Command-line Series Introductory Linux for Bioinformatics

Linux/Unix operating systems are always the first choice of Bioinformaticians to handle and analyze huge biological datasets in a much easier and more efficient manner. Linux allows you to develop and utilize command-line tools for easier and more efficient data analysis of biological nature utilizing the command-line interface of Linux/Unix operating systems. Moreover, Linux makes automated biological analysis easy by providing the ability to create various pipelines so that you don’t have to write the code again and again.

BioCode is offering Introductory Linux Scripting for Bioinformatics course so that you can learn intermediate biological programming on a Linux terminal to efficiently retrieve, manipulate and analyze bioinformatics genomic datasets. Along with this, you’ll learn how to get started with Linux, how to process and pre-process biological data to manipulate and analyze large genomic files and create pipelines to process the datasets.

This course is for absolute beginners in bioinformatics scripting and you don’t require any prior knowledge of Linux or even bioinformatics to get started with this course. In Bioinformatics, the researchers have to work through a huge set of textual data in the form of CSV files, genomic data, tabular data, etc. Such data analysis can be quickly and easily done using the command line or terminal interface of Linux or Unix operating systems. Moreover, it takes a few minutes to complete a task on a Linux OS that you cannot even think of performing on Windows or any other operating system.

This course will include the following sections:

Section 1: Linux

Description: This section will focus on making sure that the students gain an understanding of the Linux operating system and the different functions and commands that can be performed on biological data using Linux.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Discuss Linux Operating System.
2. Print Working Directory in Linux.
3. Change Directories in Linux.
4. Make Directories in Linux.
5. Move Files, Directories, and Data.
6. Find the Installed Programs in Linux.
7. Delete Files and Directories in Linux.
8. Find the Files Created by the User.
9. List Files and Directories on Linux.
10. Pipe and Redirect Data.
11. Visualize and Inspect Text Data.
12. Read the Specified Number of Lines from the Bottom
13. Modify File Statistics and Create Files.
14. See the Statistics of Files & Directories.
15. Retrieve Genome Assemblies.
16. Retrieve Bioinformatics Files.
17. Create and Edit Text Files.
18. Find Sequence Differences in Files.
19. Compress and Archive Files Efficiently.
20. Extract Compressed Content.
21. Create Archives of Genome Data.
22. Find Uncharacterized Proteins in the Human Genome.
23. Sort Data.
24. Find Unique Data Items.
25. See the Statistics of Data Within the File.
26. Copy Files and File Contents.
27. Properly Visualize Delimited Datasets.

The command line, taught for biological data

Almost every bioinformatics pipeline runs on Linux, and almost every general Linux course teaches it with examples that have nothing to do with biology. This course spends its 8 hours 50 minutes on the commands you will actually use, applied to the files you will actually handle — FASTQ, SAM, BED, GTF — rather than on generic system administration.

What you learn

• Getting comfortable in Linux — the filesystem, navigation, permissions, and the small set of commands that carry most of the work.
• Piping and controlling data flow — chaining commands so that large files are processed in a stream rather than loaded whole. This is the idea that makes the command line worth learning.
• Pre-processing biological datasets — inspecting, filtering, cleaning and reformatting sequencing data before it enters a pipeline.
• Processing and analysing biological datasets — extracting what you need from large files and summarising it without opening anything in a spreadsheet.

Why this is worth your time

A genomics file can be tens of gigabytes. Opening it is not an option, and the difference between someone who can answer a question about that file in thirty seconds and someone who cannot is a handful of command-line tools. Once grep, awk, sort and pipes are second nature, a large class of daily problems stops being difficult.

It is also the prerequisite that silently blocks people. Most RNA-Seq, variant calling and ChIP-Seq courses assume comfort at a terminal, and students who skip that foundation struggle for reasons that have nothing to do with the biology.

What you can do afterwards

Navigate a remote server or HPC cluster confidently, inspect and manipulate large sequencing files, build command chains that do real work, and follow any bioinformatics tutorial that assumes a terminal — which is nearly all of them.

Who it suits

Biologists moving into computational work, students starting a bioinformatics programme, and anyone who has been told to “just SSH in” and found that unhelpful. No prior command-line experience is assumed.