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Courses@CRG: Nextflow 2023

Courses@CRG: Containers & Nextflow 2022

Courses@CRG: Containers & Nextflow

Benedek Dankó

Grygoriy Zolotarov

Miguel Cortés

Zachary Compton

The course is organized in 3 modules: * Module 1: * * Introduction to R and RStudio * Paths and directories * R basics (syntax, objects) * Functions in R (run, get help) * R scripts * Data types & data structures (vector, matrix, data frame, factor) * Module 2: * * Input / output (read in and write out files) * Library and packages (install, load) * Regular expressions (with grep and gsub) * Conditional statement (if statements) * Repetitive execution (for loops) * Module 3: * * "Base" plotting (scatter plots, histograms, barplots, pie plots, boxplots) * Plotting with ggplot2 (scatter plots, histograms, barplots, dot plots) * Saving plots in files.

* Module 1 - DNA, gene, and protein sequences * Module 2 - Sequence comparison * Module 3 - Genome sequences and annotations * Module 4 - Gene and protein function * Module 5 - Protein-DNA interactions * Module 6 - Gene expression * Module 7 - Differential gene expression * Module 8 - Functional annotation of gene sets

Learning objectives: * Understand the steps from raw reads to expression counts, differential expression and interpretation of gene lists using enrichment analysis * Define a good experimental design, including experimental design, sequencing design, and quality control steps) * Perform quality assessment of RNA-seq data, raw and processed * Understand file formats commonly used in RNA-seq data analysis * Gain an overview on common software tools for RNA-seq data analysis and their limitations * Run RNA-seq pipeline to perform differential expression analysis