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ontological analysis
Sorin Draghici
sorin at wayne.edu
Fri Jul 8 12:08:04 PDT 2005
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.
--
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: http://vortex.cs.wayne.edu/Sorin/ (personal)
WWW: http://vortex.cs.wayne.edu/Projects.html (lab)
Check out my recent book: Data Analysis Tools for Microarrays:
http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3154&parent_id=&pc=
--
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