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ontological analysis

Sorin Draghici sorin at
Fri Jul 8 12:08:04 PDT 2005


Questions regarding various tools and approaches to help with the 
biological interpretation of microarray results using GO seem to appear 
periodically on this list. These are usually followed by a flurry of 
emails suggesting x or y tool. Currently, there are over a dozen  
different tools that have been developed for this purpose. Although 
these tools use the same general approach, they differ greatly in many 
respects that influence in an essential way the results of the analysis. 
In most cases, researchers using such tools are either unaware of, or 
confused about certain crucial features. We have spent a few months 
reviewing 15  such tools looking at criteria such as:
- statistical model(s) used,
- type of correction for multiple comparisons,
- processing speed,
- reference microarrays available,
- scope of the analysis,
- visualization capabilities,
- capabilities for analysis at a custom level of abstraction,
- prerequisites and installation issues
- the sources of annotation data and the types of IDs accepted.

The results are reported in a paper which has been accepted for 
publication in Bioinformatics. The subscribers of this list might be 
interested in this short but reasonably comprehensive comparison of 
these tools. The pre-print is available at: 

The abstract is included below.

Best regards,



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 15 such tools using the following criteria: scope of the 
analysis, processing speed, 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.

Sorin Draghici, Ph.D. 

Director of the Bioinformatics Core, Karmanos Cancer Institute

Associate Professor		Tel: (313) 577-5484
Dept. of Computer Science	Fax: (313) 577-6868
Wayne State University
5143 Cass Ave, Room 431 State Hall, 
Detroit, MI, 48202
WWW: (personal)
WWW: (lab)

Check out my recent book: Data Analysis Tools for Microarrays:

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