Contributing to Biopython.
A Guide to Contributing to Biopython
So you want to contribute to Biopython, huh? Great! New contributions
are the lifeblood of the project. However, if done incorrectly, they can
quickly suck up valuable developer time. (We have day jobs too!) This is
a short guide to the recommended way to contribute code to Biopython.
See also the chapter about contributing in the
If you get as far as making a pull request with changes, you should
which describes how the automated testing including style checks are
Even if you don’t feel ready or able to contribute code, you can still
help out. There always things that can be improved on the
documentation (even just proof reading, or
telling us if a section isn’t clear enough). We also need people on the
mailing lists to help out with beginner’s
questions, or to participate in debates about new features. Maybe you
can propose general examples for the wiki
Finding a Project
The best contributions are the code that you have already been using in
your daily research. This should be code that you think might be useful
for other people and is already free of bugs. If you are thinking of
sending this in, go on to Step 2, Submitting Code!
Otherwise, there are still many ways to contribute to Biopython, both
involving coding and not. Some things that you can help on include:
- More unit tests: Some of our modules still only have partial
unit test coverage.
- Support for More Programs: There are many different
bioinformatics programs being developed. Identify one that does not
currently have support in Biopython and add support for it.
- Support for More File Formats: We can read and write lots of
different file formats, but there are always more. For sequences and
alignments look at the SeqIO and
AlignIO pages first. Note that HTML parsers
for specific websites are discouraged as these require long
- Support for Databases: Identify a biological database that does
not currently have support in Biopython and add support for it.
- Add New Data Type: You can add code that works with a new type
of data. This is a tough area, though. Creating a new robust and
useful data type is difficult, and we may be hesitant to add
something unless it’s already tried and tested.
- Add New Algorithm: You can add a new well-known algorithm that
might be useful for other biologists.
- Parser Verification: As Biopython supports more and more
databases, the difficulty in maintaining the format
parsers increases. These formats are changing very quickly. Thus, we
need to periodically verify that the parsers are still working. For
example, the GenBank parser needs to be checked to make sure it
handles each new dump of GenBank.
- Regression Tests: Biopython uses a regression testing framework
to make sure code is working. Although most of the functionality in
Biopython is tested, there are still some holes.
- Documentation: The tutorial is not complete and can use
some work. New users can be especially helpful here, as you learn
new packages. Our API documentation could also do with some work,
see coding conventions below.
- News Postings: If you keep up with the mailing lists (which are
fairly low volume), we need someone to help summarize important
posts and events as news items.
In general, we will consider any code that is applicable to biological
or chemical data. Please do not submit code whose functionality largely
overlaps with code already in Biopython, unless there is an obvious
improvement and you have a plan for integrating the code.
Before you submit it, please check that:
- It is generalized and likely to be useful for other things.
- The dependencies are reasonable. Adding support for a third party
command line tool is fine, but requiring additional python libraries
needs discussion. Dependencies on commercial closed-source software
probably won’t be accepted to Biopython.
- The code will be licensed with the Biopython license.
- You must have the legal right to contribute it and license it under
the Biopython license.
- You are enthusiastic about maintaining it and responding to
- You included docstrings in the code, and are willing to
- You have written, or are willing to write, a unit test for the
If all these terms seem acceptable, please send a description of your
code to email@example.com (see Mailing
lists). Be sure to subscribe to biopython mailing list
before sending a message, otherwise your message will be discarded by
the mail server (this was done to avoid spam on the mailing list). Don’t
send the code directly to the biopython mailing list. Instead,
please use our GitHub page by
creating an issue and either attaching the file(s) or linking to your
Biopython tries to follow the coding conventions laid out in
PEP 8 and
PEP 257. The important
- Classes should be in AllFirstLetterUppercase style.
- Functions should be in lowercase_separated_by_underscores style.
- Variables are either in lowercase_separated_by_underscores or
lowercasemungedtogether style, depending on your preferences and the
length of the variable.
- _single_leading_underscores to indicate internal functions or
classes that shouldn’t be called directly by a user.
- Tabs are bad. Most people in the Python community now dislike tabs
and instead prefer using 4 spaces for indentation. Most editors can
help you take care of this (Emacs python-mode uses the 4 space rule,
for instance). Tools/scripts/reindent.py in the Python distribution
will help get rid of tabs in files.
The one notable exception is module names, where we tend to use title
case. With hindsight this is unfortunate, but we are constrained by
is being used to generate
automatic documentation of the source code so it definitely is useful to
put helpful comments in your code so that they will be reflected in the
API documentation (in addition to
all the normal reasons to document code).
We generally don’t do anything fancy to try and format the comments in
the code – but they are interpreted as reStructuredText markup which
allows things like bullet points and italics. This isn’t fancy, but
it’s effective and easier then trying to deal with the myriad of
different ways to try and structure source code comments.
However, there are a few tricks to make your documentation look its
best. The main ones are:
- Modules, classes and function documentation should start with a one
line description. This must end with a period.
Here’s an example of a module documented so that epydoc will be happy:
"""This is a one line description of the module followed with a period.
More information about the module and its goals and usage.
"""One line description of the class followed by a period.
More information about the class -- its purpose, usage, and
def my_function(self, spam):
"""A terse description of my function followed with a period.
A longer description with all kinds of additional goodies. This may
include information about what the function does, along with
what parameters it will be passed and what it returns. You know,
information so people know how to use the function.
# the code ...