Hello! I am Da Bai Wang Wang~For all the friends engaged in plant molecular biology and genomic analysis, have you heard your seniors say: “Want to learn bioinformatics analysis? First, get a grasp of Linux!”
When you first hear the term “Linux”, does it feel a bit overwhelming? It seems like a tool exclusive to programmers, and not very relevant to our work in genomic and transcriptomic analysis?
Today, let’s clarify in the simplest terms: What exactly is Linux? Why has it become the standard entry point for bioinformatics? How should beginners choose and get started?
01 First, understand: What exactly is Linux?
Let’s define Linux: it is an open-source, multi-user, multi-tasking operating system, comparable to the commonly used Windows and macOS—it’s a “low-level manager” that manages computer hardware (CPU, memory, hard drive) and software (analysis tools, scripts).
However, compared to Windows and macOS, it has three core differences, which make it a favorite among researchers:
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Open Source and Free: The source code is completely open, anyone can modify and distribute it, allowing installation on laboratory computers and servers without spending a dime, which is very friendly for budget-tight research groups;
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Command Line is King: Although there is a graphical interface, the core power lies in the “command line”—a single line of code can perform batch operations, which is crucial for processing sequencing data from dozens or hundreds of samples;
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Stability: It can run continuously for long periods without crashing, and when processing dozens of GB of genomic data, it won’t freeze or crash like some systems.
To put it simply: if Windows is like a “fully furnished apartment” with everything set up but no flexibility in layout; Linux is like a “bare shell house” with a solid foundation, where you can install any tools and optimize processes as you wish, offering maximum flexibility.
02 Core Question: Why must biologists use Linux?
At the end of the day, we researchers only care about whether it’s useful. The value of Linux in plant molecular biology research is mainly reflected in these four “irreplaceable” aspects:
1. Over 90% of bioinformatics tools only recognize Linux
The core tools we use for analysis, such as BWA for genome alignment, Samtools for processing BAM files, Trinity for transcriptome assembly, and plant-specific tools like RepeatMasker for repetitive sequence annotation and ALLHiC for polyploid assembly—are almost all developed for Linux.
For example, a student wanted to run Trinity assembly for a plant transcriptome on Windows, spent three days troubleshooting, and spent two days just resolving Perl dependency issues, only to end up with an error; switching to Linux, with a single command “conda install -c bioconda trinity”, it was installed in 10 minutes and ran successfully.
This doesn’t mean Windows is completely unusable, but it requires going through virtual machines or subsystems, which complicates configuration and reduces efficiency (for example, the same analysis takes 48 hours on a Windows virtual machine, while Linux completes it in 24 hours).
2. Batch processing samples can be over 10 times more efficient
Those engaged in plant breeding and population genetics know that one experiment often requires processing dozens or hundreds of samples—like RNA-seq data from 100 leaves of a certain species. If using Windows with a graphical interface, opening software one by one for quality control and alignment could take several sleepless nights.
But in Linux, a single command can handle batch operations. For example, using fastp for quality control, just this line of code: for file in *.fq; do fastp -i $file -o clean_$file; done.
After hitting enter, the computer will automatically loop through all samples, allowing you to eat and rest while waiting for the “clean data” to be ready. This level of automation is something graphical interfaces can never match.
3. Handling large files without issues, seamless server integration
How “terrifying” is plant sequencing data? Sometimes, a single sample of whole genome sequencing can exceed 10GB, and transcriptomes can be 10-20GB per sample. Windows often struggles with such large files, leading to issues like “unable to read” or “freezing and crashing”; whereas Linux has inherent advantages in reading and writing large files and memory management.
More importantly, for large-scale analyses, we ultimately need to use laboratory Linux servers or HPC clusters. If you use Windows locally, when transferring scripts to the server, you have to change file paths (from C:\ to /home/) and adjust line endings (from CRLF to LF), which can take half a day just for debugging; whereas if you use Linux locally, you can copy scripts directly to the server and run them with zero adaptation cost.
4. It meets the specific needs of plant research
Everyone understands the “pits” of plant genomes—highly repetitive sequences and polyploidy (like apples and pears). To achieve good assembly and annotation, specialized tools are essential.
Additionally, for batch downloading data from the NCBI SRA database, using the prefetch command in Linux allows you to copy a string of SRR numbers for bulk downloading, with support for resuming interrupted downloads; if you use Windows and click manually, if the network drops halfway, you have to start over, which is incredibly frustrating.
03 Must-Read for Beginners: How to Choose a Linux Version? How to Get Started?
Many friends find Linux difficult, but it’s often because they haven’t chosen the right version or method. Here are some key points for beginners:
1. Choosing the wrong version can waste all your efforts (Beginners should choose these two)
Linux is not “one system” but a collection of “distributions”, similar to how mobile systems have iOS and Android, and Android has Xiaomi MIUI and Huawei EMUI. Beginners don’t need to be overwhelmed; just focus on these two:
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Ubuntu (20.04/22.04 LTS): The best choice for beginners! It has the most community resources, is easy to install, supports dual systems and Windows Subsystem for Linux (WSL2); installing tools is as simple as “sudo apt-get install tool_name”, and if you encounter problems, a quick search will yield plenty of solutions.
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CentOS Stream: If you often work with laboratory servers, choose this! It has the highest compatibility with most HPC cluster systems, ensuring zero obstacles in script migration.
2. You don’t need to be an expert; knowing these will suffice
Getting started with Linux doesn’t require aiming to “master the operating system”; as researchers, we just need to be functional. Focus on learning these three categories:
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Basic Commands: cd (change directory), ls (list files), cp (copy), mv (move), grep (filter), awk (data processing)—mastering these will cover 80% of file operations;
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Tool Installation: Learn to use conda (the “universal installer” for bioinformaticians), which can install most bioinformatics tools with a single command;
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Simple Scripts: Learn basic Bash loops (like the for loop for batch processing samples mentioned earlier), and combine it with Python for data processing, which will cover most routine analysis needs.
3. Environment Setup: Recommended Methods for Beginners
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WSL2 for Windows Users: Without reinstalling the system, install a Linux subsystem in Windows 10/11, allowing you to work in Windows and seamlessly switch to Linux for analysis—perfect!
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For Stability, Choose Dual Boot: Install a dual system of Windows + Ubuntu on your computer, selecting the system at boot time, using Linux for analysis and Windows for daily tasks, with no interference.
In Conclusion
I hope we do not fear the difficulties of learning new things, taking it step by step, without too much burden, because we learn Linux not to become programmers, but to master an efficient research tool.