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ontological analysis

Claude Pasquier claude.pasquier at
Sun Jul 10 00:56:18 PDT 2005

Hi Sorin,

By reading your article, I wondered how you selected, from the 35 
available tools listed in, 
the 14 tools analysed ? I am working on one such tool called THEA 
(, which is available to researcher 
from december 2003. THEA has been presented in a paper published last 
year in Bioinformatics 
I  was disappointed not to see this tool reviewed in your paper. If 
there is a special reason or if you encountered difficulties to download 
or install it, please, let me know.

Best regards,

Claude Pasquier
Institute of Signaling, Developmental Biology and Cancer Research
Laboratory of Virtual Biology, Parc Valrose, Nice 06108, Cedex 02, France

Sorin Draghici a écrit :

> Hi,
> 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,
> Sorin
> Abstract
> ======
> 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.

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