Old case studies

From Chris Stoeckert 27th Sept 2007

1. Experiment Design Type a. I want to find available ChIP-chip studies. b. Need an identifiable set of terms for the common types of microarray applications based on what is being hybridized. c. possible source: MO: TechnologicalDesign. currently contains 4 terms - example: binding_site_identification_design synonyms: chromatin immunoprecipitation, chromatin IP, chromatin_immunoprecipitation, ChIP-chip d. Generalize to other types of array applications such as those based on the kinds of questions addressed by the experiment or the kinds of factors used in the experiment (i.e. intent).

2. Biological Context a. Find all studies related to cancer b. Need terms that tell you which fields in the assay annotation to look at. Then need a lexicon that identifies cancer related terms. c. Source(s) for fields: MAGE-TAB headings; MO: BioMaterialCharacterisitics (e.g. with terms from DiseaseState); Factor values (e.g. factors of type clinical_information or disease_state). Source(s) for cancer: NCI Thesaurus, Disease Ontology, eVOC d. Generalize to any biological context.

3. Factor correlation a. What experimental factor is most correlated with my gene(s) b. Need terms that tell you which fields in the assay annotation to look at. Also need terms that were consistently applied. c. Source(s) for fields: MAGE-TAB headings; MO: FactorValue, ExperimentalFactorCategory subtypes. d. Provide covariates for Biobase part of Bioconductor.

4. Smart forms a. I want to submit an Affymetrix microarray experiment. b. Need to have classes and terms for annotations relevant to Affymetrix experiments tagged so that only relevant choices are provided for annotation. c. MO, OBI, OBO d. Want to submit experiments using appropriate dynamically generated templates

5. Annotation term enrichment a. Anything significant about the types of samples in my (bi-)cluster? b. Need terms that were consistently applied so that they can be used to bin assays and thereby amenable to tests like hypergeometric. c. Source(s) for fields: MAGE-TAB headings; MO classes and instances including synonyms d. Provide MIAME class (experiment description) and covariates for Biobase part of Bioconductor.

6. Quality Control a. Is my experiment annotated properly? b. Need to be able to extract terms for biological context, intent, and protocols and use these to compare with other experiments to see if my experiment and assays have expected matches. c. Source(s) for fields: protocol type, experimental factor type, factor value, sample characteristic. d. Find similar experiments, assays based on protocol type, experimental factor type, factor value, sample characteristic.

7. Meta-analysis a. Can I gain power in analysis by adding relevant assays from ArrayExpress to mine? b. Need to be able to extract terms for biological context, intent, and protocols and use these to compare with other experiments to see if there are any other studies that could be combined. c. Source(s) for fields: protocol type, experimental factor type, factor value, sample characteristic. d. Find similar experiments, assays based on protocol type, experimental factor type, factor value, sample characteristic.

From Elisabetta Manduchi 2nd Oct 2007

Specific Evaluation for Data Processing

1. I have conducted an expression microarray experiment involving two conditions with replicate assays per condition, where I have both biological and technical replicates. I’m running two kinds of differential expression analyses: (a) one at the gene level and (b) one at the gene set level. In (a) my aim is to identify differential expressed genes (e.g. via algorithms like PaGE and SAM). In (b) my aim is to identify, from an a priori given collection of gene sets (e.g. user provided, or based upon GO annotation), which of these sets are differentially expressed as a whole (e.g. via algorithms like GSEA or SAM-GSA). Before running the analyses I’m preprocessing the data with the following data transformation series: (i) filter out flagged reporters, (ii) normalize the individual assays, (iii) average across technical replicates (but not across biological replicates). I need to annotate all steps above.

2. I have conducted an experiment involving multiple phases, both experimental and computational: a. A gene expression experiment where I have profiled gene expression in human across multiple conditions. b. An analysis where I first cluster the genes using the data from (a) and then I perform sequence analysis on the promoter regions of genes in the same cluster to find common motifs. The latter is accomplished both by focusing on conserved regions (after alignment of my sequences to mouse sequences) in these promoters, and then by the application of motif finder algorithms (e.g. MEME or PWM-based). Through this I identify a collection of candidate binding sites for a set of transcription factors. c. I compare the candidates I found in (b) to results from separate ChIP-chip experiments involving those same transcription factors and samples. I need to annotate all the above. This includes all pre-processing performed on the expression and ChIP-chip data and all the analyses carried out: clustering, identification of promoter regions, alignment, motif finding, identification of bound reporters in the ChIP-chip experiment, etc.

3. I have conducted an experiment involving miRNA expression arrays, mRNA expression arrays and proteomics. After pre-processing each dataset, I relate differentially expressed miRNAs to their computationally predicted targets (using a pre-defined prediction algorithm). Then I verify whether such targets are differentially expressed (inversely to the miRNAs) in either the mRNA or the proteomics experiment to confirm targets and also the action of the miRNA on them (mRNA degradation vs translation repression). I need to annotate all pre-processing in the three dataset, all differential expression analyses and the target prediction analysis.

From Ryan Brinkman 3rd October 2007

Data Exchange

1. I have completed a flow cytometry experiment that I wish to annotate and provide to a collaborator along with the primary data for analysis. We have agreed to use RDF to describe instrumentation details and I will use OBI to provide the terms. We will exchange a data bundle which has both the raw data, RDF metadata describing instrumentations, XML metadata describing other experimental details and an RDF index file. Software tools will be able to parse the various component files and be able to access the instrumentation information as part of the analysis procedure.

2. We will submit the experimental datafile bundle to a data repository, which will parse the instrumentation RDF file so included details can be searched for by third parties.

From Melanie Courtot 27th November 2007

(note: I am trying to summarize the work done during a previous project - let me know if you need more details)

I am working in a network of wet labs. We are all together trying to over express and purify, then crystallize membrane proteins of interest. I want to annotate every step of the project, from cDNA collection to structure resolution: cDNA collection, cloning, subcloning,expression assays in different systems (yeast, viruses...), expression results analysis (dot blots, western blots, biacore assays...), solubilisation and purification trials/results, refolding assays if necessary, then crystallization trials and structures solved (different technics, NMR, x-ray diffraction...). + in some cases structure prediction after target selection.

I want to:
 * prepare a list of annotated targets of interest
 * store these results using a LIMS
 * be able to export them (share them with an other lab), with enough information for somebody else to reproduce the experiment
 * be able to track back the previous step(s) of an experiment
 * be able to compare the different protocols/systems

I also might want to:
 * compare/align sequences (DNA or proteins)
 * search for certain patterns in these sequences (hydrophylic/phobic residues...)
 * apply certains calculations to my sequences (eg calcul of the fusion point)

From James Malone Dec 2007

We wish to annotate microarray data using OBI which consists of three benchmark datasets: Leukemia cancer dataset, Colon cancer dataset and Lymphoma cancer data set. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Multi-layer perceptron, k-nearest neighbour, support vectormachine and structure adaptive self-organizing map have been used for classification.

See: http://portal.acm.org/citation.cfm?id=820189.820213

Flow cytometry data analysis found here: http://jcsmr.anu.edu.au/facslab/analysis.html

From Susanna Sansone/Philippe Rocca-Serra/Daniel Schober Jan 2008

We are developing a system archive to store 1) metadata descriptors for studies with assays employing transcriptomics, proteomics and metabolomics (omics) technologies and 2) their associated data files. We will have a) a tool for entering/describing the study and assay metadata, plus submit the associated data files, and b) an interface for querying the studies and their assays - using the metadata descriptors only- and downloading the data files. See BioMAP at http://www.ebi.ac.uk/net-project/

There are key metadata descriptors for which we need CVs/ontologies, e.g.
 * type protocols
 * type of instruments and their parameters
 * data processing methods
 * study design
 * sample characteristics (e.g. organism name, organ part)
 * factors, or variables of the study (virtually these can be everything, in most of our use cases are compound/drug, dose related)
 * measuraments or endpoints measured (e.g. gene expression, protein identification): these are relate to the one below
 * technology types (e.g. DNA microarray, Mass Spectrometry): these need to be related to the one above
 * data file type

These same metadata descriptors will be provided to the users to browse and query the content our system.

From Tina Boussard Jan 2008

1. I want to perform a systematic review of the clinical data in OBI that prospectively compared mechanical bowel preparation with no mechanical bowel preparation for patients undergoing elective colorectal surgical resection. I would search using the following search terms: (1) “Surgical Procedures, Elective” AND “Colorectal Surgery” AND mechanical bowel preparation; (2) mechanical bowel preparation AND elective AND surgery; (3) mechanical bowel preparation AND surgery AND colon AND rectum. I am interested in studies that have two groups (one with mechanical bowel preparation and one without mechanical bowel preparation) and have the two outcomes of interest, anastomotic leaks and wound infections. Meta-analysis will be performed on the data using Peto-Odds ratio (fixed effects model).

2. I have completed an analysis assessing alcohol use patient who have undergone bastric bypass surgery in the past 5 years and I want to annotate this experiment using OBI. Each patient is sent a medical questionnaire to assess changes in their alcohol consumption, tolerance, and abuse since the time of their operation. Total pre and post alcohol consumption and unadjusted alcohol consumption was compared and trends were analyzed. Data will be reported with odds-ratios.

From John Westbrook, Structural Biology Use Case 31st Jan 2008

Structural Biology Scenarios:

Here is a narrative description of a structural biology investigation:

An experimental objective is to determine the atomic-level 3D-structure of a target protein which will be used to establish the protein function. Two experimental approaches are attemped: X-ray crystallography and NMR. The structure result of the investigation is later used to model a component in a larger structural investigation.

A preliminary NMR HSQC experiment provides evidence of a folded protein. A 3D NOESY-[1H,15N,1H]-ZQ-TROSY NMR experiment is performed on isotopically labeled sample of the target protein. An ensemble of the 30 structural instances is derived from this experiment. One member of this set is chosen as the representative based on a minimum energy refinenement criterion.

The selenomethione-labeled protein was expressed, purified and crystallized in the manner described for the native target protein. The crystal specimen is obtained by sitting drop vapor diffusion experiment consisting of soluble protein and buffer components. Diffraction data is collected at SSRL on Beamline BL9-1 on the cryogenically frozen protein crystal. The 3D X-ray structure of the protein crystal is subsequently determined using MAD phasing followed by TLS refinement. Analysis and validation of the resulting structure model reveals two regions of low stereochemical quality and the structure is subsequently refit to address this. The final model exhibits a novel structural fold. The protein interactions observed in the putative multi-meric structure provides supporting evidence of the biochemical function of the protein.

A subsequent 3DEM experiment uses this X-ray structure as a molecular replacement model in a structural study of viral protein assembly ...

Use case examples related to this example:

The narrative presents the context that a user would like to appreciate about a structural investigation. In fact the parts of this experiment may come from the work of many different investigators working asynchronously. Linking the component experiments must be performed based on protein sequence and deposition time.

Protein sequence is a fundamental information entity in the PDB data processing pipeline. Resolving the mapping of the experimentally observed structure onto the protein sequence is a key task in the annoation process. Linking to biological annotation is performed largely via sequence database accession or via protein sequence. Similarly, the sequence is an important entry point to finding PDB data.

For small molecule components of structural experiments, chemical identity and nomemclature is standardized relative to a project dictionary of chemical descriptions. This reference file includes: molecular names and synonyms; conventional atom names; covalent bonding and stereochemical assignments; model and ideal 3D coordinates; and descriptors such as InChI and SMILES.

In a PDB dataset, each step in the above narrative procedure may be accompanied by additional experimental details. As with any resource, the PDB data model provides placeholders for the maximum level of detail to capture this information. However, the coverage of these details is uneven and the focus here will be on those details that are likely query targets.

Here are 3 examples which are typical of the experimental detail we would like to represent. There are many common features in these examples such as: physical measures such as length & temperature; dates and intervals; instruments used and their operating conditions; solution preparation & concentration measures; software applications used; and numerical details characterizing the results.

Example 1. Protein Production -

The following example for protein expression is typical of the protocol level detail collected by PepcDB (http://pepcdb.rcsb.org/ for PSI Structural Genomics targets.  Protocols are collected for target selection, cloning, expression, purification, crystallization, NMR screening.  We are beginning to classify/mine these protocols for common methodologies/treatments, instrumentation,  named substances and biological materials.

--- Representative expression protocol - see http://pepcdb.rcsb.org/ for - Target ID: NYSGXRC-10077d 	Site: NYSGXRC

General Expression Protocol Details:

E. coli Fermentation in HighYield (HY) Media with SeMet Buffer

This protocol describes the routine laboratory procedure for growth of E. coli in HighYield (HY) media and expression of protein with Selenomethionine labeling.

This SOP is written for 1 L cultures. Scale up as necessary.

Preparation of HY Media:

To a 2 L baffled flask containing 950 ml autoclaved MilliQ water add the following: 1. One packet of M9 salts (Cat# MD045004A)* Open packet using scissors cleaned with ethanol. Carefully pour powder in to neck of flask. Some powder may stick to the neck of the flask. 2. 10 ml of Mineral supplement (CAT# MD045004B). 3. 1 ml of Vitamin supplement (CAT# MD045004C)*. 4. 1 ml each of antibiotic as needed:  Kanamycin (30 mg/mL solution), Chloramphenicol (30 mg/mL solution). Other antibiotics can be added when appropriate to an effective concentration. 5. 10 ml 50% Glycerol. 6. Mix flask and allow salts to go in to solution before using.

Overnight Cultures: 1. Label a sterile 250 ml baffled shake flask. 2. Add 20 ml of HY media to bottle. 3. Scrape cell stock using a pipette tip and add to media. 4. Grow culture overnight at 30C, shaking at 250 rpm.

Growth Cultures: 1. Add 15 ml of overnight culture to a sterile 2L baffled shake flask containing 1 L fresh HY media. 2. Place flasks in shaker at 37C, shaking at 250 rpm until O.D. reaches ~1 3. Lower temperature of shaker to 22C. 4. Continue shaking 20 minutes. 5. Add 1.5X** (30 ml) of SeMet Buffer (CAT#MD045004D)*.

In cases of proteins containing 10 or more Methionines and relatively high solubility profiles, add 2X** (40 ml) SeMet Buffer. 6. Continue shaking 20 minutes. 7. Induce the cells by adding 1 ml IPTG (Cat#MD045003E)* to each flask. 8. Continue shaking for 21 hours at 22C.

Pellet Collection: 1. After 21 hours remove flasks and add culture to 1 L spin bottle. 2. Balance bottles and close securely. 3. Spin bottles in centrifuge at 6500 rpm for 11 minutes. 4. Pour out supernatant. 5. Scrape out pellet and put in labeled 50 ml conical tube. 6. Place tubes in -80C freezer.


 * Refer to Medicilon SOP for preparing Stock Solution.
 * Medicilon standard protocol indicates use of 1X (20ml) SeMet. Experimental results indicated more optimal results at 1.5-2X concentration in our process.

Additional trial level details contributed relative to the general procedure above:

Fermentation
 * Growth Media (large scale): HY
 * Total volume (L): 1
 * Induction time (hr): 21
 * Induction temp. (C): 22
 * Pellet weight (g): 9
 * Harvest date: 08/31/2006
 * Selenomet: Y

---

Example 2. Crystallization description

The following details typically accompany the description of a successful crystallization trial. These include the solution conditions and preparation method used to obtain the crystal. In some cases there may be negative results as well as the details of any robotic screening process leading to the successful trial. There are also parameters defining the final crystal specimen on which data is collected.


 * Method: 	  Vapor diffusion, sitting drop
 * pH: 	         5.5
 * Temperature: 	 298.0
 * Solution Details: protein sample + 0.1M Bis-Tris + 3M Sodium chloride

Crystal parameters -
 * Length A 	49.69 (Angstroms)  	Angle Alpha	90.00 (degrees)
 * Length B 	49.69			Angle Beta     90.00
 * Length C 	71.19			Angle Gamma 	90.00
 * Space Group Name 	P 41

Example 3. Data Collection for X-ray diffraction experiments

The following is typical of the level of detail obtained for an X-ray diffraction data collection at a synchrotron radiation facility. The details in the example include: instrumentation such as radiation source, monochrometer, and detector; essential settings (wavelength & temperature); software used in processing the collected data; and essential features of the collected (eg. resolution range, merging statistics, completeness). The instrumentation details here are represented by controlled vocabularies (e.g. synchrotron facilities, source details, available detectors). Many of these details are maintained by our BioSync project (http://biosync.pdb.org).

Diffraction Source Instrument:

Overall Statistics. In the highest resolution shell.
 * Date of data collection       : 06-APR-2007
 * Temperature          (K)      : 100.0
 * pH                            : 7.00
 * Number of crystals used       : 1
 * Synchrotron             (Y/N) : Y
 * Radiation source              : SSRL
 * Beamline                      : BL9-1
 * Monochromatic or Laue   (M/L) : M
 * Wavelength or range       (A) : 0.979
 * Monochromator                 : FLAT MIRROR (VERTICAL FOCUSING); SINGLE CRYSTAL          FOCUSING); SI(111) BENT MONOCHROMATOR (HORIZONTAL FOCUSING)
 * Optics                        : MIRRORS
 * Detector type                 : CCD
 * Detector manufacturer         : ADSC QUANTUM 315
 * Intensity-integration software : MOSFLM
 * Data scaling software         : SCALA
 * Number of unique reflections  : 21854
 * Resolution range high     (A) : 2.900
 * Resolution range low      (A) : 20.000
 * Rejection criteria (SIGMA(I)) : 0.000
 * Completeness for range    (%) : 97.0
 * Data redundancy               : 3.300
 * R Merge                   (I) : 0.04000
 * R Sym                     (I) : NULL
 * Sigma for the data set : 8.6000
 * Highest resolution shell, range high (A) : 2.90
 * Highest resolution shell, range low (A) : 3.06
 * Completeness for shell    (%) : 96.3
 * Data redundancy in shell      : 3.50
 * R Merge for shell         (I) : 0.79800
 * R Sym for shell           (I) : NULL
 * Sigma for shell        : 0.600

Competencies related to the structural biology examples -


 * 1) Standardization: Our data processing and annotation pipeline needs to recogonize and annotate common/standard experimental details. Similarly, these need to be provided as query targets in our search services.
 * 2) Validation: Automatically identify improbable or incompatible experimental conditions.
 * 3) Traversal: Starting from the specification of a protein sequence/name create an ordered summary of the available experimental results (similar to the narrative description).
 * 4) Mining: Select those structures annotated to participate in a particular biological process (GO:xxxx) and constrained by some experimental condition(s).
 * 5) Integration: For all protein structures lacking an assignment of biochemical function, find results from other biomedical investigations that may contribute to an assignment. Similarly, present semantic entry points to the available structure data making this data interpretable in other biomedical investigations.

Specific Competency Questions
From Elisabetta Manduchi 2nd Oct 2007

1. Identify all gene expression experiments where GSEA has been used to identify GO Biological Processes differentially expressed between a Wild Type and a Knock Out condition.

2. Identify computationally potential malaria drug targets by looking at genes with evidence of expression (both mRNA and protein) in the merozoite stage of Plasmodium Falciparum and annotated with the GO term “catalytic activity”. (Example extracted from www.plasmodb.org).

From Elisabetta Manduchi 14th Nov 2007

3. Identify all phylogenetic trees obtained utilizing homologous sequences relative to user-specified protein and show the method/algorithm used to build each such tree.

From Richard Scheuermann, Jan 2008

1. Identify all experiments in which the levels of IL2 mRNAs were measured, regardless of the methodologies used.

2. Identify all experiments in which the levels of IL2 protein were measured in secondary organs of the immune system in investigations to examine novel therapies for any autoimmune disease.

3. Identify all experiments in which the xyz antibody was used as an analyte detection reagent.

4. Identify all experiments in which the level of IL2 was significantly different between the control group and the experimental group of samples.

General Competency Questions
From Elisabetta Manduchi 2nd Oct 2007

1. Identify all pre-processed microarray data which express values as log ratios (of two conditions) for a specified logarithmic base.

2. Identify all experiments where gene expression microarray data have been combined with sequence analysis to identify potential regulatory elements.

3. Identify all aCGH experiments where aberrant regions have been identified through an algorithm based on multiple sample analysis (e.g. STAC: www.cbil.upenn.edu/STAC/)

From James Malone Dec 2007

1. Which microarray experiments have involved the use of SVMs for data analysis of gene expression data for classifying cancer survival rates? See http://www.biostat.pitt.edu/biost2055/2002_Shipp_human_lymphoma.pdf

2.

From Susanna Sansone/Philippe Rocca-Serra/Daniel Schober Jan 2008

1. Search/filter/sort study using any of the metadata descriptors - I list above - in combination

2. Create intelligent forms to drive submission of the metadata

3.

From Richard Scheuermann, Jan 2008

1. OBI should support a description of assay types in which the relationships between assay types based on similarities in input types and output types are captured.

2. OBI should support the assignment of specific roles to all of the objects involved in investigations and how the role of an object may change with time or context.

3. OBI should provide a mechanism for describing all aspects of a biomedical investigation, including study design, specimen processing, laboratory measurements and data analysis, and how each of the objects and processes relate to each other through a logical framework.

Annotation Use Case Examples
-

Investigation of Treatment of Inflamation

 * proposed by [mailto:Kevin.Clancy@invitrogen.com Kevin Clancy]
 * Google Spreadsheet version
 * [mailto:alanruttenberg@gmail.com Alan Ruttenberg's] OWL workup of this example

Hypothesis
Asprin can reduce swelling in an injured limb in mice

Experimental Approach

 * 1) Induce swelling in limbs of mice
 * 2) Half of the mice receive asprin, half do not
 * 3) Measure the reduction in swelling in both groups over 24 hours
 * 4) If a statistically significant number of mice reduce swelling more quickly than mice treated with a placebo, then asprin has the ability to reduce swelling
 * 5) If there are differences in the response to asprin, then we examine tissues to try and identify the causes of the differences.

Protocol 1 - Selection and housing of mice

 * 1) Select 50 male and 50 female mice of a given strain
 * 2) Have a standard housing protocol for all the mice. Acclimatize them for two days
 * 3) After two days, 48 male and 50 mice were determined to be healthy and could be used in protocol 2. The other mice died during acclimatization

Protocol 2 - Induction of injury

 * 1) Measure the volume of the left front foreleg of each mouse.
 * 2) Use an apparatus to immobilize and hit the front foreleg of each mouse with a defined amount of force.
 * 3) Allow each mouse to recover for 4 hours
 * 4) Measure the amount of swelling by increased volume
 * 5) Mice with a greater than 10% increase in volume are used for the study

Protocol 3 - Preparing asprin and placebo doses.

 * 1) Prepare a 100 microgram/ml solution of asprin
 * 2) Quantitate the asprin concentration by spectrophotometry
 * 3) Prepare a 100 microgram/ml solution of CaCl
 * 4) Quantitate the CaCl concentration by spectrophotometry

Protocol 4 - Administration of Asprin

 * 1) Divide the male and female mice up into four equally sized groups.
 * 2) Take one group of  each sex and inject each animal with 1 ml of asprin solution. Record the time of injection.
 * 3) Measure and record the volume of the limb for each asprin injected mouse at two hour intervals.
 * 4) Take the second group of each sex and inject each animal with the CaCl placebo solution. Record the time of injection.
 * 5) Measure and record the volume of the limb for each CaCl injected mouse at two hourly intervals.
 * 6) Continue all measurements for a 48 hour period.

Protocol 5 - Analysis of Data

 * 1) For each animal subtract the volume of the limb before treatment from the volume of the limb after treatment for each animal at each measured time period.
 * 2) For each group for each time period, work out the mean value of the difference in volume and the standard deviation of the measurements.

Protocol 6 - Analysis of organs

 * 1) Each animal was sacrificed by cervical dislocation after the last measurement.
 * 2) The following organs were harvested and weighed for each animal: Brain, heart, lungs, spleen, liver, kidneys, left and right foreleg muscles
 * 3) Sections of each organ were prepared for histological examination by using the paraffin embedded mounting procedure.
 * 4) All remaining tissues and the prepared slides were stored at -20C prior to analysis

Results

 * 1) CaCl2 treated animals continued to show increased swelling for 12 hours after administering the CaCl2 solution. Asprin treated animals showed increased swelling for only  2 hours after administration of the asprin solution
 * 2) After 24 hours post injection, 75% of the asprin treated animals' limbs has returned to their original sizes. Only 65% of CaCl2 treated animals limbs' had returned to their original size by the end of the study.
 * 3) The weights of all organs, except for swollen muscle limbs showed no significant differences in weight. The muscles crushed muscles showed increased weight compared to the uninjured muscles.
 * 4) We examined the tissue sections of affected and unaffected limbs animals that showed swelling at the end of the study. Sections were stained to show nuclei and cell boundaries. Muscle fibers from injured limbs had increased size compared to uninjured controls. Injured limbs had increased macrocyte infiltration. However asprin treatment reduced both muscle size and the degree of muscular swelling seen in sections.

Conclusions

 * 1) Asprin reduces muscular swelling post injury. CaCl2 was seen to have little or no affect
 * 2) Some animals show lower levels of responsiveness to the administration of asprin.
 * 3) In tissue sections, the administration of asprin has the effect of reducing muscular swelling due to injury and reducing macrocyte infiltration as a response to injury
 * 4) We conclude animals that had asprin and which responded better to the treatment by reducing swelling faster have a gene mutation that allows them to respond faster. Animals that do not respond as quickly lack this mutation. We are investigating this further.

Informal Compentency Questions
Informal Competency Questions: the requirements of the ontology, dependent on the Motivation. Described as informal questions or tasks that an ontology must be able to answer. The Gomez-Perez book on Ontological Engineering (http://books.google.com/books?id=UjS0N1W7GSEC) describes competency questions as (pp 119-120, when describing the methodology to create the TOVE ontologies): "..a set of natural language questions, called competency questions, are used to determine the scope of the ontology. These questions and their answers are both used to extract the main concepts and their properties, relations, and formal axioms of the ontology...Given the set of informal scenarios, a set of informal competency questions are identified. Informal competency questions are those written in natural language to be answered by the ontology once the ontology is expressed in a formal language. The competency questions play the role of a type of requirement specification against which the ontology can be evaluated."


 * Note: the original compentency question page can be found [here]. The details of which need to be reworked and merged within this page

Informal Competency Questions for the PSI Gel Working group
According to the MIAPE GE guidelines, OBI would be required to hold terms specific to the methods and techniques of protein separation using gel electrophoresis and cover gel manufacture and preparation, running conditions, protein detection techniques, the method of image acquisition and a technical description of the digitised image generated. Therefore, the competency questions are:
 * 1) Does OBI have all the terms that are represented in sepCV?

The competency questions specific for sepCV are:


 * Does sepCV support the minimal information reporting guidelines of MIAPE GE?
 * Does sepCV explicitly model the terms required by the use-cases modelled using GelML?
 * Are the concepts an accurate representation and a reflection of the terms used in the application domain of Sample processing and separation techniques?
 * Are the definition and semantic meaning of terms unambiguous within the context of the application domain of sample processing and separation techniques?
 * Is sepCV a community defined and accepted lexicon for a sample processing and separation techniques?


 * OBIEvaluation - Link to the evaluation strategy pages for OBI which included all submitted competency questions.

Informal Competency Questions as a result of discussions on obi-instrument branch
Archived Email thread These represent more discussion points rather than specific questions

The requirements of those acting as OBI-complient data producers are different from the requirements of OBI-complient data consumers.
 * 1) Producers need to have guidlines, the framework and support in providing their data.
 * 2) Consumers need the framework, available data and tools to support a wide range of querying, including fairly ad-hoc queries.
 * 3) Do we need to distinguish between 'browsing' consumers where all the work is done by a person clicking on links, and 'systematic' consumers where the heavy-lifting is done by software agents?

=== Archived email Thread - Regarding commercial terms within OBI ===


 * 1) The company has the option to maintain the OBI-view of their catalogue


 * 1) OBI should provide clear guide-lines for how a company could represent their catalogue with OBI


 * 1) Presenting catalogues using OBI should not impact in any way the ability of the company to represent, present or distribute the catalogue in alternative formats


 * 1) The presence of a company-maintained catalogue does not impact upon the possibility of some other party (OBI or a third-party) maintaining information about some or all of the products listed in the catalogue in some other OBI-complient source. It is, however, desirable that where practical, a single product is described in a single place.

And there is a responsibility that the company has:


 * 1) The company has a responsibility to make it clear what the licensing terms are for any OBI-formatted catalogue information, so that recipients know if they can refer to or replicate in part or in full the information contained within the catalogue.


 * 1) OBI must enable annotation tools (OBI editors, or less specialized apps) to use a wide range of relevant OBI-complaint terms. Regardless of the underlying engineering or technical details, the user must have the option of being presented with a single, integrated view of the domain.

OBI Function branch - Use Cases
This page provides background on the definition and apparent intended use of BFO:Function, as well as specific OBI Function examples derived from BFO:Function. The OBI examples include an explicit declaration of the related continuants, occurants, and relations required to DEFINE an OBI Function

<< Back to OBI Function maing page <<Back to Homepage

BFO:function Definition

 * A realizable entity the manifestation of which is an essentialy end-directed activity of a continuant entity in virtue of that continuant entity being a specific kind of entity in the kind or kinds of contexts that it is made for.

BFO:function Examples

 * the function of a birth canal to enable transport;
 * the function of the heart in the body to pump blood;
 * the function of reproduction in the transmission of genetic material;
 * the digestive function of the stomach to nutriate the body;
 * the function of a hammer to drive in nails;
 * the function of a computer program to compute mathematical equations;
 * the function of an automobile to provide transportation;
 * the function of a judge in a court of law

BFO:function Background

 * Discussion on BFO List on how to distinguish and use bfo:function, bfo:disposition, bfo:role

Current OBI Realizable Entities

 * as of 2007-07-22
 * OBI Realizable Entities Protege class hierarchy View
 * OBI Realizable Entities OWL Viz graph View

OBI Examples/Use Cases

 * putative related continuants in bold-italic; putative related occurrents in italic
 * the function of a high pressure liquid chromatagraphic (HPLC) system to separate molecules based on their solubility properties;
 * continuant in which function inheres: 'high pressure liquid chromatagraphic (HPLC) system*''
 * process: 'molecules* being separated'' based on their solubility properties - input  distinct fractions of mixture based on relative hydrophobicity
 * role (mode of participation in the process):
 * comments:
 * AR: (role as sub-relation) - the separation process hasseparator high pressure liquid chromatagraphic (HPLC) system - hasseparator isa subproperty of hasparticipant
 * BB: - high pressure liquid chromatagraphic (HPLC) system has the role separator in the hydrophobicity-based separation process - separator is a type of role that inheresin the high pressure liquid chromatagraphic (HPLC) system_
 * PATO: solubility (more specifically hydrophobicity)
 * the function of Nomarksi optics to enhance the constrast of specimens viewed in a light microscope based on their ability to refract visible wavelengths of light.
 * the function of the neuropsychological exam Ray's Auditory Verbal Learning and Memory Test (RAVLT) to assess the relative impairment of cognitive ability.
 * the function of the Tail Flick Analgesia test to measure pain sensitivity in mice and rats as they respond to the application of heat to a small area of their tails.
 * the function of an antibody-coated Enzyme-linked Immunosorbant Assay (ELISA) multi-well plate to identify the presence of a specific molecule based on its matching epitopes binding to the immobilized antibodies coating the plate wells;
 * the function of Cy5 coupled-ligands to enable separation of cells in a Fluorescence-Activated Cell Sorter (FACS) by virtue of cells possessing specific receptors with an specific affinity to bind the coupled ligand thereby causing only those cells to fluoresce when illuminated with laser light frequency that specifically excites Cy5, triggering relays and redirecting the solution flow to a separate effluent container;
 * the function of semi-permeable dialysis tubing to separate solutes based on size by virtue of restricted diffusion and osmosis.
 * the function of an image processing segmentation algorithm to automatically identify objects in digital images based on the pixel intensity values they contain.
 * the function of the electromagnetic lenses in an electron microscope to direct the incident electron beam trajectory so as to systematically raster across a specimen when constructing the overall image.