Master Metagenomics and Microbiome Data Analysis Using Linux

Explore how metagenomics enables culture-independent analysis of microbial communities by extracting DNA from environmental or body niches, sequencing to FASTQ, and analyzing all microbial genomes in a single sample.

Compare amplicon sequencing and shotgun metagenomics to study microbial communities, highlighting 16S rRNA targets and functional profiling, in a workflow from sample collection to data analysis.

Explore major microbiome initiatives, including hmp, imt, earth microbiome project, terra ocean survey, and american gut, noting body sites, sample scales, and health associations.

Explore Linux basics for bioinformatics, including open source concepts, Ubuntu and CentOS, command line workflows, package managers, and performing high-performance computing with Slurm and cron.

Set up the Windows Subsystem for Linux on Windows, install Ubuntu, configure the terminal, update packages, and enable graphical user interface apps with x11 for metagenomics workflows.

Navigate the Linux file system, master absolute and relative paths, and organize bioinformatics data with mkdir -p and structured directories.

Master basic Linux commands for bioinformatics file and directory management, including copy, move, and delete operations on fastq, bam, and ttf data to streamline workflows and reduce errors.

Process large bioinformatics text files in linux by extracting fasta headers, filtering gff and gtf entries, and processing data with core tools like grep, awk, sort, and uniq.

Learn linux file compression and archiving for bioinformatics using gzip, gunzip, bzip2, tar, and zip to reduce storage and enable faster data transfers of fastq reads.

Learn to install and manage bioinformatics tools on Linux using apt, yum, conda, precompiled binaries, or building from source; use containers, test through fastqc, and troubleshoot common issues.

Learn how to install and use conda with Miniconda or Anaconda on Linux, Windows, and Mac, including terminal commands and basic package management.

Install conda and set up qiime2, then download and prepare the moving pictures of human microbiome dataset for 16s rna data across time points.

Demultiplex Illumina reads by sample using barcodes, then perform quality control with dada2 and deblur, generate a feature table and ASVs, and visualize results for metagenomics and microbiome data analysis.

Explore alpha and beta diversity analyses in metagenomics data, including observed features, the Shannon index, and unifrac-based beta diversity, with practical steps to run QIIME workflows.

Explore taxonomic classification using a naive Bayes classifier with reference databases like Silva and Greengenes, a v4 database, and 80 percent confidence threshold to visualize taxa abundance via bar plots.

Perform differential abundance testing to identify taxa significantly more abundant between groups, using ancom-bc to correct compositional biases and report w statistics and log two fold changes.

Use crack in two for taxonomic classification, bracken for abundance correction, human two for functional annotation, and corona for visualization in metagenome data analysis with the eight gigabytes mini database.

Download metagenomics data and databases, set up a conda environment, run quality control with fastqc and multiqc, and perform taxonomic classification with relative abundance analysis.

Assess raw reads with FastQC, aggregate reports with MultiQC, and trim adapters and improve quality with fastp to prepare data for downstream taxonomic classification.

Perform taxonomic classification of metagenomic reads with Kraken2 in an Anaconda environment using k-mers and the mini cracking database, then estimate species abundance with Bracken.

Visualize metagenomics taxonomic results using Corona, import taxonomy, update taxonomy databases, and generate a taxonomic chart to explore species and scores.