Contribute to scikit-bio — scikit-bio

New Roadmap of scikit-bio
Scikit-bio is a community-driven open-source software project, and we warmly welcome your contributions!
We are interested in many types of contributions, including feature additions, bug fixes, continuous integration improvements, and documentation/website updates, additions, and fixes. Whether you are a researcher, educator, or developer; whether your interest lies in biology, mathematics, statistics, or computer science; your input is invaluable. You can help making scikit-bio a better software package for our entire community.
This document covers the information you may need to get started with contributing to scikit-bio. In addition, for a broader perspective, we recommend the inspiring guide: How to Contribute to Open Source.
Visit our GitHub repository: scikit-bio/scikit-bio for the source code of scikit-bio. You will need a GitHub account to interact with the scikit-bio codebase and community.
Valuable contributions can be made without or with minimum amount of coding. We detail various ways you can contribute below:
Ask a question | Report an error | Suggest a new feature | Fix a typo
Contributing code to scikit-bio is a rigorous and rewarding process. We have prepared the following step-by-step guidelines:
Before coding | Set up a workspace | Write code | Test code | Document code | Style code | Submit code | Review code
In addition, there are separate documents covering advanced topics:
Your inquiry matters! By asking questions in the scikit-bio issue tracker or discussion board, you are not only giving us (and the community) the chance to help, but also let us assess the needs of users like you. Before asking a question, take a moment to search existing threads to see if there are any relevant ones. We also keep an eye on broader community forums such as Stack Overflow and BioStars for questions related to our scope.
The scikit-bio team is proud of our high-quality, well-tested codebase. That being said, no software is immune to errors, which may arise from bugs, overlooked edge cases, or confusions in documentation. In any situation, we would appreciate it if you can report the error you encountered to us.
You may open an issue to report the error. Please provide a detailed description of the error such that the developers can reproduce it. Specifically, you may include the following information in the report:
The exact command(s) necessary to reproduce the error.
The input file(s) necessary for reproducing the error. You may either attach the file in the issue (by dragging & dropping) if it is small, or provide a link to it otherwise. The file should only be as large as necessary to reproduce the error.
For example, if you have a FASTA file with 10,000 sequences but the error only arises due to one of the sequences, create a new FASTA file with only that sequence, run the command that was giving you problems, and verify that you still get an error. Then post that command and link to the trimmed FASTA file.
This is extremely useful to the developers, and it is likely that if you don’t provide this information you’ll get a response asking for it. Often this process helps you to better understand the error as well.
We take error reports very seriously. Once confirmed that they should be fixed, we will update the code to fix them as soon as we can, and ship the update in the next scheduled release of scikit-bio. If the error could result in incorrect results or inability to access certain functionality, we may release a bug-fix version of scikit-bio ahead of the schedule.
We are always looking for new ideas to enhance scikit-bio’s capabilities, especially from users with unique research interests. If you believe there is an analysis or feature that could extend scikit-bio’s current offerings, we warmly invite you to share your suggestions with us.
Please describe why the functionality that you are suggesting is relevant. For it to be relevant, it should be demonstrably useful to scikit-bio users and it should also fit within the biology/bioinformatics domain. This typically means that a new analytic method is implemented (you should describe why it’s useful, ideally including a link to a paper that uses this method), or an existing method is enhanced (e.g., improved performance).
If the scope of the suggested method overlaps with any pre-existing methods in scikit-bio, we may request benchmark results comparing your method to the pre-existing ones (which would also be required for publication of your method) so pointing to a paper or other document containing benchmark results, or including benchmark results in your issue, will help.
Before suggesting a new feature, it is also a good idea to check whether the functionality exists in other Python packages, or if the feature would fit better in another Python package. For example, low-level statistical methods/tests may fit better in a project that is focused on statistics (e.g., SciPy or statsmodels).
If your proposal represents a significant research direction or requires a substantial suite of methods, we encourage you to consider establishing a formal academic or industrial collaboration with the scikit-bio team. For more details on this process, please refer to the Collaboration section.
If you spot small errors such as typos, redundant spaces, broken links, missing citations etc. in the scikit-bio code or documentation, and want to give it a quick fix, you may follow the procedures detailed below. All procedures will take place in the web browser, and don’t involve creating anything in your local computer.
This approach should not be applied to anything larger than small errors. For the latter, please read Before coding.
Locate the specific code file that needs to be fixed in the GitHub repository. If you are reading the documentation, you can click the [source] link next to the header to locate the corresponding code file.
In the top-right corner of the code viewer there is an Edit () button, wit