diversity of bacteria or fungi were robust to contrasting preservation methods. The primer sets often used for plants provided similar results to those often used for soil studies suggesting that simultaneous studies on plant and soil microbiomes are possible. Our findings provide novel evidence that preservation approaches do not significantly impact plant microbiome data interpretation and primer differences do not impact the treatment effect, which has significant implication for future large-scale and global surveys of plant microbiomes.

Project description:BackgroundTaxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs.ResultsWe developed VALENCIA (VAginaL community state typE Nearest CentroId clAssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA.ConclusionVALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets. Video abstract.

Project description:BACKGROUND: Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. RESULTS: This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of ?-helix structure and thermophilic proteins to be soluble. CONCLUSIONS: The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. AVAILABILITY: The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.

Project description:Macroautophagy/autophagy plays a critical role in the pathogenesis of various human diseases including neurodegenerative disorders such as Parkinson disease (PD) and Huntington disease (HD). Chemical autophagy inducers are expected to serve as disease-modifying agents by eliminating cytotoxic/damaged proteins. Although many autophagy inducers have been identified, their precise molecular mechanisms are not fully understood because of the complicated crosstalk among signaling pathways. To address this issue, we performed several chemical genomic analyses enabling us to comprehend the dominancy among the autophagy-associated pathways followed by an aggresome-clearance assay. In a first step, more than 400 target-established small molecules were assessed for their ability to activate autophagic flux in neuronal PC12D cells, and we identified 39 compounds as autophagy inducers. We then profiled the autophagy inducers by testing their effect on the induction of autophagy by 200 well-established signal transduction modulators. Our principal component analysis (PCA) and clustering analysis using a dataset of "autophagy profiles" revealed that two Food and Drug Administration (FDA)-approved drugs, memantine and clemastine, activate endoplasmic reticulum (ER) stress responses, which could lead to autophagy induction. We also confirmed that SMK-17, a recently identified autophagy inducer, induced autophagy via the PRKC/PKC-TFEB pathway, as had been predicted from PCA. Finally, we showed that almost all of the autophagy inducers tested in this present work significantly enhanced the clearance of the protein aggregates observed in cellular models of PD and HD. These results, with the combined approach, suggested that autophagy-activating small molecules may improve proteinopathies by eliminating nonfunctional protein aggregates.Abbreviations: ADK: adenosine kinase; AMPK: AMP-activated protein kinase; ATF4: activating transcription factor 4; BECN1: beclin-1; DDIT3/CHOP: DNA damage inducible transcript 3; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 3; EIF2S1/eIF2α: eukaryotic translation initiation factor 2 subunit alpha; ER: endoplasmic reticulum; ERN1/IRE1α: endoplasmic reticulum to nucleus signaling 1; FDA: Food and Drug Administration; GSH: glutathione; HD: Huntington disease; HSPA5/GRP78: heat shock protein family A (Hsp70) member 5; HTT: huntingtin; JAK: Janus kinase, MAP1LC3B/LC3: microtubule associated protein 1 light chain 3 beta; MAP2K/MEK: mitogen-activated protein kinase kinase; MAP3K8/Tpl2: mitogen-activated protein kinase kinase kinase 8; MAPK: mitogen-activated protein kinase; MPP+: 1-methyl-4-phenylpyridinium; MTOR: mechanistic target of rapamycin kinase; MTORC: MTOR complex; NAC: N-acetylcysteine; NGF: nerve growth factor 2; NMDA: N-methyl-D-aspartate; PCA: principal component analysis; PD: Parkinson disease; PDA: pancreatic ductal adenocarcinoma; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; PMA: phorbol 12-myristate 13-acetate; PRKC/PKC: protein kinase C; ROCK: Rho-associated coiled-coil protein kinase; RR: ribonucleotide reductase; SIGMAR1: sigma non-opioid intracellular receptor 1; SQSTM1/p62: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TFEB: Transcription factor EB; TGFB/TGF-β: Transforming growth factor beta; ULK1: unc-51 like autophagy activating kinase 1; XBP1: X-box binding protein 1.

Dataset Information

Analysis of composition of microbiomes: a novel method for studying microbial composition.

Publications

Analysis of composition of microbiomes: a novel method for studying microbial composition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets