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Bioinformatics
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RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
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.
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
* 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
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
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
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
RESOURCES AND TRAININGS ONLINE
Resources and Trainings Online
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