bioRxivMicrobiology
animalcules: Interactive Microbiome Analytics 1 and Visualization in R
W. E. Johnson, A. Federico, T. Faits, S. Manimaran, S. Monti, Y. Zhao
Abstract
BackgroundMicrobial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. ResultsTo address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis are enhanced in the toolkit, while new methods such as biomarker identification are introduced. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. ConclusionsWe present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html.
- organism: human
- assay: metagenomics
- assay: metabolomics
- research focus: clustering
- research focus: differential expression
- research focus: database / resource
- approach: deep learning
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- ampvis2: an R package to analyse and visualise 16S rRNA amplicon data
Kasper Skytte Andersen, Rasmus Hansen Kirkegaard, … Mads Albertsen·bioRxiv·Apr 11, 2018
- Mian: Interactive Web-Based 16S rRNA Operational Taxonomic Unit Table Data Visualization and Discovery Platform
Boyang Tom Jin·bioRxiv·Sep 14, 2018
- Metaviz: interactive statistical and visual analysis of metagenomic data
Justin Wagner, Florin Chelaru, … Héctor Corrada Bravo·Nucleic Acids ResearchPeer-reviewed·Feb 15, 2018
- LorMe: a streamlined and interoperable R framework for end-to-end microbiome analysis
Ningqi Wang, Yaozhong Zhang, … Zhong Wei·MicroBiome and One HealthPeer-reviewed·Jan 1, 2026
- Namco: A microbiome explorer
Alexander Dietrich, Monica Steffi Matchado, … Markus List·Microbial GenomicsPeer-reviewed·Aug 2, 2022
- SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis
Stevenn Volant, Pierre Lechat, … Amine Ghozlane·BMC BioinformaticsPeer-reviewed·Aug 10, 2020