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Data integration via XML. Ela Hunt John Wilson Vangelis Pafilis Inga Tulloch. http://xtect.cis.strath.ac.uk/. Overview. Four biological scenarios of data integration Data integration - problem definition XTECT indexing approach Literature review Current status and further work. - PowerPoint PPT Presentation
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Data integration via XMLEla Hunt
John Wilson
Vangelis Pafilis
Inga Tulloch
http://xtect.cis.strath.ac.uk/
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Overview
• Four biological scenarios of data integration• Data integration - problem definition• XTECT indexing approach• Literature review• Current status and further work
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Scenario 1: Cardiovascular Functional Genomics
• AIM: discover genes causing hypertension• Rat animal models of hypertension (rat strains which
suffer from stroke)• Microarrays are used to compare gene expression in sick
and healthy rats, typically 100-400 genes are differentially expressed
• microarray results are visualised on maps – and data are interpreted using public web databases (browsing and querying)
Hunt, Wilson, Pafilis and Tulloch, Glasgow
SyntenyVistaSyntenyVista
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Scenario 2: Mouse mammary gland development as a
model of cancer proliferation
• AIM: find genes active in cancer growth• Take mouse samples and apply to a microarray slide• Measure trends in gene expression, identify 400 genes
of interest• Use public web databases to interpret information on
400 genes (interpreting 100 genes took 6 months, now the information is out of date)
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Scenario 3: Rat model of schizophrenia
• AIM: understand which genes are expressed during schizophrenia
• Rats have symptoms of schizophrenia after a chemical treatment (2 models are used)
• Measure gene expression in two models• Interpret data on 250 genes: find if microarray probes
correspond to genes by using BLAST (DNA sequence comparison) and PubMed (bibliographic database)
• Gather DNA sequences for real genes from Ensembl (BLAST hits), design probes
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Scenario 4:Proteomics
• AIM: understand and record protein functions• Case 1: study the proteome of Trypanosoma brucei. For
all proteins identified, find information on the web which might shed light on their function
• Case 2: interpret data on human proteins differentially expressed in human cells invaded by Toxoplasma gondii.
• Compare protein and gene expression• Use SwissProt, PubMed, GeneOntology and any other
web resources
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Problem definition
• Given a large microarray or proteomics experiment (a list of gene names or peptide masses)
• Find all known information about those genes or proteins on the web
• Make this information accessible
Hunt, Wilson, Pafilis and Tulloch, Glasgow
What we expect to achieve
Query: table ofnames
Result1: table of integrated information Result2:
map of probes and synteny
Result3: Clusters based onto the number of relevantquery terms found
Hunt, Wilson, Pafilis and Tulloch, Glasgow
• Use item matching - XML leaves - to start• Match starting from leaves and extend towards the
schemas expressed as paths• Use database techniques - indexing• Use data mining techniques – get statistics on data
Hunt, Wilson, Pafilis and Tulloch, Glasgow
More detail
• Index all paths and leaves in XML trees for a representative set of biological databases
• Relational technology• Warehouse• Match leaves (data values)• Find path overlaps => remove redundancies in data
Hunt, Wilson, Pafilis and Tulloch, Glasgow
First problem solved:query expansion
• 30K human, 30K rat, and 30K mouse genes, some of them have synonyms
• Query expansion to include the synonyms• Prototype in Java, 300 ms for synonym lookup• Same idea as in GeneCards which focuses on human
data
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Second – indexing XML
• Medline (40 GB) in XML (bibliographic)• SwissProt + Trembl, 1 GB in XML (proteins)• OMIM and HUGO databases of genes, small (human
diseases and human genes)• Affymetrix microarray files for the mouse, small, XML• Ensembl – no XML files, access via MySQL (human,
mouse, rat genomes and predicted genes)• Mouse Genome MGD – direct access to Sybase, no
XML• Rat database RGD – stores little data!• Gene Ontology – around 1GB in XML
Hunt, Wilson, Pafilis and Tulloch, Glasgow
• Paths and tags indexed using integer encoding, preserving XML order
• Indexing of Medline and OMIM needs to be resolved (text + XML)
Hunt, Wilson, Pafilis and Tulloch, Glasgow
How the index will work
Swiss-Prot
PubMedID
12345
GeneName
agene1
PubMed
accession
12345
abstract
.. interactions ofagene1 withagene2 ...
Swiss-Prot/PubMedID ~ PubMed/accessionSwiss-Prot/GeneName ~ PubMed/abstract
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Matching
• Db1/path1/socs3 and Db2/path2/socs3 => synonymous paths
• Get statistics for full and partial path matches and postulate schema matches
• Manually inspect the matched paths, and examine support for each path match
• Automate the procedure
Architecture
List of names
PubMed Sprot Affy OMIM Hugo
Datareplicas
Synonymexpander
WAREHOUSE
PROCESSING LAYERXML treefinder
XML treemerger
Gene treesXML
VisualisationINTERACTION
Mapping generation and lookup
Microarray experimentProteomics experiment
INDEX
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Status
• Mirroring external XML data• Query expansion is implemented• Software to XMLise OMIM and some of the
MGD• Testing indexing software for loading into Oracle• Designing an algorithm for data mining• Developing ideas on adding sequence
comparison and text retrieval, and connecting to visualisation tools (collaboration with e-Science project BRIDGES)
To sequence To multiplealignment
To tabularsummaries
THEVISION
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Other work
• Schema-based approaches: look at the schemas to find mappings between them– use constraints, tree shape, some data– involve the user/programmer: YATL, Clio, REVERE
• Data-based approaches: look at data values in order to find mappings between attributes– ML approaches are inefficient, all-against-all
• Problems:– Expensive in terms of labour (programmer or user)– Only very similar schemas can be matched– Not scalable
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Recent papers
• Kurgan et al., 2002, machine learning for schema matching (2 very similar schemas)
• Doan et al., VLDBJ03, machine learning, 2 semi-structured schemas (ontologies), schemas + some data
• Chua et al., VLDBJ03, (RDBMS) given entity matches (table names), match attributes (values), based on a variety of statistical tests
• Halevy et al, CIDR-2003, user-driven schema matching by example, and mapping by transitivity (no algorithm has been given)
Hunt, Wilson, Pafilis and Tulloch, Glasgow
Summary
• Aim - to overcome the problems associated with manual or schema-based mapping approaches which are expensive
• Scale up, take into account data values• Provide a digest of information for a list of
gene/protein names of interest• Using XML and relational indexes
Vangelis Pafilis
John Wilson
Collaborators at Glasgow
Barry Gusterson
Andy JonesTorsten SteinInga TullochCatherine WinchesterAnna F. DominiczakNeil HanlonBRIDGES project (uses DB2)
FUNDING: Carnegie Trust for the Universities of ScotlandMedical Research Council (UK)Royal SocietySynergy