Streamline Microbiome Analysis with mbtools

Mbtools is a powerful R package designed to simplify and streamline microbiome data analysis. This comprehensive toolkit provides a structured workflow, incorporating common analytical steps and adhering to best practices derived from extensive experience in the field. This article explores the core functionalities of mbtools, highlighting its key features and demonstrating how it empowers researchers to efficiently process and interpret complex microbiome data.

Understanding the mbtools Philosophy

mbtools adopts a workflow-based approach to analysis, mirroring the established framework of Qiime 2. This structure breaks down complex analyses into a series of discrete steps, each building upon the previous one. This modularity promotes clarity, reproducibility, and facilitates troubleshooting. Each step operates on defined input data and utilizes a specific configuration, ensuring consistency and transparency throughout the entire process.

Data Types in mbtools

The package primarily handles three fundamental data types:

• Artifacts: These are complex data objects generated by individual analysis steps within the mbtools workflow. They encapsulate intermediate results and serve as inputs for subsequent steps.
• Phyloseq Objects: mbtools leverages the popular phyloseq package, utilizing its specialized objects to represent microbiome data. These objects integrate sequence variant abundances, taxonomic classifications, and sample metadata into a unified structure.
• Tidy Data Tables: Adhering to the principles of tidy data, mbtools employs data frames organized in a consistent format. This ensures compatibility with a wide range of R packages and facilitates data manipulation and visualization.

Workflow Steps with mbtools

A typical mbtools workflow consists of a sequence of interconnected steps, each performed by a dedicated function. These steps can be seamlessly chained together using the pipe operator (%>%), creating a fluent and readable analysis pipeline. A basic workflow might include steps like demultiplexing, quality control, preprocessing, and denoising. Crucially, each step is guided by a configuration object that specifies parameters and options.

library(mbtools)

config <- list(
  demultiplex = config_demultiplex(barcodes = c("ACGTA", "AGCTT")),
  preprocess = config_preprocess(truncLen = 200),
  denoise = config_denoise()
)

output <- find_read_files("raw") %>%
  demultiplex(config$demultiplex) %>%
  quality_control() %>%
  preprocess(config$preprocess) %>%
  denoise(config$denoise)

This example demonstrates the clarity and conciseness of an mbtools workflow. Furthermore, the configuration can be stored externally, for example in a YAML file, promoting reproducibility and collaboration.

preprocess:
  threads: yes
  out_dir: preprocessed
  trimLeft: 10.0
  truncLen: 200.0
  maxEE: 2.0

denoise:
  threads: yes
  nbases: 2.5e+08
  pool: no
  bootstrap_confidence: 0.5
  taxa_db: https://zenodo.org/record/1172783/files/silva_nr_v132_train_set.fa.gz?download=1
  species_db: https://zenodo.org/record/1172783/files/silva_species_assignment_v132.fa.gz?download=1
  hash: yes

This configuration can then be easily loaded and applied to a new analysis.

config <- read_yaml("config.yml")

output <- find_read_files("raw") %>%
  quality_control() %>%
  preprocess(config$preprocess) %>%
  denoise(config$denoise)

Beyond Workflows: Additional mbtools Functionality

In addition to its core workflow structure, mbtools offers a suite of functions for specialized tasks, data visualization, and generating analysis endpoints. These functions often operate on phyloseq objects or tidy data tables, providing a versatile toolkit for comprehensive microbiome analysis. mbtools allows researchers to perform complex analyses with ease, fostering reproducible research and accelerating the pace of microbiome discovery.