Courses@CRG
Decoding Transcriptomes:
A Practical Course on RNA-seq

The assessment will cover only the topics listed below. You should be able to understand, execute, and make simple modifications to commands and R code, but you are not expected to write complete scripts or perform bioinformatics analyses.

1. Linux command line

You should be able to:

• Navigate the filesystem: use pwd, ls, and cd; understand absolute and relative paths, including . and ..
• Manage files and directories: use mkdir, cp, mv, and rm.
• View text files: use cat, head, tail, and less.
• Inspect compressed files: use zcat to view the contents of .gz files without decompressing them.
• Edit text files: use nano or another simple text editor to open, modify, save, and close files.
• Search and manipulate text: use grep, cut, and sort to search for patterns, extract columns, and sort lines.
• Use pipes and redirection: understand |, >, and >> to combine commands and save output.
• Use wildcards: understand * for matching filenames.
• Understand command syntax: recognize commands, options, arguments, and file paths; execute and modify provided commands.
• Recognize common errors: identify problems such as missing files, incorrect paths, or unrecognized commands.

You do not need to learn shell scripting, loops, awk, sed, or advanced Linux administration.

2. R and RStudio

You should be able to:

• Use RStudio: navigate the console, script editor, environment, and plots; execute supplied R code.
• Understand basic R syntax: variables, assignment (<-), numbers, strings, logical values, and comments.
• Work with basic data structures: vectors, matrices, and data frames; understand rows, columns, and dimensions.
• Access and subset data: use [ ], $, and column names to select values, rows, and columns.
• Import data: read CSV and tab-delimited files using supplied commands.
• Inspect and summarize data: use head(), dim(), str(), summary(), mean(), and sum().
• Understand functions: recognize function calls, arguments, and returned values; modify simple arguments in supplied code.
• Work with packages: understand library() and the difference between installing and loading a package.
• Understand basic plots: recognize scatter plots, boxplots, and histograms and interpret their axes.

You do not need to learn programming loops, writing functions, advanced statistics, ggplot2, tidyverse, or Bioconductor before the course.

The course requires basic Linux command-line and R skills. As part of registration, you will complete a short assessment to help us select a group with similar skill levels and adapt the course accordingly. We encourage you to prepare using the recommended learning resources before registering. Please complete the assessment independently, without asking the Internet, AI tools, or external help. We value an accurate picture of your skills more than a high score.

The registration form includes a Linux and R skills assessment, which takes approximately 30–45 minutes to complete.

Before starting, please make sure that:

• You have a terminal available on your computer (Linux, macOS, or Windows with WSL) and can execute basic Linux commands.
• You have R and RStudio installed and ready to use.
• You have downloaded both assessment files (see Download assessment files).
• You have a stable Internet connection and enough uninterrupted time to complete the form.

Important: The registration form may not save your progress. If your connection is interrupted or you close the page, you may lose your answers and need to start again. We recommend reviewing the required skills and completing the suggested preparatory tutorials before opening the registration form.

Before starting registration, download both files using your terminal:

Linux assessment (FASTA file):

wget https://github.com/biocorecrg/RNAseq_coursesCRG_2026/raw/refs/heads/master/assessment/linux_assessment.fasta

or

curl -L -O https://github.com/biocorecrg/RNAseq_coursesCRG_2026/raw/refs/heads/master/assessment/linux_assessment.fasta

R assessment (data file):

wget https://github.com/biocorecrg/RNAseq_coursesCRG_2026/raw/refs/heads/master/assessment/r_assessment.csv

or

curl -L -O https://github.com/biocorecrg/RNAseq_coursesCRG_2026/raw/refs/heads/master/assessment/r_assessment.csv

If you are not yet familiar with all the required Linux and R skills, we recommend the following free online tutorials to help you prepare before registration.

Linux command line

Software Carpentry — The Unix Shell

Recommended lessons:

• Navigating Files and Directories
• Working With Files and Directories
• Pipes and Filters
• Finding Things

R and RStudio

Software Carpentry — R for Reproducible Scientific Analysis

Recommended lessons:

• Introduction to R and RStudio
• Data Structures
• Exploring Data Frames
• Subsetting Data