Scripts differentiate computational research from software production. A script is a file of code with a specific purpose such as running a BLAST search on the E. coli genome. Contrast this with much larger programs designed to manage a variety of inputs and commands. A bioinformatician uses scripts as research tools in the same way a laboratory biologist uses a pipette. In software development, scripting supplements the designing of a software product. The focus is the finished product and scripts there to make source code management or unit testing easier. Since scripts receive comparably less attention as a part of software design, is there best practice for using scripts?
Scripts are often required to run in a specific order. One script produces a result which is the input to the next script. This means the second script is dependent on the first. Dependency in software equates to increased complexity and requires more work to maintain a project. For example, if there is an undetected bug in one script mistakes are propagated as the next scripts are run. Or if one script in a series is missed, and the output files of a previous iteration still remain, then datasets are mixed between workflow repetitions resulting in unexpected side effects.
Removing the dependencies between workflow steps is difficult. Build files such as Rake, Ant, and make allow dependencies between scripted steps to be formalised: the required previous steps are automatically run first. This is useful to force the requirement that all previous results are deleted before hand, or that all rows in the database are refreshed, or even that the entire analysis is repeated from scratch. Capistrano is a variant where build files can be used to automate repetitive tasks across multiple remote computers. All of this can be managed using single command line calls.
Light and fluffy
Light and simple scripts are easier to maintain. To simplify, a script reads in a set of input data (such as a protein sequence), analyses the data (formatdb on a sequence database followed by BLAST), and then returns to the data (prints the results to the command line). Using this simplification, a script only needs to know what data is coming in, how to analyse the data, and how to return it.
Scripts can be made lighter by reducing the amount of analytical code. Instead of writing the code to call and parse BLAST, use existing code such as in BioPerl. If the code you need doesn't exist anywhere else, consider writing it as a separate library which can be shared across all your scripts. A script that reads in a the data, calls an external library, then prints the results will be smaller and simpler to understand. Contrast this with a script that reads in data, formats the data, has several lines of a code to interpret and massage the results, then writes output.
Keeping light and simple, and formalising dependencies makes script-based projects easier to manage, maintain, and repeat.