Modelling and computingthe quality of information in e-science (a Qurator talk)

1. Modelling and computing the quality of information in e-science Paolo Missier , Suzanne Embury, Mark Greenwood School of Computer Science University of Manchester, UK Alun Preece, Binling Jin Department of Computing Science University of Aberdeen, UK http://www.qurator.org Roma, 3/4/07

4. Taxonomy for data quality dimensions

7. Correctness in biology - examples No false positives: Every protein in the output is actually present in the cell sample Generate peptides peak lists, match peak lists (eg Imprint) Qualitative proteomics: Protein identification No false positives, no false negatives Microarray data analysis Transcriptomics: Gene expression report (up/down-regulation) Functional annotation f for p correct if function f can reliably be attributed to p Manual curation Uniprot protein annotation Correctness Creation process Data type

10. Example: protein identification Data output Protein identification algorithm “ Wet lab” experiment Protein Hitlist Protein function prediction Correct entry  true positive This evidence is independent of the algorithm / SW package It is readily available and inexpensive to obtain Evidence : mass coverage (MC) measures the amount of protein sequence matched Hit ratio (HR) gives an indication of the signal to noise ratio in a mass spectrum ELDP reflects the completeness of the digestion that precedes the peptide mass fingerprinting

11. Correctness of protein identification Estimator function: (computes a score rather than a probability) PMF score = (HR x 100) + MC + (ELDP x 10) Prediction performance – comparing 3 models: ROC curve: True positives vs false positives

14. Layered definition of Quality DB DB Data sources custom quality knowledge Quality Assertions functions QA QA QA Quality Views: definition of acceptability regions QV QV QV QV quality evidence annotations Env Annotation functions Long-lived reusable Commodities Expert-defined Dynamic User controlled

20. Example: original proteomics workflow Taverna workflow Quality flow embedding point

21. Example: embedded quality workflow

22. Interactive conditions / actions

23. Generic quality process pattern Collect evidence - Fetch persistent annotations - Compute on-the-fly annotations <variables <var variableName=&quot; Coverage “ evidence=&quot; q:Coverage &quot;/> <var variableName=&quot; PeptidesCount “ evidence=&quot; q:PeptidesCount &quot;/> </variables> Evaluate conditions Execute actions <action> <filter> <condition> ScoreClass in {``q:high'', ``q:mid''} and Coverage > 12 </condition> </filter> </action> Compute assertions Classifier Classifier Classifier <QualityAssertion serviceName=&quot; PIScoreClassifier &quot; serviceType=&quot; q:PIScoreClassifier &quot; tagSemType=&quot; q:PIScoreClassification &quot; tagName=&quot; ScoreClass &quot; Persistent evidence

24. Reference (semantic) model quality evidence annotations custom quality knowledge DB DB Env Data sources Annotation functions Quality Assertions functions QA QA QA Quality Views definition of acceptability regions QV QV QV QV Common Semantic Model (IQ Ontology)

25. A semantic model for quality concepts Quality “upper ontology” (OWL) Evidence annotations are class instances Quality evidence types Evidence Meta-data model (RDF)

26. Main taxonomies and properties assertion-based-on-evidence: QualityAssertion  QualityEvidence is-evidence-for: QualityEvidence  DataEntity Class restriction: MassCoverage   is-evidence-for . ImprintHitEntry Class restriction: PIScoreClassifier   assertion-based-on-evidence . HitScore PIScoreClassifier   assertion-based-on-evidence . Mass Coverage

27. The ontology-driven user interface Detecting inconsistencies: no annotators for this Evidence type Detecting inconsistencies: Unsatisfied input requirements for Quality Assertion

28. Qurator architecture

29. Quality-aware query processing