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IMAP: Integrated Microbiome Analysis Pipelines: End-to-End Practical User Guides Using Integrated Approaches

5 Exploring Mothur Output: OTU Table, Taxonomy, and Metadata

Welcome to the next phase of our microbiome bioinformatics journey, where we dive into the analysis using Mothur’s output. In this section, we’ll explore key components, including the OTU table, Taxonomy information, and associated metadata.

5.1 Initial Data Exploration

Before delving into the analysis, let’s familiarize ourselves with the main outputs generated by Mothur:

  1. OTU Table: The Operational Taxonomic Unit (OTU) table represents the abundance of different microbial taxa across samples.

  2. Taxonomy Data: This provides information about the taxonomic classification of each OTU, helping us understand the microbial community composition.

  3. Metadata: Additional information associated with each sample, such as sample source, experimental conditions, or any other relevant details.

5.2 Starting the Analysis

Begin by loading the OTU table, Taxonomy, and metadata into your R environment.

In the example R code below, we use the read_csv function from the readr package to efficiently read CSV files.

library(readr)

# Example R code for loading data
otu_table <- read_csv("path/to/otu_table.csv")
taxonomy_data <- read_csv("path/to/taxonomy_data.csv")
metadata <- read_csv("path/to/metadata.csv")

Here’s a breakdown of the key points:

  • The read_csv function is used to read CSV files.
  • The read_csv function automatically detects the delimiter used in the CSV file.
  • Ensure the actual delimiter in your CSV files matches the default assumption (a comma).