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Quantifying Specificity of GO Terms

Andreas Schlicker andreas.schlicker at mpi-sb.mpg.de
Thu Apr 19 01:17:11 PDT 2007


Hi,

Sorin Draghici schrieb:
 >
 > ...
 > The question at hand here is how to quantify the specificity of a given
 > term. This is independent of any experiment and any set of
 > differentially regulated genes and has to do with the structure of the
 > GO and the position of the given term in the DAG. For instance,
 > "regulation of apoptosis through extracellular signals" is more specific
 > than "regulation of apoptosis" or "apoptosis". The problem is how to
 > numerically quantify this specificity. To my knowledge, there is no
 > tools of any kind that would even remotely provide any quantitative
 > assessment of this specificity. Any answers or thoughts on this issue
 > would be very valuable.

We have developed a measure of similarity between GO terms that is based on the
information content of a GO term [1]. The information content is based on the
frequency of a term in UniProt, and can be used to quantify the specificity of a
GO term. A high value corresponds to a high specificity, terms less frequently
annotated and usually deeper in the graph. We have such a table for the August
2006 release of GO in our database.

[1] Schlicker A, Domingues FS, Rahnenfuehrer J, Lengauer T. A new measure for
functional similarity of gene products based on Gene Ontology. BMC
Bioinformatics 2006, 7:302 (http://www.biomedcentral.com/1471-2105/7/302)

Kind regards,
Andreas

> Stan Dong wrote:
>> Another tool is the GO-TermFinder by Gavin Sherlock. I believe there 
>> is interest for Amigo to incorporate this tool.
>>
>>     http://search.cpan.org/dist/GO-TermFinder/
>>
>> SGD has been using it with great satisfaction from our users. You may 
>> check the SGD page to get some sense of a use case.
>>
>>     http://db.yeastgenome.org/cgi-bin/GO/goTermFinder
>>
>> -Stan
>>
>> On Apr 17, 2007, at 9:36 PM, Paul Shannon wrote:
>>
>>> The Bioconductor project has, I believe, a fine solution to this 
>>> problem -- though
>>> please forgive me if I have misconstrued things.   The relevant 
>>> packages (see
>>> below) use the Hypergeometric distribution to calculate a p-value for 
>>> the
>>> enrichment of any GO node for the genes in question.  I typically map 
>>> proteins
>>> to GeneID's as the first step in my analysis.
>>>
>>> If this sounds like it addresses your problem, you may wish to take a 
>>> look at
>>>
>>>    http://bioconductor.org/packages/1.9/bioc/html/GOstats.html   and
>>>    http://bioconductor.org/packages/1.9/bioc/html/Category.html
>>>
>>> Each of these web pages contains a 'vignette' in a pdf file which 
>>> makes for
>>> a good introduction to the methods.
>>>
>>> Though orginally conceived in the context of microarrays, I use these 
>>> packages
>>> quite fruitfully with proteomics data.
>>>
>>>  - Paul
>>>
>>>
>>>>> I am working on a project that involves curation of protein data that
>>>>> includes GO terms, and it would be very helpful if I had some
>>>>> numerical quantification of the specificity of each term.  It is
>>>>> possible to manually examine each term to determine this specificity,
>>>>> but because there is a large amount of data, I would like to automate
>>>>> the process.  I understand that there is no reliable way to do this
>>>>> simply using the level in the DAG hierarchy, but I am wondering if any
>>>>> of you might have a work-around.
>>>
>>> -- 
>>> This message is from the GOFriends moderated mailing list.  A list of 
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>>
>>
>> -- 
>> This message is from the GOFriends moderated mailing list.  A list of 
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>>
> 



-- 
Andreas Schlicker, M.Sc.
Max-Planck-Institute for Informatics
Department 3: Computational Biology and Applied Algorithmics
Stuhlsatzenhausweg 85
66123 Saarbruecken
Germany

Phone: +49 681 9325 321
Fax: +49 681 9325 399
Homepage: http://www.mpi-inf.mpg.de/~schlandi

--
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