OmicsLogic Metagenomic Data Analysis using DADA2 Pipeline on T-Bioinfo Server

The microbial world is a treasure trove of hidden wonders, and metagenomic data analysis is our guide to discovering them. Lets embark on an exciting journey to explore taxonomy classification, uncover microbial abundance, and dive deep into the realms of alpha and beta diversity using the potent tools DADA2 and QIIME2 on the T-Bioinfo Server.

DADA2 PIPELINE

The pipeline is based on running a number of programs, including DADA2, Ape, and Phyloseq algorithms. DADA2 generates amplicon sequence variant (ASV) tables, which are similar to OTU tables but detailed in that they tabulate the number of identical amplicon sequence variants from different samples. Microbial studies utilizing DADA2 provide high resolution accurately reconstructed amplicon sequences that improve the detection of sample diversity and biological variants.?

Now let's have a look at an example Metagenomics pipeline on the T-Bioinfo Server: https://server.t-bio.info/pipelinesamplicon16s18s/demopipelines/example-human-diet and learn about the types of input files that should be uploaded, parameters chosen to run the pipeline, processing pipeline and finally what the output files look like.

Downloadable Results & Data files: DADA2 Pipeline

Quality Profile Plot

Graphical representation of the distribution of base quality scores at each position in a sequence read. The plot displays the average quality score at each position, as well as the distribution of scores around the mean. Quality scores are a measure of the confidence in the base calls, with higher scores indicating higher confidence. The quality profile plot provides information about the quality of the sequencing data, such as the average quality score, the distribution of quality scores across the read length, and the presence of any systematic biases or errors.

Error Plot

An error plot is a graphical representation of the error rates for each base position in the sequencing reads. Error rates can arise due to various factors, such as sequencing errors, PCR amplification errors, or sequencing library preparation biases. By visualizing the error rates across the read length, an error plot can provide insights into the quality of the sequencing data and identify any systematic errors or biases that may need to be addressed.

OTU Taxonomy Classification Table

An OTU taxonomy classification table is a table that assigns taxonomic classifications to operational taxonomic units (OTUs). OTUs are clusters of sequences that are similar to each other and are often used as a proxy for species or taxonomic groups in metagenomic datasets. Taxonomic classifications are typically assigned to OTUs based on the similarity of their sequences to known reference sequences in a taxonomic database, such as the NCBI non-redundant database or the SILVA database.

PCA Plots

Identifies the most important variables (i.e., taxa or functional genes) that contribute to the variation between samples and represent them as a smaller set of principal components.

Taxa Abundance Bar Plot

Displays the relative abundance of different taxonomic groups (e.g., phyla, genera, species) in a sample or group of samples.

Taxa Proportionate Abundance Bar Plot

A taxa proportionate abundance bar plot is similar to a taxa abundance bar plot, but instead of showing the absolute abundance of each taxonomic group, it displays the proportion or percentage of the total abundance that each group represents.

Alpha Diversity Measure

Alpha diversity is a measure of biodiversity within a single community or ecosystem. It quantifies the diversity of species or other taxonomic units within a given area or sample. Alpha diversity measures are typically used to assess the richness (number of different taxa) and evenness (relative abundance of each taxon) of a community.

NMDS Plot

Non-metric multidimensional scaling (NMDS) is a method used to visualize similarities or dissimilarities between samples or groups of samples based on multiple variables or dimensions. The NMDS plot uses a dimensionality reduction technique to project the multivariate data onto a low-dimensional space (usually 2D or 3D) while preserving the relative distances between samples. Unlike other methods such as PCA or clustering, NMDS is a non-parametric method that does not assume any specific distribution or structure of the data.

Phyloseq Plot

Provides a comprehensive overview of the taxonomic composition of microbial communities, including the relative abundance of different taxa and their distribution across different samples or experimental conditions.

To learn more about each section & get a practical hands on experience, get started with “Metagenomic Data Analysis" mentor guided training program.

Link to the register for the program: https://learn.omicslogic.com/programs/metagenomic-data-analysis-2023

For any questions, you can reach out to us at [email protected]