Integration of Biological Data (LifeDB) Presented By Md. Shazzad Hosain ([email protected]) Supervised By Dr. Hasan Jamil ([email protected]) Wayne State.

Integration of Biological Data (LifeDB)

Presented ByMd. Shazzad Hosain ([email protected])

Supervised ByDr. Hasan Jamil ([email protected])

Wayne State University, Detroit, USA

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Outline

Data Integration WebFusion (our previous work) LifeDB (our goal) Research Scopes

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Data Integration Example

Detroit to Bologna air ticket Alitalia, Italy Airline Air France NorthWest Airline Lufthansa etc.

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Integration Example cont.

CheapAir.com / Expedia.com

Alitalia Lufthansa Air France Delta

myAirFare.com

CheapAir.com Expedia.com ……

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Integration Approaches

Warehouse Integration

Mediator based Integration

Navigational Integration

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Warehouse Integration

Materialize data from all sources to local warehouse

Emphasize data translation rather query translation

Advantages: Low network bottleneck, efficient Disadvantages: reliability in terms of most up

to date data, system maintenance

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Mediator – based Integration

Concentrates on Query translation GAV approach and LAV Approach

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GAV Approach

Query reformulation easy, but addition or removal of sources are difficult

Preferred when sources are known an stable

S1 S2 S3 S4

Mediator Schema

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LAV Approach

Query reformulation is difficult but addition or removal of source are easy

Appropriate for large scale ad-hoc integration ARIADNE, Discovery Link, TAMBIS, KIND etc

Mediator Schema

S1 S2 S3 S4

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Navigational Integration

Some sources provide information that would not/hardly be accessible without point-and-click navigation

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WebFusionDr. Liangyou Chen

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LinkDB

DBGET

KEGG Pathways

Can these be done electronically for a biologist?

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Go to: http://www.ncbi.nlm.nih.gov/LocusLink/

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Click <Register Web Process> menu

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2. Press <Pickup Input> button

1. Input: 103730

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1. Press <Next> button

2. Press [Go] button

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1. Mark the table

2. Press <Pickup Table> Button

04/18/23 20Press the <Create> Button

04/18/23 212. Press the <Update & Redraw> Button

1. Uncheck all Boxes except 2~6

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1. Give it a name called: LocusLink

2. Name them as: Link, LocusID, Org, Symbol, Descriptionrespectively

3. Select appropriate transformations

4. Press <Update & Redraw> button

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Press <Confirm & Create Table>

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LocusLink web process is created

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LinkDB

DBGET

KEGG Pathways

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1. Select ‘LocusLink’ table

2. Type in ‘LocusLinkQuery’ as a query name

3. Check these fields to display

4. Double click here

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1. Select ‘local_gene_ids’ table

2. Select ‘LID’ field

3. Click here (any place)

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Click <This Query> button

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Press <Execute> button

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Here shows in progress results

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LifeDB

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LinkDB

DBGET

KEGG Pathways

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Resource Discovery Automatic Schema/Ontology Matching Query Optimization WorkFlows

LifeDB

BioFlow (A declarative WorkFlow Language)

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Glimpse of BioFlow

GeneBankURL FlyBaseURL

DNA sequence repositories

EMBL formatGeneBank format

Combine these sequence

Reading Frame Predictor (input_seq : FASTA format, species)

Score and predicted DNA region

University of Minnesota

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BioFlow

workflow open_reading_frame ; use ontology BioSystems ; declare found logical, count int ; define data sequences_1 at GeneBankURL as (seq_1 DNA) ; define tool orf at URL parameter (seq DNA, target organism)

results (score int, predicted_region DNA) ; combine sequences_1, sequences_2 into sequences (seqs); select seqs, orf (seqs, “drosophila”) from sequences ;

Goal is to develop a formal BioFlow language syntax with compositionality, closure property and type safety

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Research Scope

Resource Discovery Automatic Schema/Ontology Matching Query Optimization WorkFlows

7-8 PhD positions 3-5 years funding

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Thanks to all