and formed the most suitable pipeline for microbiome analysis. This paper accompanied by detailed code examples, which can help beginners to learn, as well as help analysts compare and test different tools. This
参考文献: The best practice for microbiome analysis using R Protein Cell. 2023 May 2 PMID: 37128855 DOI: 10.1093/procel/pwad024 The best practice for microbiome analysis using R
Delving into the intricate world of microbiomes through metagenomic analysis opens a window to understand the hidden interactions of microbial communities within diverse ecosystems. This chapter dissects the core aspects of metagenome analysis using R packages, emphasizing the need for adapting workflows ...
As per the outcomes of metagenomic analysis, Proteobacteria (39.04%), Firmicutes (5.27%), Actinobacteria (2.94%), and Basidiomycota (2.77%) were found to be the dominant phyla in the microbiome of the vaginal samples. At the genus level, Pseudomonas (21.90%) was found to be the most ...
34,35. For example, this approach led to the discovery of a superfamily of translation-targeting toxin-antitoxin systems, TumE–TumA34. Additionally, the field of functional annotation predictions has benefited significantly from the application of natural language processing (NLP) techniques. In NLP...
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
MGX is a framework for the analysis of metagenome data obtained by high-throughput sequencing. MGX is implemented as a client/server solution (Fig. 1) based on the Java programming language, which ensures maximum portability across a variety of commonly used operating systems such as MS Windows,...
From a microbiome perspective, the primary concern with using alternative media and consumer-grade materials is the risk of contaminant RNases and/or PCR inhibitors. The presence of these molecules would increase the false-negative rate of detecting SARS-CoV-2 RNA by either degrading the virus, ...
Results: We developed an R package 'metamicrobiomeR' that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real ...
N.R. executed and designed the microbiome analysis protocol and is the author of the KrakenTools α-diversity tools. J.L. developed the pathogen identification protocol and is the author of Bracken and KrakenTools. M.S. authored the Jupyter notebooks for the protocol. D.E.W. is the senior...