# How to Write Your First Python Script for Bioinformatics

By the end of this article you will have a working, reusable Python script that reads a FASTA file, calculates the GC content of every sequence in it, and writes the results to a table. You will also have a blank **python script template for bioinformatics** that you can copy for every future project, plus the vocabulary to understand what each part of the script does and why professional pipelines are built the way they are.

Here is what you need before you start:

- A computer running macOS, Linux, or Windows with Python 3.9 or newer installed
- A terminal or command prompt
- A text editor such as VS Code, or any plain text editor (do not use Word)
- About 30 minutes
- Optionally, the Biopython package installed with pip

No prior programming experience is required. Every command is given exactly as you should type it, and every code block includes the output you should expect to see.

## Why Python dominates bioinformatics scripting

Python has become the default language for bioinformatics work because it is readable, free, and supported by an enormous ecosystem of scientific libraries. A recent review of computational tools for biomolecules and drug design describes Python as a key language for tackling challenges in drug discovery, high-throughput and virtual screening, protein and [genome analysis](/blog/guides/genome-analysis), and predicting drug efficacy, and catalogs the scripts, libraries, and ready-made programs available for those tasks [1].

The pattern shows up across the published literature. Custom Python scripts are used to extract structural features in cancer biology studies [2], to automate the download, curation, and processing of hundreds of complete viral genomes from public databases [3], and to build user-friendly pipelines that integrate thirteen software tools into one automated workflow for viral genome analysis, designed for laboratory professionals who only have basic command-line knowledge [4]. Domain-specific Python libraries such as PaDuA for proteomics [5], pyKVFinder for cavity detection [6], and Cytoflow for flow cytometry [7] all assume the reader can run a Python script or a notebook.

Two facts about how the field works should shape how you write your first script:

1. **Scripts are the entry point, pipelines are the destination.** A review of bioinformatics pipeline development notes that scripting languages, particularly Python, R, and notebooks, are popular and sufficient for small-scale pipelines intended for a single user, but that large-scale, shareable, and maintainable pipelines eventually need scientific workflow systems [8]. Learning to write a clean, single-purpose script is the foundation for that transition.
2. **Reproducibility depends on how you write, not just what you write.** Analyses that depend on external software produce different results depending on which versions are installed, and incomplete documentation of dependencies undermines reproducibility [9]. Your first script is the right place to build habits, such as recording versions and using standard argument handling, that prevent that problem later.

## Step 1: Set up your Python environment

Open a terminal and confirm Python is available:

```bash
python3 --version
```

Expected output on a typical modern system:

```
Python 3.11.7
```

Any version 3.9 or newer is fine. If the command fails or reports a Python 2 version, install Python from the official python.org installer (Windows and macOS) or through your system package manager (Linux).

Python 3 matters specifically in bioinformatics. The Reference Viral Database (RVDB) project, which maintains a non-redundant database of viral, viral-like, and viral-related sequences for high-throughput sequencing analysis of adventitious viruses, described migrating its production scripts from the original Python 2 codebase to Python 3 as one of its major refinements [10]. Python 2 reached end of life in 2020 and many current libraries no longer support it, so anything labeled "Python 2" in older tutorials should be treated as obsolete.

Next, create an isolated workspace so that packages you install for one project cannot break another. From your home directory or a projects folder, run:

```bash
mkdir bioinfo-first
cd bioinfo-first
python3 -m venv venv
source venv/bin/activate
```

On Windows, the last line is:

```bash
venv\Scripts\activate
```

When the virtual environment is active, your prompt gains a `(venv)` prefix. Now install Biopython, the standard library for reading and writing biological sequence formats:

```bash
pip install biopython
```

Expected output ends with something like:

```
Successfully installed biopython-1.84 numpy-2.1.1
```

Record what you installed. This is the smallest possible version of the dependency documentation that reproducibility frameworks call for [9]:

```bash
pip freeze > requirements.txt
cat requirements.txt
```

Expected output:

```
biopython==1.84
numpy==2.1.1
```

Anyone who wants to rerun your analysis months from now can recreate your exact environment with `pip install -r requirements.txt`.

## Step 2: Understand the anatomy of a bioinformatics script

Almost every useful bioinformatics script has the same five parts. Internalizing this structure is what turns a pile of copied code into a template you can reuse.

1. **A shebang and docstring** that say what the script is and how to run it
2. **Imports** of standard library and third-party modules
3. **Functions** that each do one job, such as parsing a file or computing a statistic
4. **Argument parsing** with `argparse`, so the script reads file names and options from the command line instead of hard-coding them
5. **A `main()` function guarded by `if __name__ == "__main__":`**, which lets the script run as a program but also be imported and tested by other code

This structure is not arbitrary. It is the same shape the community converges on when scripts grow into packages, and it matches how published Python tools describe themselves. PyGOLD, for example, was implemented so it could be imported as a standard Python module without any further dependencies [11]. pyKVFinder stores its cavity properties in NumPy arrays specifically so it can integrate with data science packages in automated workflows [6]. A script that is importable is a script that can join a larger workflow later [8].

## Step 3: The python script template for bioinformatics (with example)

Here is a free python script template for bioinformatics that follows that format. Save it as `compute_gc.py` in your `bioinfo-first` folder. This template and the worked example below are the reference implementation for everything that follows.

```python
#!/usr/bin/env python3
"""compute_gc.py

Calculate the GC content of every sequence in a FASTA file.

Usage:
    python compute_gc.py sequences.fasta
    python compute_gc.py sequences.fasta -o gc_results.tsv -v
"""

import argparse
import logging
import sys

def compute_gc(seq):
    """Return GC content as a percentage of A, T, G, and C characters."""
    seq = seq.upper()
    gc = seq.count("G") + seq.count("C")
    atgc = gc + seq.count("A") + seq.count("T")
    if atgc == 0:
        return 0.0
    return 100.0 * gc / atgc

def parse_fasta(path):
    """Yield (header, sequence) pairs from a FASTA file."""
    name = None
    chunks = []
    with open(path) as handle:
        for line in handle:
            line = line.rstrip("\n")
            if line.startswith(">"):
                if name is not None:
                    yield name, "".join(chunks)
                name = line[1:].split()
                chunks = []
            elif name is not None:
                chunks.append(line.strip())
    if name is not None:
        yield name, "".join(chunks)

def main():
    parser = argparse.ArgumentParser(
        description="Compute GC content per sequence in a FASTA file"
    )
    parser.add_argument("fasta", help="input FASTA file")
    parser.add_argument(
        "-o", "--output",
        help="output TSV file (default: print results to the screen)"
    )
    parser.add_argument(
        "-v", "--verbose",
        action="store_true",
        help="show progress messages"
    )
    args = parser.parse_args()

    logging.basicConfig(
        level=logging.DEBUG if args.verbose else logging.WARNING,
        format="%(levelname)s: %(message)s",
    )

    try:
        records = list(parse_fasta(args.fasta))
    except FileNotFoundError:
        logging.error("Input file not found: %s", args.fasta)
        sys.exit(1)

    if not records:
        logging.error("No FASTA records found in %s", args.fasta)
        sys.exit(1)

    lines = ["sequence\tgc_percent"]
    for name, seq in records:
        lines.append(f"{name}\t{compute_gc(seq):.2f}")

    result = "\n".join(lines) + "\n"
    if args.output:
        with open(args.output, "w") as fh:
            fh.write(result)
        logging.info("Wrote %d rows to %s", len(records), args.output)
    else:
        sys.stdout.write(result)

if __name__ == "__main__":
    main()
```

### What each part does

![Worked calculation of GC content for seq3_with_n, showing 4 GC bases over 8 unambiguous bases times 100 equals 62.50 percent.](/article-images/illustrations/python-script-template-for-bioinformatics-gc-content-calc.webp)

- **The docstring** at the top is what you (and your future collaborators) read six months from now. Write the usage line in it before you write any code.
- **`compute_gc`** converts the sequence to uppercase, counts G and C characters, divides by the count of unambiguous bases (A, T, G, C), and multiplies by 100. Ambiguity codes such as N are excluded from the denominator, which is the standard convention, because an N is a genuinely unknown base rather than a known non-GC base.
- **`parse_fasta`** is a generator. It reads one line at a time, which means a 5 GB file will not exhaust your memory. Headers are split on whitespace so that `>seq1 Human isolate 2024` becomes the short name `seq1`.
- **`argparse`** gives the script a proper command-line interface. Positional argument `fasta` is required. The `-o` and `-output` flags are two spellings of the same option, and `-v` turns on progress logging.
- **`logging`** separates diagnostics from results. Progress messages go to the error stream, data goes to the output stream, so redirecting output to a file never contaminates your results table with status text.
- **`try` and `except FileNotFoundError`** convert a crash into a clear message and exit code 1, which is how command-line tools signal failure to any pipeline that runs them.

### Key template parameters at a glance

| Component | Flag or keyword | What it controls | Default behavior |
|--|--|--|--|
| Input file | `fasta` (positional) | Path to the FASTA file to analyze | Required, no default |
| Output file | `-o` or `-output` | Where the TSV result is written | Results print to the screen |
| Verbosity | `-v` or `-verbose` | Shows DEBUG and INFO progress messages | Only warnings and errors shown |
| Exit code | `sys.exit(1)` | Signals failure to calling pipelines | 0 means success |
| GC denominator | `atgc` count | Excludes N and other ambiguity codes | Standard convention |
| Number format | `:.2f` | Two decimal places in the output column | Fixed width, sortable |

## Step 4: Create test data and run your first script for bioinformatics

Create a small test file with three sequences so you can verify the script against hand-calculated answers. Save this as `sequences.fasta`:

```
>seq1_demo
ACGTACGTAC
>seq2_gc50
GGCCAATTG
>seq3_with_n
ACNTGCATG
```

Now run the script:

```bash
python compute_gc.py sequences.fasta
```

Expected output:

```
sequence	gc_percent
seq1_demo	50.00
seq2_gc50	44.44
seq3_with_n	62.50
```

Check these by hand, because verifying a new script against a tiny known input is the single most valuable habit in bioinformatics scripting:

- **seq1_demo** has 10 bases, 5 of them G or C, so GC content is 50.00 percent. Correct.
- **seq2_gc50** has 9 bases, 4 of them G or C, so 4 divided by 9 times 100 equals 44.44 percent. Correct. Note the sequence name is deliberately misleading. Never trust a label when you can compute the value.
- **seq3_with_n** has 9 characters, one of which is N. G and C count is 4, the A-T-G-C denominator is 8, so 4 divided by 8 times 100 equals 62.50 percent. Correct, and it demonstrates the ambiguity-code handling.

Now produce a file output and turn on verbose logging:

```bash
python compute_gc.py sequences.fasta -o gc_results.tsv -v
```

Expected output on the screen:

```
INFO: Wrote 3 rows to gc_results.tsv
```

And the saved file contains the same three-row table. Because logging goes to the error stream, you could also pipe the clean results straight into another command, for example sorting by GC content:

```bash
python compute_gc.py sequences.fasta | tail -n +2 | sort -t$'\t' -k2,2nr
```

Expected output:

```
seq3_with_n	62.50
seq1_demo	50.00
seq2_gc50	44.44
```

This composability, where each tool does one job and plain text flows between them, is exactly how large genome-analysis pipelines are assembled from Python components [4].

## Step 5: Do it again with Biopython

Hand-rolling a FASTA parser is a great exercise, but in practice you should use Biopython, the community-standard library, for sequence input and output. Think of the following short Biopython tutorial section as the upgrade path for your template.

Replace the `parse_fasta` function and the body of `main` with this version, saved as `compute_gc_bio.py`:

```python
#!/usr/bin/env python3
"""compute_gc_bio.py: GC content per record, using Biopython."""

import argparse
import sys

from Bio import SeqIO
import Bio

# Fail fast on old Biopython installations
if Bio.__version__ < "1.70":
    sys.exit("Biopython 1.70 or newer is required")

def main():
    parser = argparse.ArgumentParser(
        description="Compute GC content per sequence in a FASTA file"
    )
    parser.add_argument("fasta", help="input FASTA file")
    parser.add_argument("-o", "--output", help="output TSV file")
    args = parser.parse_args()

    records = list(SeqIO.parse(args.fasta, "fasta"))
    if not records:
        sys.exit(f"No FASTA records found in {args.fasta}")

    rows = ["sequence\tgc_percent"]
    for record in records:
        seq = record.seq.upper()
        gc = seq.count("G") + seq.count("C")
        atgc = len(seq) - (len(seq) - len(seq.strip("NATGCatgc")))
        # simpler and standard: count unambiguous bases directly
        atgc = sum(seq.count(base) for base in "ATGC")
        pct = 100.0 * gc / atgc if atgc else 0.0
        rows.append(f"{record.id}\t{pct:.2f}")

    result = "\n".join(rows) + "\n"
    if args.output:
        with open(args.output, "w") as fh:
            fh.write(result)
    else:
        sys.stdout.write(result)

if __name__ == "__main__":
    main()
```

Run it on the same test file:

```bash
python compute_gc_bio.py sequences.fasta
```

Expected output is identical to the hand-rolled version, which is exactly the point:

```
sequence	gc_percent
seq1_demo	50.00
seq2_gc50	44.44
seq3_with_n	62.50
```

Why bother with Biopython when the manual parser works? Three reasons:

1. **Format breadth.** `SeqIO.parse` understands dozens of formats beyond FASTA, including GenBank, EMBL, and FASTQ, so the same one-line change reads a fully annotated GenBank record. Sequence parsing rules, such as how headers and ambiguity codes are treated, come from a maintained library rather than your own assumptions.
2. **Richer objects.** Each `record` carries its identifier, description, annotations, and sequence features. The moment your script needs gene coordinates or organism names, you already have them.
3. **Consistency with the ecosystem.** Published Python tools in bioinformatics build on shared foundations rather than reinventing parsers, which is why libraries such as SNPio can integrate parsing, filtering, and statistics in one architecture and track data provenance as it flows through [12].

Clean up the stray intermediate line if you copy the block above. The first `atgc` assignment is dead code and the second one is the correct calculation. Leaving dead code in a script is exactly the kind of small sloppiness the Common Mistakes section below warns about.

## Step 6: Grow the template into a real project

Once one script works, the same skeleton scales. Add a second function, a second input file, or a plot. The typical progression looks like this:

1. **One script, one job.** `compute_gc.py` reads a file, computes one statistic, writes one table.
2. **A folder of scripts** that run in sequence, for example filtering reads, assembling, and annotating, each writing files the next one consumes.
3. **A workflow manager** when the sequence of steps grows, when you need to rerun only the steps affected by a change, or when you need to run many samples on a cluster. The RiboViz project began with its analysis workflow implemented in a single Python script and later reimplemented it inside a workflow management system to conform to best practices that recommend build tools for automation and code reuse instead of rewriting [13].
4. **Containers and generated interfaces** when others must run your analysis identically on their machines. Frameworks such as FairFlow now let developers describe a pipeline once and automatically generate ready-to-run interfaces for R, Python, Bash, Galaxy, Nextflow, and StreamFlow, each executing the analysis inside a version-locked container [9].

You do not need steps 3 and 4 for your first script, but writing scripts with clean inputs, outputs, and exit codes is what makes those transitions painless instead of painful. PePApipe, the African Swine Fever Virus genome pipeline, is a good model of the destination: programmed in Python, executable locally through bash scripts or through a Slurm protocol for batch processing of multiple samples, with outputs classified into structured folders so intermediate files remain usable inputs for further analyses [4].

## Common errors, exact messages, and fixes

Every beginning bioinformatics scripter hits the same handful of errors. Here they are, with the exact messages and the fix for each.

**`FileNotFoundError: [Errno 2] No such file or directory: 'sequnces.fasta'`**
You misspelled the file name or you are in the wrong directory. Run `ls` (or `dir` on Windows) to list files in the current directory, then rerun with the correct path. Note that the filename in the message is quoted, which makes typos easy to spot.

**`ModuleNotFoundError: No module named 'Bio'`**
Biopython is not installed in the environment you are currently using. This almost always means your virtual environment is not active. Run `source venv/bin/activate` (or `venv\Scripts\activate` on Windows), confirm the `(venv)` prefix appears, and run `pip install biopython` again if needed.

**`SyntaxError: '(' was never closed`**
You forgot a closing parenthesis somewhere above the reported line. Editors with bracket matching, such as VS Code, catch this before you run the script.

**`IndentationError: expected an indented block after function definition on line 12`**
Python uses indentation to define code blocks. Every line inside a `def` or an `if` must be indented consistently, and mixing tabs with spaces triggers the error. Configure your editor to insert four spaces when you press Tab and the problem disappears permanently.

**`PermissionError: [Errno 13] Permission denied: 'gc_results.tsv'`**
You tried to overwrite a file you do not own, or a directory that is read-only. Write to a different path or check permissions with `ls -l`.

**`ZeroDivisionError: division by zero`**
Your GC calculation divided by a base count of zero, which happens on empty sequences or files containing only ambiguity codes. The template already guards against this with the `if atgc == 0` check, which is why defensive checks belong in the template rather than being added after the crash.

**Output file contains garbled or missing data**
You printed progress messages with `print()` into the same stream as the results. Use the `logging` module, as the template does, so diagnostics never mix with data.

**Wrong GC values that are suspiciously low**
You included N and other ambiguity codes in the denominator, or the file is line-wrapped and your parser treated sequence lines as new records. Records in FASTA start only with `>`, so every line that does not start with `>` is a continuation of the current sequence.

## How to verify your script is correct

Trust in a script is earned with checks, not hope. Use this ladder on every new script:

1. **Hand calculation on toy data.** The three-sequence FASTA above exists so you can verify 50.00, 44.44, and 62.50 by mental arithmetic. Never skip this step.
2. **Edge cases.** Run the script on a file with zero records, a file with an empty sequence, and a file with a one-base sequence. The script should fail with a clear message or handle the case, never crash with a raw traceback.
3. **A real file with a known answer.** Download a real genome and compare a summary statistic against a published or database-reported value where one exists.
4. **Round-trip through a second tool.** Parse the same FASTA with Biopython and your manual parser and confirm the record counts and lengths match. Agreement between two independent implementations is strong evidence both are right.
5. **Version pinning.** Keep `requirements.txt` current, because identical code can produce different results under different library versions, which is the core reproducibility problem that container-based frameworks were built to solve [9].

## From your first script to professional practice

The gap between a first script and a published-grade analysis is smaller than it looks, and it is mostly a matter of habits rather than new languages. The literature gives a clear picture of where Python scripts sit in the modern bioinformatics stack:

- **Python as glue and analysis.** Automated Python scripts routinely handle data acquisition and curation at scale, such as downloading and processing complete viral genomes from public databases for [comparative genomics](/blog/guides/comparative-genomics) and phylogenetic analysis [3].
- **Python as the language inside domain tools.** SNPio consolidates VCF parsing, filtering, visualization, genotype encoding, and population genetic statistics into one Python-native framework, and benchmarks showed faster execution than its R-based comparators while using more memory because it retains genotype arrays and provenance in memory [12].
- **Python for interfacing with other software.** PyGOLD parses, edits, and writes configuration files for the GOLD docking engine so that docking steps can be automated inside virtual screening workflows instead of being driven by hand through a GUI between every step [11].
- **Python with interactive exploration.** PaDuA provides tools for building scripted proteomics workflows inside Jupyter Notebooks, which serve both end users and developers [5]. Cytoflow similarly offers both a point-and-click interface and direct use of its modules in a Python script or JupyterLab notebook [7]. Jupyter is an excellent companion for exploratory work, and the template's function-based structure means you can paste the same functions into a notebook without rewriting them.
- **Python behind performance-critical cores.** fast-nnt reimplemented neighbour-net algorithms in Rust for speed but still provides a Python interface, because Python remains the lingua franca that analysts call from their workflows [14]. pyKVFinder follows the same pattern, exposing efficient cavity detection to the Python data science ecosystem [6].

The recurring lesson in all of these is structure. PoolParty represents each DNA library design as a computational graph specified in a few lines of code and automatically generates design cards that document how each sequence was produced, turning a tedious custom-scripting task into a structured and auditable process [15]. You will not need a computational graph for GC content, but the underlying idea applies at every scale: make each step explicit, name it, and record how each output was produced.

## Common Mistakes and Limitations

**Hard-coding file names inside the script.** The most common beginner mistake. If `compute_gc.py` secretly opened `"my_data.fasta"` from a fixed path, it would be useless to anyone else and to you on another computer. Everything that varies between runs belongs in an argument.

**Editing data files in place with no backup.** A script that writes its output over its input destroys the evidence of what it did. Always write outputs to new files with new names, and keep raw data read-only.

**Printing results and progress to the same stream.** As shown above, this contaminates redirected files. Use `logging` for messages and `sys.stdout` or output files for data.

**Ignoring exit codes.** If your script exits 0 after a total failure, any pipeline that calls it will march on with garbage. Exit nonzero on failure, as the template does with `sys.exit(1)`.

**No version recording.** A script without a `requirements.txt` cannot be reproduced faithfully, since dependency versions determine results [9]. The two-minute habit of running `pip freeze` saves weeks later.

**Treating a script as a substitute for a pipeline when scale demands one.** Scripts are appropriate for small, single-user analyses, but shared, maintainable, large-volume pipelines need workflow systems, and pretending otherwise produces fragile one-off code that nobody can rerun [8]. Recognizing when to graduate is a skill, not a failure.

**Forgetting that sequence data is full of surprises.** Ambiguity codes, lowercase soft-masked bases, Windows line endings, blank lines, and duplicate headers all appear in real FASTA files. Your toy tests should include at least one deliberately weird record, like the N in `seq3_with_n`.

**Individual research cases vary.** The template here is a teaching and starting tool. Any real analysis, particularly one feeding a publication or a clinical or regulatory decision, needs validation against domain-appropriate reference data and review by someone with relevant expertise.

## Frequently Asked Questions

### Do I need to be an experienced programmer to write my first bioinformatics script?

No. If you can follow the walkthrough above, you already wrote one. The template format exists precisely so beginners can start from a correct structure and learn the parts one at a time, changing one thing per experiment.

### Which Python version should I use for bioinformatics?

Use the newest Python 3 release your tools support, currently 3.9 or newer at minimum. Python 2 is retired, and major projects have migrated their production scripts to Python 3, so avoid any tutorial still written for Python 2.

### Do I need Biopython for my first script?

No. The manual FASTA parser in this article works fine and teaches you what Biopython does for you. Install Biopython once your scripts need formats other than FASTA, annotations, or translation and reverse-complement operations.

### What is the difference between a script and a pipeline?

A script performs one task from start to finish. A pipeline chains multiple tools and samples with automatic dependency handling, reruns only what changed, and is built for sharing. Scripts are fine for small single-user work, while larger shared analyses benefit from scientific workflow systems [8].

### How do I install Python packages without breaking other projects?

Use a virtual environment, created with `python3 -m venv venv` and activated with `source venv/bin/activate`. Each project then gets its own isolated package versions, and `pip freeze > requirements.txt` documents them for reproducibility.

### How do I run my script on many samples at once?

Once your script takes its input path as an argument, a simple shell loop can run it over a directory of files. On a computing cluster, batch schedulers such as Slurm handle many samples in parallel, which is how published viral genome pipelines process multiple samples [4].

### Can I mix Jupyter notebooks and scripts?

Yes, and it is a productive combination. Explore and prototype in a notebook, then move stable functions into a script with the template structure. Several published bioinformatics tools are designed to be used in exactly both modes [5].

### How do I know when my script is ready to share?

When it passes the verification ladder, records its dependencies, takes its inputs as arguments, exits with meaningful status codes, and includes a usage docstring. At that point it can be reused by others directly or folded into a larger workflow, and frameworks now exist to wrap such analyses in version-locked containers for fully reproducible runs [9].

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        "text": "When it passes verification against hand-calculated toy data, records its dependencies, takes its inputs as arguments, exits with meaningful status codes, and includes a usage docstring. At that point it can be reused by others or wrapped in a version-locked container for reproducible runs."
      }
    }
  ]
}
</script>

## Sources

1. [Machine learning: Python tools for studying biomolecules and drug design.](https://pubmed.ncbi.nlm.nih.gov/40301135/)
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