A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes

Pookhao, Naruekamol; Sohn, Michael B.; Li, Haiquan; Jenkins, Isaac C.; Du, Ruofei; Jiang, Hongmei; An, Lingling

doi:10.1093/bioinformatics/btu635

Cited by 17 publications

(15 citation statements)

References 29 publications

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“…The differential expression of specific bacterial genera between groups was determined using the two-stage statistical procedure. 23 This was a statistical algorithm developed by Pookhao et al 23 to detect differentially abundant features in large metagenomes under various conditions. In the first stage, the algorithm aims to detect informative features associated with a particular phenotype that results in the dimensional reduction of the metagenomic data.…”

Section: Methodsmentioning

confidence: 99%

Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva

Doan

Akileswaran

Andersen

et al. 2016

Invest. Ophthalmol. Vis. Sci.

202

203

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PurposeTo characterize the ocular surface microbiome of healthy volunteers using a combination of microbial culture and high-throughput DNA sequencing techniques.MethodsConjunctival swab samples from 107 healthy volunteers were analyzed by bacterial culture, 16S rDNA gene deep sequencing (n = 89), and biome representational in silico karyotyping (BRiSK; n = 80). Swab samples of the facial skin (n = 42), buccal mucosa (n = 50), and environmental controls (n = 27) were processed in parallel. 16S rDNA gene quantitative PCR was used to calculate the bacterial load in each site. Bacteria were characterized by site using principal coordinate analysis of metagenomics data. BRiSK data were analyzed for presence of fungi and viruses.ResultsCorynebacteria, Propionibacteria, and coagulase-negative Staphylococci were the predominant organisms identified by all three techniques. Quantitative 16S PCR demonstrated approximately 0.1 bacterial 16S rDNA/human actin copy on the ocular surface compared with greater than 10 16S rDNA/human actin copy for facial skin or the buccal mucosa. The conjunctival bacterial community structure is distinct compared with the facial skin (R = 0.474, analysis of similarities P = 0.0001), the buccal mucosa (R = 0.893, P = 0.0001), and environmental control samples (R = 0.536, P = 0.0001). 16S metagenomics revealed substantially more bacterial diversity on the ocular surface than other techniques, which appears to be artifactual. BRiSK revealed presence of torque teno virus (TTV) on the healthy ocular surface, which was confirmed by direct PCR to be present in 65% of all conjunctiva samples tested.ConclusionsRelative to adjacent skin or other mucosa, healthy ocular surface microbiome is paucibacterial. Its flora are distinct from adjacent skin. Torque teno virus is a frequent constituent of the ocular surface microbiome. (ClinicalTrials.gov number, NCT02298881.)

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Section: Methodsmentioning

confidence: 99%

Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva

Doan

Akileswaran

Andersen

et al. 2016

Invest. Ophthalmol. Vis. Sci.

202

203

View full text Add to dashboard Cite

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“…For results of biochemical assays, the Kolmogorov-Smirnov test was used to check for normality; the Mann-Whitney U test was used to compare any two data sets that were not normally distributed, and the values were presented as median (25th and 75th percentiles); otherwise, one-way ANOVA followed by the Student-Newman-Keuls method was used, and the values were presented as mean 6 SME; all p values were adjusted by the Benjamini-Hochberg method (Benjamini and Hochberg, 1995). Both the Wilcoxon rank sum test combined with the Benjamini-Hochberg method and a two-stage statisti-cal procedure were applied to compare bacteria taxa (Pookhao et al, 2015). Metabolites were selected by SIMCA-P-12.0 and compared by one-way ANOVA followed by the Student-Newman-Keuls method.…”

Section: Statisticsmentioning

confidence: 99%

Alterations and correlations of the gut microbiome, metabolism and immunity in patients with primary biliary cirrhosis

Fang

Shi

et al. 2016

Environmental Microbiology

201

142

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We selected 42 early-stage primary biliary cirrhosis (PBC) patients and 30 healthy controls (HC). Metagenomic sequencing of the 16S rRNA gene was used to characterize the fecal microbiome. UPLC-MS/MS assaying of small molecules was used to characterize the metabolomes of the serum, urine and feces. Liquid chip assaying of serum cytokines was used to characterize the immune profiles. The gut of PBC patients were depleted of some potentially beneficial bacteria, such as Acidobacteria, Lachnobacterium sp., Bacteroides eggerthii and Ruminococcus bromii, but were enriched in some bacterial taxa containing opportunistic pathogens, such as γ-Proteobacteria, Enterobacteriaceae, Neisseriaceae, Spirochaetaceae, Veillonella, Streptococcus, Klebsiella, Actinobacillus pleuropneumoniae, Anaeroglobus geminatus, Enterobacter asburiae, Haemophilus parainfluenzae, Megasphaera micronuciformis and Paraprevotella clara. Several altered gut bacterial taxa exhibited potential interactions with PBC through their associations with altered metabolism, immunity and liver function indicators, such as those of Klebsiella with IL-2A and Neisseriaceae with urinary indoleacrylate. Many gut bacteria, such as some members of Bacteroides, were altered in their associations with the immunity and metabolism of PBC patients, although their relative abundances were unchanged. Consequently, the gut microbiome is altered and may be critical for the onset or development of PBC by interacting with metabolism and immunity.

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“…For whole metagenome shotgun projects, where gene protein coding information is available, functional comparative metagenomics is possible. It is based on identifying differential feature abundance (pathways, subsystems, or functional roles) between two or more conditions following a statistical procedure with some normalization step (Rodriguez-Brito et al, 2006 ; Pookhao et al, 2015 ). Some useful tools to perform robust comparative functional metagenomics are Parallel-meta and MEGAN.…”

Section: Comparative Metagenomicsmentioning

confidence: 99%

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

2015

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The study of microorganisms that pervade each and every part of this planet has encountered many challenges through time such as the discovery of unknown organisms and the understanding of how they interact with their environment. The aim of this review is to take the reader along the timeline and major milestones that led us to modern metagenomics. This new and thriving area is likely to be an important contributor to solve different problems. The transition from classical microbiology to modern metagenomics studies has required the development of new branches of knowledge and specialization. Here, we will review how the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution. New computational methods are constantly developed to collect, process, and extract useful biological information from a variety of samples and complex datasets, but metagenomics needs the integration of several of these computational methods. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design, which allows them to reveal the information in a metagenome.

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A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes

Abstract: Supplementary file is available at Bioinformatics online.

Cited by 17 publications

References 29 publications

Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva

Paucibacterial Microbiome and Resident DNA Virome of the Healthy Conjunctiva

Alterations and correlations of the gut microbiome, metabolism and immunity in patients with primary biliary cirrhosis

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

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