Ontological analysis of gene expression data: current tools, limitations, and open problems.
Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu
Purvesh
P
Khatri
Sorin
S
Drăghici
D000465:Algorithms; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods*; D012015:Reference Standards; D017422:Sequence Analysis, DNA; D012984:Software