Conclusion

Many of the ideas outlined above have been described previously either in the context of computational biology or in general scientific computation. In particular, much has been written about the need to adopt sound software engineering principles and practices in the context of scientific software development. For example, Baxter et al. propose a set of five “best practices” for scientific software projects, and Wilson describes a variety of standard software engineering tools that can be used to make a computational scientist's life easier.

Although many practical issues described above apply generally to any type of scientific computational research, working with biologists and biological data does present some of its own issues. For example, many biological data sets are stored in central data repositories. Basic record keeping—recording in the lab notebook the URL as well as the version number and download date for a given data set—may be sufficient to track simpler data sets. But for very large or dynamic data, it may be necessary to use a more sophisticated approach. For example, Boyle et al. discuss how best to manage complex data repositories in the context of a scientific research program.

In addition, the need to make results accessible to and understandable by wet lab biologists may have practical implications for how a project is managed. For example, to make the results more understandable, significant effort may need to go into the prose descriptions of experiments in the lab notebook, rather than simply including a figure or table with a few lines of text summarizing the major conclusion. More practically, differences in operating systems and software may cause logistical difficulties. For example, computer scientists may prefer to write their documents in the LaTeX typesetting language, whereas biologists may prefer Microsoft Word.

As I mentioned in the Introduction, I intend this article to be more descriptive than prescriptive. Although I hope that some of the practices I describe above will prove useful for many readers, the most important take-home message is that the logistics of efficiently performing accurate, reproducible computational experiments is a subject worthy of consideration and discussion. Many relevant topics have not been covered here, including good coding practices, methods for automation of experiments, the logistics of writing a manuscript based on your experimental results, etc. I therefore encourage interested readers to post comments, suggestions