Ontology meeting 2014-10-09: Difference between revisions

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Idea: use c16 information and equivalent class axioms to check for more specific classes. For example
Idea: use c16 information and equivalent class axioms to check for more specific classes. For example


  GO:0008283 (cell proliferation) + acts_on_population_of(CL:0000037 hematopoietic stem cell)
  GO:0008283 cell proliferation + acts_on_population_of(CL:0000037 hematopoietic stem cell)
  ==> GO:0071425 hematopoietic stem cell proliferation
  ==> GO:0071425 hematopoietic stem cell proliferation


   GO:0009913 epidermal cell differentiation + results_in_acquisition_of_features_of(CL:0002187 basal cell of epidermis)
   GO:0009913 epidermal cell differentiation + results_in_acquisition_of_features_of(CL:0002187 basal cell of epidermis)
   ==> GO:0030216 keratinocyte differentiation
   ==> GO:0030216 keratinocyte differentiation


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   ==> GO:0097154 GABAergic neuron differentiation
   ==> GO:0097154 GABAergic neuron differentiation


  GO:0001101 response to acid chemical + has_direct_input(CHEBI:3098 bile acid)
  GO:0001101 response to acid chemical + has_direct_input(CHEBI:3098 bile acid)
   ==> GO:1903412 response to bile acid
   ==> GO:1903412 response to bile acid



Revision as of 16:19, 8 October 2014

Attendees:

Minutes:


TG template for 'cell migration'

What is the most appropriate relation to use? See email thread '[go-ontology] cell migration templates'

GO into NCIt

Follow-up on negatively_regulates o part_of/positively_regulates o part_of

We'll go ahead with these, right? Previous discussion copied below:

(Need them for SF 11166)

Chris made a correction. The new diff is attached to his email with subject 'reasoner diff before and after' and dated Sept. 25th. The list is quite long - did anyone get a chance to review it?

We also need to think of this in the context of the call on monday. See: http://go-genkisugi.rhcloud.com/seed/model/gomodel:542732ad0000005 Would NOS inhibitor activity classify under the negative regulation term here? Because that is implied by the classic GAF annotation

AI: Let's give it another week and look at at least sections of it... we will all look at random sections of it and comment/decide at next week's call. If we haven't had time, we'll go ahead all the same.

Discussion on direct/indirect regulation. AI: Some of us will look at examples and figure out suggestions. David H will submit Nikolai's work as an RO ticket, and will check his presentation/slides into svn.

Automatic Annotation Deepening

Also know as Fold-based prediction. This is currently the only 'experimental' prediction method.

Idea: use c16 information and equivalent class axioms to check for more specific classes. For example

GO:0008283 cell proliferation + acts_on_population_of(CL:0000037 hematopoietic stem cell)
==> GO:0071425	hematopoietic stem cell proliferation
 GO:0009913 epidermal cell differentiation + results_in_acquisition_of_features_of(CL:0002187 basal cell of epidermis)
 ==> GO:0030216 keratinocyte differentiation
GO:0030182 neuron differentiation + results_in_acquisition_of_features_of(CL:0000617 GABAergic neuron)
 ==> GO:0097154	GABAergic neuron differentiation
GO:0001101 response to acid chemical + has_direct_input(CHEBI:3098 bile acid)
 ==> GO:1903412	response to bile acid

Activated for:

Look at 'gaf-prediction-experimental-report.txt' and 'gene_association.mgi.inf.experimental.gaf'