A Comparison of Three Bioinformatics Pipelines for the Analysis of Preterm Gut Microbiota using 16S rRNA Gene Sequencing Data

Plummer, Erica L; Twin, Jimmy; Bulach, Dieter; Garl, Suzanne M.; Tabrizi, Sepehr N.

doi:10.4172/jpb.1000381

Cited by 100 publications

(93 citation statements)

References 28 publications

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“…-Presence of PCR inhibitors (Juen & Traugott, 2006) digestion (Arai, Welch, Dunsmuir, Jacobs, & Ladouceur, 2003) (Pompanon et al, 2012) Gómez, Swift, & Dawson, 2017) -& Thomas, 2010) (Gonzalez, Portillo, Belda-Ferre, & Mira, 2012) estimates (Plummer & Twin, 2015) Sample Collection DNA extraction PCR Sequencing Bio-informatics 1.…”

Section: Potential Biases Stage Of Protocolmentioning

confidence: 99%

How quantitative is metabarcoding: A meta‐analytical approach

et al. 2018

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Metabarcoding has been used in a range of ecological applications such as taxonomic assignment, dietary analysis and the analysis of environmental DNA. However, after a decade of use in these applications there is little consensus on the extent to which proportions of reads generated corresponds to the original proportions of species in a community. To quantify our current understanding, we conducted a structured review and meta‐analysis. The analysis suggests that a weak quantitative relationship may exist between the biomass and sequences produced (slope = 0.52 ± 0.34, p < 0.01), albeit with a large degree of uncertainty. None of the tested moderators, sequencing platform type, the number of species used in a trial or the source of DNA, were able to explain the variance. Our current understanding of the factors affecting the quantitative performance of metabarcoding is still limited: additional research is required before metabarcoding can be confidently utilized for quantitative applications. Until then, we advocate the inclusion of mock communities when metabarcoding as this facilitates direct assessment of the quantitative ability of any given study.

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Section: Potential Biases Stage Of Protocolmentioning

confidence: 99%

How quantitative is metabarcoding: A meta‐analytical approach

et al. 2018

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“…A comparison of these three bioinformatic pipelines has been conducted by Plummer et al [41] using 16S rRNA gut microbial data. The study concluded that all of the three pipelines were able to generate similar and reliable results with common limitation the ability to classify at the species level due to the type of data.…”

Section: Mg-rastmentioning

confidence: 99%

Massive Sequencing: A New Tool for the Control of Alcoholic Fermentation in Wine?

et al. 2018

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Abstract:In wine industry, there is a prevalent use of starter cultures to promote a controlled and efficient alcoholic fermentation preventing the growth of spoilage microbes. However, current trends in enology aim to combine the guaranteed success of monitored process and the complexity of fermentations either by inoculating autochthonous starters or by performing spontaneously to produce distinctive wines. To understand the complex roles of microorganisms on wine fermentation, we must understand their population dynamics and their relationships with wine quality and metabolome. Current metagenomics techniques based on massive sequencing are gaining relevance to study the diversity and evolution of microbial population on every stage of the wine making process. This new tool and technique increases the throughput and sensitivity to study microbial communities. This review focuses on the current knowledge about wine alcoholic fermentation, the contribution of massive sequencing techniques and the possibility of using this tool for microbial control.

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“…It is important to recognise that due to some possible pitfalls in sample processing, the abundance of specific bacterial species and overall community composition can be distorted, thus hampering the analysis and threatening the validity of the research findings [14]. In addition, a key limitation of using 16S rRNA gene analysis for genus and species level classification is that related bacterial species may be indistinguishable due to near identical 16S rRNA gene sequences [15]. The potential for different data analysis approaches to impact on outcomes has also been recognised.…”

Section: Introductionmentioning

confidence: 99%

“…The potential for different data analysis approaches to impact on outcomes has also been recognised. Plummer et al [15] compared three pipelines commonly used for 16S rRNA gene analysis: QIIME, MG-RAST and mothur. Favourably, their results showed that the three pipelines assessed produce comparable results for analysis of faecal samples, in terms of alpha diversity analysis and usability.…”

Section: Introductionmentioning

confidence: 99%

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

Zhang

West

Chen

et al. 2018

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: A large number of microbial species have been detected in human faecal samples, with many of the species having high correlations with each other. Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to see whether individuals can be clustered into two classification groups based on predetermined criteria: control and disease group. However, there might be a combination of other components that better distinguish diseased individuals from healthy controls. Genetic algorithms (GA) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and GA for identifying sets of bacterial species associated with high body mass. Results: GA has selected the subsets of the principal components (PCs) produced by PCA. The prediction models built with theses PCs produced much higher area under the curve (AUC) values compared to the models built using topPCs which explained the most variance in the sample. The combinatorial effect of the identified bacterial species that contributed the most to the PCs may be associated with body mass. Conclusions:The proposed algorithm overcomes the limitation of using PCA for prediction modelling. The application of the algorithm on an obesity study has shown the value of applying GA for selecting PC subsets from PCA to improve prediction models. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

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A Comparison of Three Bioinformatics Pipelines for the Analysis of Preterm Gut Microbiota using 16S rRNA Gene Sequencing Data

Cited by 100 publications

References 28 publications

How quantitative is metabarcoding: A meta‐analytical approach

How quantitative is metabarcoding: A meta‐analytical approach

Massive Sequencing: A New Tool for the Control of Alcoholic Fermentation in Wine?

Combination of Principal Component Analysis and Genetic Algorithm for Microbial Biomarker Identification in Obesity

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