Hands-On RNA-Seq Analysis Crash Course: From FASTQ to DEGs

Discover linux basics for bioinformatics, including the command line, file management, and scripting. Learn about Ubuntu and WSL, cron and Slurm for workflow automation, and tools like Hisat2 and Maxquant.

Set up the Windows Subsystem for Linux on Windows, install Ubuntu, configure admin PowerShell, install and update packages, enable GUI apps with X11, and manage environments with conda.

Master basic linux commands for bioinformatics, including file and directory management with pwd, ls, cd, mkdir, and rm, and tasks like copying, moving, and viewing fastq and bam data.

Navigate the linux file system to organize bioinformatics data on AWS, mastering absolute and relative paths and essential commands like pwd, ls, and cd.

Learn linux text processing to extract, filter, and automate bioinformatics data from fasta, gff/gtf, and sam files using grep, awk, cat, and sort.

learn to compress and archive bioinformatics data on linux using gzip, gunzip, tar, tar.gz, and zip. apply these tools to reduce storage, protect fastq files, and enable efficient data transfer.

Learn RNA sequencing data analysis from raw reads to differential expression, covering RNA seq basics, single-end and paired-end reads, quality control, alignment, and gene expression counting in R.

Download paired-end Drosophila melanogaster RNA-seq fastq reads and the Ensembl genome and GTF, then process with pre-processing, trimming, alignment, BAM sorting, and featureCounts quantification.

Preprocess RNA-Seq data by performing quality control with FastQC and trimming with FastP to retain high-quality reads for downstream analysis.

Learn to index the reference genome and gene annotations, align RNA-seq reads with bwa, convert sam to bam, and prepare data for downstream read counting.

Quantify RNA-seq reads by counting aligned bam reads with feature counts, using a gtf annotation and subread. Prepare for deseq2 differential expression analysis in R to identify DEGs.

Explore how to use R for bioinformatics, from installing R and RStudio to loading libraries, understanding data structures, and applying Bioconductor packages for visualization and differential expression analysis.

Install R from CRAN and set up RStudio across Windows, macOS, and Ubuntu, then create a new project and enable optional Git version control for bioinformatics workflows.

Install and manage R packages using CRAN, Bioconductor, and GitHub, solve common installation issues, and explore visualization with ggplot2 and differential expression tools such as Deseq2 and edge R.

Perform differential expression analysis in R with raw counts and metadata. Compare cancer versus normal, normalize, apply DESeq2, and visualize log2 fold changes and p-values with volcano plots.

Perform functional enrichment analysis in R by linking differential expression to gene ontology and pathway enrichment, and visualize results to identify biomarkers.