How to Create a README File for Your Bioinformatics Project

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-11-10

A README is the first file most collaborators open and the last file most projects update. For a bioinformatics project, it has to do more than describe the goal. It must tell a new analyst which files are inputs, which file formats are expected, which software versions were tested, which command produces a small test result, and how to tell whether that result is correct. By the end of this guide you will have a README.md that another person can follow from a clean directory, plus a validation routine that catches stale commands and missing dependencies.

You need a text editor, a terminal, Git, and access to the project's environment file or container recipe. A small test dataset helps. You do not need a finished manuscript. The README should be useful on the first day of the project, not pasted in at the end.

A bioinformatics README template is a run contract

A bioinformatics README template should work as a run contract. It states what the project does and what a reader must provide to get a known result. Reproducibility problems often start with undocumented environments and unclear file expectations. CREDO was built to reduce those problems by generating Dockerfiles that store package information and dependencies so a container can be rebuilt from scratch [1]. Your README does not need to generate a container, but it should record the same facts that make a container useful: software names, versions, package sources, and the exact commands that were tested.

A complete README for a bioinformatics project usually answers these questions in this order:

1. What is the project name and one-sentence purpose?
2. What biological question or analysis does the workflow address?
3. What is the current status, such as prototype, validated, or archived?
4. What input files are required, and what formats are they in?
5. What reference files are required, such as a genome FASTA, GFF annotation, or adapter file?
6. What software, programming language, workflow manager, and container are required?
7. What is the shortest command that runs a small test?
8. What parameters matter, what are their defaults, and what range was tested?
9. What output files are produced, where do they appear, and what should a correct result contain?
10. How can a reader validate the result?
11. What versions, commits, container digests, and reference builds were used?
12. What license applies, how should the project be cited, and who should be contacted?

The last two items are easy to skip. Do not skip them. A README without a license is hard to reuse. A README without a citation leaves users guessing how to credit the work. A README without version information becomes misleading as soon as a parameter changes.

Choose a README format and a reusable template

The bioinformatics README template format used by most repositories is Markdown in a file named README.md. Markdown renders on GitHub, GitLab, and many internal code browsers. It supports headings, fenced code blocks, tables, and links without requiring a word processor. Keep a plain-text version only if your group uses a system that cannot render Markdown. Do not maintain two copies of the same documentation unless you have a build step that keeps them synchronized.

A free bioinformatics README template is a scaffold. Replace every placeholder. A template with TODO, <repository-url>, or your-project left in place is worse than a short README with accurate commands because it looks complete while failing on the first run.

Use this structure as the starting point:

# Project name

One-sentence description of the analysis and the biological question.

## Status

Prototype, validated, or archived. State the last tested date.

## Inputs

| File | Format | Description |
| --- | --- | --- |
| sample_R1.fastq.gz | FASTQ, gzip | Read 1, paired-end |
| sample_R2.fastq.gz | FASTQ, gzip | Read 2, paired-end |
| reference.fasta | FASTA | Reference sequences |
| annotation.gff3 | GFF3 | Gene features |

## Requirements

- Linux or macOS
- Nextflow version tested
- Conda environment from environment.yml
- Container image with digest

## Quickstart

git clone <repository-url> cd project conda env create -f environment.yml conda activate project-env nextflow run . -profile test, outdir results_test

## Parameters

List every user-facing parameter, its default, and the tested range.

## Outputs

List output files and what each contains.

## Validation

Give commands and expected checks.

## Versions

Workflow version, commit, container digest, reference build.

## Data availability and privacy

State where data come from and what may not be shared.

## License and citation

Name the license and the preferred citation.

## Contact

Name the maintainer and the issue tracker.

This is a bioinformatics README template example that works for RNA-seq, variant calling, assembly, metagenomics, and many other workflows. Change the input table and output list for your project. Keep the order. Readers expect to find the quickstart before a long methods section.

Build the README in nine steps

The walkthrough below uses a generic workflow project with a Nextflow entry point, a Conda environment, and a Docker image. Replace names and commands with the real ones from your project. The same steps apply if you use Snakemake, WDL, Galaxy, or a collection of shell scripts.

Step 1: Inventory the project and define the run contract

Start in the project root and list what exists. Do not write the README from memory.

cd ~/projects/rnaseq-project
find . -maxdepth 2 -type f | sort | sed 's#^./##' | head -40
git status --short

Expected output is a sorted file list and a short Git status. The exact files differ by project. The output should show the workflow entry point, environment file, container recipe, test data, and output directories. If a file is missing from the inventory, it should not appear in the README as a requirement.

The -maxdepth 2 flag keeps the listing shallow. -type f limits the listing to files. sed 's#^./##' removes the leading ./ so the README examples use clean relative paths. head -40 keeps the first review manageable.

Write the project purpose in one sentence. “Calls germline variants from paired-end FASTQ files and produces a filtered VCF” is a run contract. “A study of genetic variation” is a grant abstract. The README needs the first kind of sentence.

Check the result by asking whether a new analyst can identify the entry point, the environment file, and the test data from the inventory alone. If not, add a short ## Project layout section.

Step 2: Capture software versions and environment details

Record versions in files that can be committed to the repository. Do not rely on the versions installed on your laptop.

conda env export > environment.yml
python --version > versions.txt
fastqc --version >> versions.txt
nextflow -version >> versions.txt

The > operator overwrites versions.txt on the first command. The >> operator appends each later version. On a system with multiple tools, add one line per tool. The expected output resembles:

Python 3.11.9
FastQC v0.12.1
N E X T F L O W  version 24.04.0

The exact numbers will differ. What matters is that the README points to the file and states the tested version. Conda exports include package build strings by default. That is useful for exact reconstruction on the same operating system. If the environment file is meant for cross-platform use, describe the tested platforms and keep a container as the portable option.

Containerized workflows make version pinning easier to communicate. CREDO stores package information and dependencies in a Github-compatible format so Docker images can be rebuilt from scratch [1]. Theiagen public health workflows use open-source containerized software and WDL to support standardization and interoperability [2]. MStargetR pins third-party tools in version-controlled Docker images and supports Apptainer for high-performance computing [3]. Your README should name the image tag and, when possible, the image digest.

docker inspect --format='{{index .RepoDigests 0}}' project:1.0.0

Expected output is a digest such as:

project@sha256:4f1c2b7a9e8d0c3b6a5f4e2d1c0b9a8f7e6d5c4b3a2f1e0d9c8b7a6f5e4d3c2b

A tag can be moved. A digest identifies a specific image. Put both in the README. State the platform, such as linux/amd64, when the workflow depends on architecture-specific binaries.

Step 3: Describe inputs and distinguish FASTQ from FASTA

Input documentation is where many bioinformatics READMEs fail. “Reads” is not a format. “Genome” is not a reference build. Name the file type, compression, pairing convention, and naming pattern.

FASTA and FASTQ are different formats.

FASTA stores sequences without per-base quality scores. A record begins with a > header line, followed by sequence lines. FASTA is common for reference genomes, transcript sequences, protein sequences, and assembled contigs.

>chr1
ACGTACGTACGTACGT
>chr2
TTTTCCCCAAAAGGGG

FASTQ stores sequencing reads with a quality score for each base. Each record normally has four lines: an @ header, the sequence, a + separator, and an ASCII-encoded quality string. The quality string has the same length as the sequence. A common encoding is Phred+33.

@A00123:45:H7J2LDSX2:1:1101:1000:1000 1:N:0:ACGT
ACGTACGTACGTACGT
+
IIIIIIIIIIIIIIII

The fastq vs fasta distinction belongs in the README because it changes every downstream command. A FASTQ file can be checked for gzip integrity and read structure. A FASTA file can be counted for records. A GFF3 file is a feature annotation format, not a sequence format. Standardized annotation formats integrate with FASTA or GFF so methods and datasets can be compared across studies [4]. AssemblyQC performs file-format validation as part of its assembly quality workflow and reports the methods and checks in an HTML report [5]. Clinical bioinformatics recommendations also emphasize standardized file formats and strict version control [6].

Document the inputs with commands a reader can run:

gzip -t reads_R1.fastq.gz
zcat reads_R1.fastq.gz | head -n 4
grep -c '^>' assembly.fasta

Expected output for the gzip check is silence when the file is valid, or reads_R1.fastq.gz: OK with gzip -tv. The zcat command should show four FASTQ lines. The grep -c command should print the number of FASTA records. A typical result is:

@A00123:45:H7J2LDSX2:1:1101:1000:1000 1:N:0:ACGT
ACGTACGTACGTACGT
+
IIIIIIIIIIIIIIII
42

The 42 is the count of > header lines, not the number of bases. State that in the README so readers do not misinterpret the check.

Step 4: Write a quickstart that runs a small test

The quickstart should be copy-pasteable. It should use a small test dataset that ships with the repository or is downloaded by a documented command. It should finish in minutes, not hours.

git clone <repository-url> project
cd project
conda env create -f environment.yml
conda activate project-env
nextflow run . -profile test --outdir results_test

Expected output begins with a workflow launch message and ends with a completion message:

N E X T F L O W  ~  version 24.04.0
Launching `./main.nf` [...
...
Completed successfully

The -profile test convention is common in Nextflow projects, but it is not universal. If your workflow uses a different test profile name, document it. The -outdir parameter tells the workflow where to write results. If the workflow does not accept that parameter, use the real output flag from your project. Do not invent flags to match a template.

The README should state the expected runtime and approximate resources for the test. “About 3 minutes on 2 CPU cores and 4 GB RAM” is useful. “Fast” is not. State whether the test requires network access. State whether it writes outside the project directory.

Check the result by running the quickstart in a fresh shell. If the command depends on an activated environment from an earlier session, the README is incomplete. If the test data are too large to clone, provide a download command and a checksum.

Step 5: Document parameters and defaults in one table

A parameter table prevents guesswork. It should list the user-facing options, the default, the tested range, and the effect of changing each one. Keep internal developer flags out of the user table.

The table should be specific enough that a reader can predict what changes when a parameter is adjusted. For a variant caller, state the minimum depth and any filtering defaults. For an assembler, state the expected input type and whether it accepts compressed FASTQ. For an annotation workflow, state the model, reference set, and output format because those choices affect traceability and comparison across runs [7].

Write defaults as they appear in the code, not as you remember them. If a default is computed from other values, describe the rule. “Threads default to the number of available CPUs, capped at 8” is better than “threads default to 8.”

Step 6: Document outputs and validation checks

A README should tell the reader what success looks like. List every primary output and one validation command for each.

find results_test -maxdepth 2 -type f | sort
grep -c '^>' results_test/assembly.fasta
sha256sum results_test/assembly.fasta > results_test/assembly.fasta.sha256
cat results_test/assembly.fasta.sha256

Expected output is a short file list, a contig count, and a checksum line:

results_test/assembly.fasta
results_test/assembly.fasta.sha256
results_test/multiqc_report.html
42
9c1f0e2d7b6a5f4e3d2c1b0a9f8e7d6c5b4a3f2e1d0c9b8a7f6e5d4c3b2a1f0e  results_test/assembly.fasta

The checksum is a 64-character SHA-256 digest. It lets another reader compare a regenerated file with the tested output. Checksums are not a substitute for biological validation. They confirm file identity.

AssemblyQC shows a useful pattern: file-format validation, quality measurements, and method descriptions are summarized in a single HTML report with tables and visualizations [5]. Your README can point readers to a report and state which sections to inspect first. If your workflow produces a MultiQC report, say so. If it produces a VCF, state whether the file is filtered and which reference build was used.

Document outputs that are intentionally absent as well. If the workflow does not produce alignments, say that. If intermediate files are deleted by default, say where they would appear if -keep_intermediates is enabled.

Step 7: Record provenance, parameters, and reference builds

Provenance is the record of what produced the result. A README should name the workflow version, Git commit, container digest, reference build, and any configuration file used for the reported run.

git tag -a v1.0.0 -m "Tested README quickstart"
git rev-parse HEAD
docker run --rm project:1.0.0 --help

Expected output includes the commit hash and the container help text. The exact hash differs. Put the commit hash in the README release notes, not in every paragraph.

TRaP encodes complete processing pipelines in a single configuration file so workflows can be shared and rerun [8]. FANTASIA exposes control over model choice, reference composition, lookup parameters, evidence traceability, and output formats [7]. The README should point to the configuration file and explain which values were used for the test. If the workflow has a nextflow.config, config.yaml, or params.yml, name it and show the command that selects it.

For human genomic work, state the reference build explicitly. Clinical bioinformatics recommendations from the Nordic Alliance for Clinical Genomics include adopting hg38 as the reference build and using standardized file formats and strict version control [6]. A README that says “human genome” without a build is ambiguous.

Step 8: Add license, citation, data availability, and contact

A README should tell readers what they may do with the code and how to credit it. Add a LICENSE file and name the license in the README. Add a CITATION.cff file if your repository supports it, or write a short citation block with the project title, authors, version, and repository. Do not invent a DOI. If the project is archived in a repository that issues a DOI, add the DOI after it exists.

State data availability separately from code availability. Some projects can share code but not patient data. Some can share processed counts but not raw FASTQ files. Some can share a small test dataset but not the full cohort. Write those boundaries plainly. If data access requires an application, say where the application goes. If the project uses controlled-access data, say that the README does not grant access.

Public health workflows need auditable results and standardized outputs [2]. A README supports auditability by recording the software, parameters, and output formats. It does not replace a validated quality system. For clinical projects, state the validation status and the quality system in use. For research projects, state that the workflow is not validated for clinical diagnosis.

Step 9: Test the README from a clean directory

The final check is a clean-room run. Use a temporary directory and follow only the README.

tmpdir=$(mktemp -d)
cd "$tmpdir"
git clone <repository-url> project
cd project
conda env create -f environment.yml
conda activate project-env
nextflow run . -profile test --outdir results_test
test -s results_test/multiqc_report.html
echo $?

Expected output ends with 0 when the report exists and is non-empty. A nonzero exit code means the quickstart did not produce the file the README promised.

Check for leftover placeholders:

grep -nE 'TODO|TBD|FIXME|<repository-url>|<version>' README.md

Expected output is empty. If the command prints lines, the README still contains scaffolding. Replace each placeholder before release.

Check that the README does not depend on your home directory:

grep -n '/home/\|/Users/\|C:\\' README.md

Expected output is empty. Absolute paths are a common reason a README works for the author and fails for everyone else. Use relative paths or environment variables.

Finally, read the README as a new user. Start at the top and stop when you cannot continue. Every stop is a missing instruction. Fix it, then run the clean-room test again.

Common errors when readers run your README

These are errors a reader may see when the README skips a dependency or states the wrong command. Quote the message, explain the cause, and give the fix.

bash: conda: command not found

Conda is not on the reader's PATH. The README should either link to the installation instructions for the project's supported Conda distribution or provide a container command that does not require Conda. Do not assume every reader uses the same shell configuration.

PackagesNotFoundError: The following packages are not available from current channels:

The environment file requests a package or version that is not available from the configured channels. The fix is to correct the channel order, pin a version that exists, or use the container image. The README should state the channels and the platform used for testing.

docker: Error response from daemon: pull access denied for project, repository does not exist or may require 'docker login': denied: requested access to the resource is denied.

The image name is wrong, private, or not built locally. The README should state whether the image is public, how to build it, and the exact tag and digest. If the image is private, state the login step.

gzip: reads_R1.fastq.gz: not in gzip format

The file is not gzip-compressed despite its extension, or it is an HTML error page downloaded instead of the data. The fix is to check the file type with file reads_R1.fastq.gz and re-download from the documented source. State the expected compression in the README.

nextflow: command not found

The workflow manager is not installed. The README should list the tested Nextflow version and either provide an installation command or a containerized launch command. A version range without a tested version is not enough.

No such file or directory

This message usually hides the real missing path. Run the command with bash -x or check each path in the README. Replace absolute paths with project-relative paths and state which files the test data must contain.

Common Mistakes and Limitations

The most common mistake is writing the README after the project is “done.” By then the author has forgotten the undocumented environment variables, the manual rename step, and the one sample that needed a different adapter file. Write the quickstart early and run it after every release.

The second mistake is copying a template without changing the commands. A README with a nextflow run command for a Snakemake project is worse than no quickstart. A README with a placeholder repository URL is worse than a short note that says the code is not yet public.

A third mistake is listing software versions without pinning the environment. A list of tool names does not reproduce a run. A container digest or an exported environment file does. CREDO and MStargetR both treat dependency records and container pinning as part of reproducibility, not as an afterthought [1][3].

A fourth mistake is mixing FASTQ and FASTA in the input description. FASTQ carries per-base quality scores. FASTA does not. A workflow that expects raw reads needs FASTQ files, not a reference FASTA. A workflow that expects a reference needs FASTA, not FASTQ. State the role of each file in the input table.

A fifth mistake is documenting only the full dataset. A reader cannot debug a workflow with a 500 GB cohort as the first test. Include a small test dataset with known expected outputs. If the test data cannot be shared, provide a script that generates a synthetic dataset and state its limitations.

A sixth mistake is hiding the output validation. A README that says “check the results” is not actionable. Name the files, give the command, and state the expected count, checksum, or report section. AssemblyQC's HTML report is a useful model because it combines method descriptions, tables, and visualizations in one place [5].

A seventh mistake is using latest as a container tag. A tag can be reassigned. Use a version tag and a digest. If the workflow depends on a database that updates, record the database release date and the download command.

An eighth mistake is forgetting the license. Without a license, reuse is legally unclear even when the code is public. Add a license before you ask others to use the project.

A ninth mistake is treating the README as a substitute for a workflow manager or a validated quality system. A README can describe a reproducible run. It cannot guarantee that the run is correct for every dataset. Clinical bioinformatics recommendations emphasize validation, standardized formats, and version control because documentation alone does not establish analytical validity [6].

A tenth mistake is letting the README drift. When a parameter default changes, update the table. When a container is rebuilt, update the digest. When a release is tagged, update the version block. A stale README is a reproducible failure waiting to happen.

The limitations are practical. A README cannot fix a missing reference file, a restricted dataset, or a platform-specific binary. It cannot replace biological validation. It cannot make a non-deterministic workflow deterministic. It can, however, make the difference between a reader who can run the test and a reader who gives up.

Frequently Asked Questions

What is the best format for a bioinformatics README?

Use Markdown in a file named README.md. Markdown renders on common code hosting platforms and supports headings, code blocks, tables, and links.

What should a minimal bioinformatics README template include?

Include the project purpose, input files and formats, software requirements, a tested quickstart, expected outputs, version information, license, and contact. A short README with accurate commands beats a long README with placeholders.

Should I document FASTQ and FASTA files differently?

Yes. State that FASTQ carries per-base quality scores and is used for raw reads, while FASTA carries sequences without quality scores and is used for references, proteins, or assemblies. Also state compression and pairing conventions.

How do I make my README reproducible?

Pin software versions, provide an environment file or container digest, include a small test dataset, show the exact test command, and list expected outputs with validation checks.

Do I need a Docker container in my README?

Not always. Document the tested environment even if you do not use Docker. If you provide a container, include the image tag, digest, and platform.

How long should a README be?

As short as possible while still letting a new reader run the test. Put the quickstart near the top and move long methods, parameter notes, and background to later sections or separate documents.

Should I include sample data in the repository?

Include a small test dataset when licensing and privacy allow it. Never include patient data or controlled-access data. If sample data cannot be shared, provide a generator script and state what the test does not cover.

How often should I update the README?

Update it at every release and whenever a command, parameter, dependency, container, or output changes. Run the clean-room quickstart before tagging the release.

Sources

1. CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications.
2. Accelerating bioinformatics implementation in public health.
3. MStargetR: a reproducible, containerised workflow for end-to-end targeted (MRM/SRM) mass spectrometry data processing in R.
4. DeNoFo: a file format and toolkit for standardized, comparable de novo gene annotation.
5. AssemblyQC: a Nextflow pipeline for reproducible reporting of assembly quality.
6. Recommendations for bioinformatics in clinical practice.
7. FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.
8. TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems.