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ontological analysis

Claude Pasquier claude.pasquier at gmail.com
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 http://www.geneontology.org/GO.tools.shtml, 
the 14 tools analysed ? I am working on one such tool called THEA 
(http://thea.unice.fr/index-en.html), which is available to researcher 
from december 2003. THEA has been presented in a paper published last 
year in Bioinformatics 
(http://bioinformatics.oxfordjournals.org/cgi/content/abstract/20/16/2636). 
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
--
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:
>
> http://bioinformatics.oxfordjournals.org/cgi/reprint/bti565?ijkey=patTVvCzHtSiPPK&keytype=ref 
>
>
> 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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