data integration via xml ela hunt john wilson vangelis pafilis inga tulloch

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


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)


SyntenyVistaSyntenyVista


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400 genes (interpreting 100 genes took 6 months, now the information is out of date)


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


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


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


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


• 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


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


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


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


• Paths and tags indexed using integer encoding, preserving XML order

• Indexing of Medline and OMIM needs to be resolved (text + XML)


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


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


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


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


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)


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

data integration via xml ela hunt john wilson vangelis pafilis inga tulloch

Documents

data slide

glasgow syntenyvista

glasgow scenario

work slide

querying slide

date slide

information accessible

real genes