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Journal ArticleDOI

Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.

Jasmine Chong, +3 more
- 15 Jan 2020 - 
- Vol. 15, Iss: 3, pp 799-821
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TLDR
This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data, a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces.
Abstract
MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min. This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data.

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Citations
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Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

TL;DR: MetaboAnalyst 5.0 as discussed by the authors extends the previous 2011 Nature Protocol by providing stepwise instructions to optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata.
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Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

TL;DR: In this paper, a review of the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities, is presented.
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Bile acids drive the newborn's gut microbiota maturation.

TL;DR: The authors investigate the impact of the developing host hepatic metabolism on the murine intestinal microbiota composition with comparative analysis at immediate postnatal period, early infancy and weaning and adulthood.
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Mass spectrometry-based metabolomics in microbiome investigations.

TL;DR: In this article, the authors provide a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

TL;DR: The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
Journal ArticleDOI

DADA2: High-resolution sample inference from Illumina amplicon data

TL;DR: The open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors is presented, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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