Biomedical Sciences - Revision history

Matthan at 18:56, 1 September 2010

2010-09-01T18:56:49Z

Matthan at 17:34, 1 September 2010

2010-09-01T17:34:23Z

Matthan at 17:33, 1 September 2010

2010-09-01T17:33:29Z

Matthan at 17:18, 1 September 2010

2010-09-01T17:18:28Z

Priti at 15:13, 12 August 2010

2010-08-12T15:13:41Z

Priti at 15:08, 12 August 2010

2010-08-12T15:08:19Z

Priti at 13:28, 13 July 2010

2010-07-13T13:28:44Z

Priti at 15:46, 12 July 2010

2010-07-12T15:46:55Z

Priti at 15:46, 12 July 2010

2010-07-12T15:46:30Z

Priti at 15:46, 12 July 2010

2010-07-12T15:46:13Z

@@ Line 31: / Line 31: @@
 #	Modeling of Experimental Processes and Protocols:  Two classes namely, gene_knockout_process and strain_creation_process, are created as subclass of provenir:process3 class, to model the generic gene knockout and strain creation processes. The knockout_project_protocol and strain_creation_protocol classes represent the particular protocols used in the lab. The GKO and SP protocols consist of multiple sub-processes, which are also modeled in PEO, for example sequence_extraction, plasmid_construction, transfection, drug_selection, and cell_cloning (Figure 1).
-#	Modeling of Datasets and Parameters Used in Protocols:  A novel feature of the provenir ontology is the distinct modeling of provenir:data_collection (representing entities that undergo processing in an experiment) and provenir:parameter (representing entities that influence the behavior of a process or agent).  In the PEO, for example process, transfection, the input value Tcruzi_sample is modeled as a subclass of provenir:data_collection class and the parameter value transfection_buffer is modeled as a sub-class of the provenir:parameter class. Further, the parameter values are also categorized along the space, time, and theme (domain-specific) dimensions, for example the date on which an experiment is conducted is modeled using the Time:DateTimeDescription class (Figure 1). [[Image:ExtendingProvenir.jpg| 400x| Figure: 1 Extension of PO to PEO]]
+#	Modeling of Datasets and Parameters Used in Protocols:  A novel feature of the provenir ontology is the distinct modeling of provenir:data_collection (representing entities that undergo processing in an experiment) and provenir:parameter (representing entities that influence the behavior of a process or agent).  In the PEO, for example process, transfection, the input value Tcruzi_sample is modeled as a subclass of provenir:data_collection class and the parameter value transfection_buffer is modeled as a sub-class of the provenir:parameter class. Further, the parameter values are also categorized along the space, time, and theme (domain-specific) dimensions, for example the date on which an experiment is conducted is modeled using the Time:DateTimeDescription class (Figure 1). [[Image:ExtendingProvenir.jpg| center| 600px| Figure: 1 Extension of PO to PEO]]
 #	Modeling of Experiment Materials and Other Details:  PEO uses provenir:agent class to model researchers, instruments, and biological agents involved in an experiment. For example, transfection_machine, microarray_plate_reader are instruments modeled as subclass of provenir:agent; researcher is an example of human agent; and knockout_plasmid is an example of a biological agent.
 In addition to the eleven relationships defined in provenir ontology, new object and datatype properties specific to experiment protocols have been created. For example, four new object properties are defined to model the similarity relationships between two genomic regions, namely is_paralogous_to, is_orthologous_to, is_homologous_to, and is_identical_to.

@@ Line 62: / Line 62: @@
     |title = <span style="color:#ffffff">Reference</span>
     |title_background= #008b8b
-    |content= :*S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.
+    |content= *S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.
 }}

@@ Line 43: / Line 43: @@
     |title_background= #008b8b
     |content=The PEO forms schema to query semantically integrated parasite experimental datasets and databases using intuitive query processing tool, Cuebee. Following are the example provenance queries that can be formulated and successfully executed using PEO and Cuebee:
-::*	List all groups that are using“target_region_plasmid_Tc00.1047053504033.170_1”.
+:*	List all groups that are using“target_region_plasmid_Tc00.1047053504033.170_1”.
-::*	Find the name of the researcher who created the knockout plasmid “plasmid66.”
+:*	Find the name of the researcher who created the knockout plasmid “plasmid66.”
-::*	“cloned_sample66” is not episomal. How many transfection attempts are associated with this sample?
+:*	“cloned_sample66” is not episomal. How many transfection attempts are associated with this sample?
-::*	Which gene was used create the cloned sample “cloned_sample66"?
+:*	Which gene was used create the cloned sample “cloned_sample66"?
 }}

@@ Line 1: / Line 1: @@
-The Provenir Ontology was used as upper level ontology to develop [[Parasite Experiment ontology |Parasite Experiment Ontology]] (PEO) for the NIH-funded T.cruzi Semantic Problem Solving Environment (SPSE) project. The PEO extends the classes and relationships in provenir ontology to model provenance information associated with experiment protocols for Gene Knockout (GKO), Strain Creation (SC), Microarray, and Proteome Analysis. These protocols consist of multiple sub-processes, which are modeled in PEO as different classes (Figure 1).
+{| style="width:100%; vertical-align:top;"
-[[Image:ExtendingProvenir.jpg| 400x| Figure: 1 Extension of PO to PEO]]
+<!--begin left panel -->
-The PEO forms schema to query semantically integrated parasite experimental datasets and databases using intuitive query processing tool, Cuebee.  Following are the example provenance queries that can be formulated and successfully executed using PEO and Cuebee:
+|-
-*List all groups using“target_region_plasmid_Tc00.1047053504033.170_1” target region plasmid.
-*Find the name of the researcher who created the knockout plasmid “plasmid66.”
+{{block
-*“cloned_sample66” is not episomal. How many transfection attempts are associated with this sample?
+   |title= <span style="color:#ffffff">Provenance in Biomedical Sciences</span>
-*Which gene was used create the cloned sample “cloned_sample66"?
+   |title_background= #008b8b
-'''Reference:'''
+|-
 *S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.

@@ Line 1: / Line 1: @@
-The Provenir Ontology was used as upper level ontology to develop [[Parasite Experiment ontology |Parasite Experiment Ontology]] (PEO) for the NIH-funded T.cruzi Semantic Problem Solving Environment (SPSE) project. The PEO extends the classes and relationships in provenir ontology to model provenance information associated with experiment protocols for Gene Knockout (GKO), Strain Creation (SC), Microarray, and Proteome Analysis. These protocols consist of multiple sub-processes, which are modeled in PEO as different classes (Figure 1).  The PEO forms schema to query semantically integrated parasite experimental datasets and databases using intuitive query processing tool, Cuebee.
+The Provenir Ontology was used as upper level ontology to develop [[Parasite Experiment ontology |Parasite Experiment Ontology]] (PEO) for the NIH-funded T.cruzi Semantic Problem Solving Environment (SPSE) project. The PEO extends the classes and relationships in provenir ontology to model provenance information associated with experiment protocols for Gene Knockout (GKO), Strain Creation (SC), Microarray, and Proteome Analysis. These protocols consist of multiple sub-processes, which are modeled in PEO as different classes (Figure 1).
 [[Image:ExtendingProvenir.jpg| 400x| Figure: 1 Extension of PO to PEO]]
 '''Reference:'''
 *S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.

← Older revision		Revision as of 15:46, 12 July 2010
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	'''References:'''		'''References:'''
−	S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.	+	*S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.

@@ Line 5: / Line 5: @@
 [[Image:Provenir_to_PEO.jpg| 400x| Figure: 1 Extension of PO to PEO]]
-''References:''
+'''References:'''
 S.S. Sahoo, D.B. Weatherly, R. Mutharaju, P. Anantharam, A. Sheth, R.L. Tarleton, “Ontology-driven Provenance Management in eScience: an Application in Parasite Research”, The 8th International Conference on Ontologies, DataBases, and Applications of Semantics, (ODBASE 2009), Vilamoura, Algarve-Portugal, Nov 02 - 04, 2009.