Software development vis à vis collaboration in interdisciplinary research.

The ability to reanalyze microarray data would potentiate research explaining the molecular bases of embryological development, aging, cancer, and a plethora of other diseases. This potential results from the rapidly growing volume of microarray data, and from the wide range of applications of this technology. It is, therefore, not surprising that one of the primary goals of bioinformatics researchers is the development of software that would allow biologists to share, combine, and reanalyze microarray data. Wan and Pavlidis present an excellent overview of efforts to make the reanalysis of microarray data a reality. As Wan and Pavlidis demonstrate, reanalysis of microarray data in neuroscience has not yet occurred to any significant extent. To understand why, it helps to examine the chain of events that constitute this endeavor. At the risk of oversimplification, I have divided this chain into five major events: sample collection, gene-expression profiling, annotation, sharing, and statistical analysis. First, samples for microarray analysis must be obtained. Wan and Pavlidis acknowledge the difficulties in obtaining nontumor human brain tissue, as inflammatory, developmental, and psychiatric conditions rarely meet clinical criteria for biopsy. Even for tumors, however, not all biopsies are equivalent: tissue can be obtained from a variety of sources, including cell culture and surgical biopsy, and may be refined further via laser capture microdissection (LCM). If obtained from human subjects, their treatment histories and demographics, in addition to other clinical information (e.g., presence or absence of metastasis), may affect subsequent statistical analysis. Once the samples have been obtained, the investigator must acquire gene-expression profiles, a process rife with options, including the type of microarray platform, and detection and normalization techniques for gene-expression values. Although the MicroArray Quality Control (MAQC) project (MAQC Consortium 2006) demonstrated good reproducibility across many of these variables, this result represents the ideal in data reproducibility, in that strict protocols were followed across this multisite trial. As the previous two paragraphs demonstrate, and as Wan and Pavlidis confirm, microarray data are much more complex than sequence data. It is for this reason that annotation is critical to the success of subsequent analysis. Scores of variables such as those described above must be included in the annotation, so as to alert biostatisticians to possible biases or confounding variables. To support this effort, researchers have developed standards and software for describing these variables: the Microarray Gene Expression Data (MGED) Society (http://mged.sourceforge. net/index.php) has specified the Minimum Information About a Microarray Experiment (MIAME) “required to ensure that microarray data can be easily interpreted” (Brazma et al. 2001). It is important to note that MIAME does not specify a file format or a controlled vocabulary (i.e., ontology); rather, it describes only the information to be transmitted. For these purposes, MGED developed the MAGE markup language MAGE-ML (Spellman et al. 2002), and the MGED ontology (Whetzel et al. 2006), respectively. It is also important to note that MIAME does not come with an enforcement mechanism; it codifies desiderata, rather than absolute requirements for data sharing. Thus, as Wan and Pavlidis note, researchers use a wide variety of terms to describe their data, rather than draw terms from a common ontology. Even if data were meticulously annotated, they could not be reanalyzed were they not freely shared among researchNeuroinform (2007) 5:176–177 DOI 10.1007/s12021-007-0008-1