/Tools-Microbiome-Analysis

A list of R environment based tools for microbiome data exploration, statistical analysis and visualization

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A list of R environment based tools for microbiome data exploration, statistical analysis and visualization

As a beginner, the entire process from sample collection to analysis for sequencing data is a daunting task. More specifically, the downstream processing of raw reads is the most time consuming and mentally draining stage. It is vital to understand the basic concepts in microbial ecology and then to use various tools at disposal to address specific research questions. Thankfully, several young researchers supported by their experienced principal investigators/supervisors are working on creating various tools for analysis and interpretation of microbial community data. A major achievement of the scientific community is the open science initiative which has led to sharing of knowledge worldwide. For microbial community analysis, several tools have been created in R, a free to use (GNU General Public License) programming language(Team, 2000). The power of R lies in its ease of working with individuals lacking programming skills and easy sharing of analysis scripts codes and packages aiding reproducibility. Using tools such as QIIME (the newer QIIME2) (Caporaso, Kuczynski, Stombaugh et al., 2010), Mothur (Schloss, Westcott, Ryabin et al., 2009), DADA2 (Callahan, McMurdie, Rosen et al., 2016) one can get from raw reads to species × samples table (OTU or ASVs amplicon sequence variants as suggested recently (Callahan, McMurdie & Holmes, 2017)). In this post, numerous resources that can be helpful for analysis of microbiome data are listed. This list may not have all the packages as this tool development space is ever growing. Feel free to add those packages or links to web tutorials related to microbiome data, there is a google docs excel sheet at this link for a list of tools which can be edited to include more tools. These are mostly for improving statistical analysis and visualisation. These tools provide convenient options for data analysis and include several steps where the user has to make decisions. The work by McMurdie PJ, Holmes S, Weiss S and Tsilimigras M.C. and Fodor A.A are useful resources to understand the data common to microbiome census. It can be tricky and frustrating in the beginning but patience and perseverance will be fruitful at the end (personal experience).

Tools:

  1. Ampvis2 Tools for visualising amplicon sequencing data
  2. CCREPE Compositionality Corrected by PErmutation and REnormalization
  3. DADA2 Divisive Amplicon Denoising Algorithm
  4. DESeq2 Differential expression analysis for sequence count data
  5. edgeR empirical analysis of DGE in R
  6. mare Microbiota Analysis in R Easily
  7. Metacoder An R package for visualization and manipulation of community taxonomic diversity data
  8. metagenomeSeq Differential abundance analysis for microbial marker-gene surveys
  9. microbiome R package Tools for microbiome analysis in R
  10. MINT Multivariate INTegrative method
  11. mixDIABLO Data Integration Analysis for Biomarker discovery using Latent variable approaches for ‘Omics studies
  12. mixMC Multivariate Statistical Framework to Gain Insight into Microbial Communities
  13. MMinte Methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data
  14. pathostat Statistical Microbiome Analysis on metagenomics results from sequencing data samples
  15. phylofactor Phylogenetic factorization of compositional data
  16. phylogeo Geographic analysis and visualization of microbiome data
  17. Phyloseq Import, share, and analyze microbiome census data using R
  18. qiimer R tools compliment qiime
  19. RAM R for Amplicon-Sequencing-Based Microbial-Ecology
  20. ShinyPhyloseq Web-tool with user interface for Phyloseq
  21. SigTree Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree
  22. SPIEC-EASI Sparse and Compositionally Robust Inference of Microbial Ecological Networks
  23. structSSI Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data
  24. Tax4Fun Predicting functional profiles from metagenomic 16S rRNA gene data
  25. taxize Taxonomic Information from Around the Web
  26. labdsv Ordination and Multivariate Analysis for Ecology
  27. Vegan R package for community ecologists
  28. igraph Network Analysis and Visualization in R
  29. MicrobiomeHD A standardized database of human gut microbiome studies in health and disease Case-Control
  30. Rhea A pipeline with modular R scripts
  31. microbiomeutilities Extending and supporting package based on microbiome and phyloseq R package
  32. breakaway Species Richness Estimation and Modeling
  33. corncob Modeling microbial abundances and dysbiosis with beta-binomial regression
  34. MicrobiomeR MicrobiomeR: An R Package for Simplified and Standardized Microbiome Analysis Workflows
  35. powmic Power assessment in microbiome case-control studies
  36. yingtools2 Tools and functions for working with clinical and microbiome data
  37. animalcules R shiny app for interactive microbiome analysis
  38. biome-shiny GUI for microbiome visualization, based on the shiny package "microbiome"
  39. MelonnPan Model-based Genomically Informed High-dimensional Predictor of Microbial Community Metabolic Profiles
  40. MaAsLin2 MaAsLin2: Microbiome Multivariate Association with Linear Models
  41. mbtools Collection of R tools to analyze microbiome data
  42. ANCOM R scripts for Analysis of Composition of Microbiomes (ANCOM)
  43. MetaLonDA METAgenomic LONgitudinal Differential Abundance method
  44. dacomp Testing for Differential Abundance in Compositional Counts Data, with Application to Microbiome Studies
  45. BEEM BEEM: Estimating Lotka-Volterra models from time-course microbiome sequencing data
  46. metamicrobiomeR Analysis of Microbiome Relative Abundance Data using Zero Inflated Beta GAMLSS and Meta-Analysis Across Studies using Random Effects Model
  47. GLMMMiRKAT A distance-based kernel association test based on the generalized linear mixed model
  48. MDPbiome MDPbiome: microbiome engineering through prescriptive perturbations
  49. bootLong The Block Bootstrap Method for Longitudinal Microbiome Data
  50. OMiSA Optimal Microbiome-based Survival Analysis (OMiSA)
  51. DECIPHER Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R
  52. DECIPHER/IIDTAXA IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences
  53. curatedMetagenomicData Accessible, curated metagenomic data through ExperimentHub
  54. themetagenomics Exploring Thematic Structure and Predicted Functionality of 16S rRNA Amplicon Data
  55. MDiNE MDiNE: a model to estimate differential co-occurrence networks in microbiome studies
  56. StructFDR False Discovery Rate Control Incorporating Phylogenetic Tree Increases Detection Power in Microbiome-Wide Multiple Testing
  57. metamicrobiomeR metamicrobiomeR: An R Package for Analysis of Microbiome Relative Abundance Data Using Zero-Inflated Beta GAMLSS and Meta-Analysis Across Studies Using Random Effects Models
  58. Pldist Pldist: Ecological Dissimilarities for Paired and Longitudinal Microbiome Association Analysis
  59. BDMMA Batch Effects Correction for Microbiome Data With Dirichlet-multinomial Regression
  60. RCM A unified framework for unconstrained and constrained ordination of microbiome read count data
  61. decontam Simple Statistical Identification and Removal of Contaminant Sequences in Marker-Gene and Metagenomics Data
  62. ZIBBSeqDiscovery A Zero-inflated Beta-binomial Model for Microbiome Data Analysis
  63. massMap A Two-Stage Microbial Association Mapping Framework With Advanced FDR Control
  64. SplinectomeR SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies
  65. DMBC A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis With Microbial Compositions
  66. MicrobiomeDDA An Omnibus Test for Differential Distribution Analysis of Microbiome Sequencing Data
  67. NMIT Microbial Interdependence Association Test--a Non-parametric Microbial Interdependence Test
  68. SparseMCMM Estimating and testing the microbial causal mediation effect with the high-dimensional and compositional microbiome data (SparseMCMM)
  69. MTA Microbial trend analysis (MTA) for common dynamic trend, group comparison and classification in longitudinal microbiome study
  70. miLineage A General Framework for Association Analysis of Microbial Communities on a Taxonomic Tree
  71. zeroSum Reference Point Insensitive Molecular Data Analysis
  72. MedTest A Distance-Based Approach for Testing the Mediation Effect of the Human Microbiome
  73. qgraph Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation
  74. Adaptive gPCA A method for structured dimensionality reduction

Other tools

  1. ggplot2 An implementation of the Grammar of Graphics in R
    • Widely used package for data visualization
  2. ggvegan ggplot-based versions of the plots produced by the vegan package
    • Convert base plots of vegan to ggplot.
  3. ggord A simple package for creating ordination plots with ggplot2
    • Alternative to ggvegan
  4. cowplot cowplot: Streamlined Plot Theme and Plot Annotations for ggplot2
    • Widely used package for combining multiple plots
  5. ggridges Ridgeline plots in ggplot2
  6. ggtext Improved text rendering support for ggplot2
    • More power in controlling annotations in plots (e.g. italicize taxa names in plots)
  7. patchwork The Composer of ggplots
    • Combining multiple plots made easy
  8. ggpubr Extension of ggplot2 based data visualization
    • Publication ready plots
  9. ggraph Grammar of Graph Graphics
    • Network graphs using ggplot2
  10. gganimate A Grammar of Animated Graphics
    • Animate ggplot2 (Useful for presenting time-series dynamics of microbial communities)
  11. ggforce Accelerating ggplot2
    • Zoom specific regions of the plots
  12. factoextra Extract and Visualize the Results of Multivariate Data Analyses
    • Powerful package for multivvariate data analysis
  13. ggcorrplot Visualization of a correlation matrix using ggplot2
  14. tidyverse R packages for data science
    • Universe of several useful R packages for data handling, analysis and vidualization
  15. Extensions of ggplot Gallary of numerous data visualistion R pacakges

Proteomics resources*

  1. RforProteomics Using R for proteomics data analysis
  2. RforProteomics Visualisation of proteomics data using R and Bioconductor
  3. proteomics proteomics: Mass spectrometry and proteomics data analysis

RNAseq resources*

  1. RNA-seq analysis in R Workflow by Shulin Cao
  2. RNA-seq workflow RNA-seq workflow: gene-level exploratory analysis and differential expression

*Note: These are not focused towards microbiome data. These are listed as a reference point for beginners. If you have or know of workflows tools specific for microbiome data please let us know and we can add them here!

Useful resources are provided by:

  1. Ben J. Callahan and Colleagues: Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses.
  2. Comeau AM and Colleagues: Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research
  3. Schloss, P. D: The Riffomonas Reproducible Research Tutorial Series
  4. Shetty SA, Lahti L., et al: Tutorial from microbiome data analysis spring school 2018, Wageningen University and Research
  5. Holmes S, Huber W.: Modern statistics for modern biology. Cambridge University Press; 2018 Nov 30.

Note:
A good practise is to use Rmarkdown for documenting your results and sharing with your collaborators and supervisors. For more information click here RStudio and
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References:

  1. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker gene data analysis. bioRxiv, 113597.
  2. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. & Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583.
  3. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K. & Gordon, J. I. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature methods 7, 335-336.
  4. Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H. & Robinson, C. J. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and environmental microbiology 75, 7537-7541.
  5. Team, R. C. (2000). R language definition. Vienna, Austria: R foundation for statistical computing.

TODO

Any help is welcome

  • Structure the list according to categories
    • General purpose
    • Visualization
    • Snapshot/cross-sectional stats
    • Time series/Longitudinal stats
    • Integrative -Omics
  • Include metagenomics/metabolomics
  • Include more general microbiology oriented R packages/tools
  • List of 'good' research paper reproducible repositories
  • and so on .....

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You can cite this resource as:
Shetty, Sudarshan A., and Leo Lahti. Microbiome data science. Journal of biosciences 44, no. 5 (2019): 115.
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