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Quantifying Specificity of GO Terms

Jane Lomax jane at ebi.ac.uk
Thu Apr 19 02:14:23 PDT 2007


Just fyi - this is the reply I originally sent to Tobias:

Hi Tobias - I'm afraid there isn't currently any reliable way to quantify 
the specificity of a GO term. Distance from the root is not proportional 
to the specificity of the term, as different parts of the graph have 
different depths (this is due to differences in the levels of research for 
different biological fields, and how much different parts of the graph 
itself have been worked on). Distance from the nearest leaf node might be 
more meaningful than distance from root, but again it's not precise 
because some areas of the graph have many very specific terms.

Sorry I couldn't be more help - it might be worth you sending a mail to 
gofriends at geneontology.org to see how others have handled this problem - 
there are lots of tool developers subscribed to that list. We are 
currently undertaking a project to try and standardize the depth v/s 
specificity a bit more in collaboration with some researchers at MIT, but 
it's a long-term project.

many thanks,

Jane Lomax




On Thu, 19 Apr 2007, Andreas Schlicker wrote:

> 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 
>>>> public
>>>> announcements and discussion of the Gene Ontology (GO) project.
>>>> Problems with the list?           E-mail: 
>>>> owner-gofriends at geneontology.org
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>>>> gofriends-request at geneontology.org
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>>> 
>>> 
>>> -- 
>>> This message is from the GOFriends moderated mailing list.  A list of 
>>> public
>>> announcements and discussion of the Gene Ontology (GO) project.
>>> Problems with the list?           E-mail: owner-gofriends at geneontology.org
>>> Subscribing   send   "subscribe"   to   gofriends-request at geneontology.org
>>> Unsubscribing send   "unsubscribe"  to  gofriends-request at geneontology.org
>>> Web:          http://www.geneontology.org/
>>> 
>> 
>
>
>
> -- 
> 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
>
> --
> This message is from the GOFriends moderated mailing list.  A list of public
> announcements and discussion of the Gene Ontology (GO) project.
> Problems with the list?           E-mail: owner-gofriends at geneontology.org
> Subscribing   send   "subscribe"   to   gofriends-request at geneontology.org
> Unsubscribing send   "unsubscribe"  to  gofriends-request at geneontology.org
> Web:          http://www.geneontology.org/
>

Dr Jane Lomax
GO Editorial Office
EMBL-EBI
Wellcome Trust Genome Campus
Hinxton
Cambridgeshire, UK
CB10 1SD

p: +44 1223 492516
f: +44 1223 494468

--
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