  Computational biology continues to spread into new fields, becoming more
accessible to researchers trained in the wet lab who are eager to take
advantage of growing datasets, falling costs, and novel assays that present new
opportunities for discovery even outside of the much-discussed developments in
artificial intelligence. However, guidance for implementing these techniques is
much easier to find than guidance for reporting their use, leaving biologists
to guess which details and files are relevant. Here, we provide a set of
recommendations for sharing code, with an eye toward guiding those who are
comparatively new to applying open science principles to their computational
work. Additionally, we review existing literature on the topic, summarize the
most common tips, and evaluate the code-sharing policies of the most
influential journals in biology, which occasionally encourage code-sharing but
seldom require it. Taken together, we provide a user manual for biologists who
seek to follow code-sharing best practices but are unsure where to start.
