  The drive for reproducibility in the computational sciences has provoked
discussion and effort across a broad range of perspectives: technological,
legislative/policy, education, and publishing. Discussion on these topics is
not new, but the need to adopt standards for reproducibility of claims made
based on computational results is now clear to researchers, publishers and
policymakers alike. Many technologies exist to support and promote reproduction
of computational results: containerisation tools like Docker, literate
programming approaches such as Sweave, knitr, iPython or cloud environments
like Amazon Web Services. But these technologies are tied to specific
programming languages (e.g. Sweave/knitr to R; iPython to Python) or to
platforms (e.g. Docker for 64-bit Linux environments only). To date, no single
approach is able to span the broad range of technologies and platforms
represented in computational biology and biotechnology.
  To enable reproducibility across computational biology, we demonstrate an
approach and provide a set of tools that is suitable for all computational work
and is not tied to a particular programming language or platform. We present
published examples from a series of papers in different areas of computational
biology, spanning the major languages and technologies in the field
(Python/R/MATLAB/Fortran/C/Java). Our approach produces a transparent and
flexible process for replication and recomputation of results. Ultimately, its
most valuable aspect is the decoupling of methods in computational biology from
their implementation. Separating the 'how' (method) of a publication from the
'where' (implementation) promotes genuinely open science and benefits the
scientific community as a whole.
