Big Biomedical DataPistoia Panel Discussion19th April 2016Mathew Woodwark, Director of Research BioinformaticsMedImmune

3

Goal: Integrative Informatics

Collect, Store and Integrate

Integrated data analysis

Biological Understanding

} Target IDTarget SelectionTarget ValidationBiomarkers

Public Collaborations Internal

Engineering better molecules, enabling better projects

Data Sources

Multiple OmicsData types

Tissue P

henomics

Flow C

ytometry

Screening

Proteom

ics

Transcriptomics

Exom

e

Whole G

enome

Clinical

Phenotype

Data Warehouse

Integrative Informatics Architecture

5

Portal and query layer

Data Marts

Integration Engine

Metadata and business

rules

Structured data

Semi-structured

data

Un-structured

data

Visualisation Layer

Queries to build data marts

Structured data

Semi-structured

data

Un-structured

data

External Internal

Rich viz components – display connected info in multiple formats

Organized by data type or by process. Data standards guide assembly

Data Extraction for further analysis in specialized tools

Reusable data connectors

Drill into underlying detailed data

Persistent and temporary marts

Able to integrate internal and external data

QC and ETL?

6

Genomics Big Data Considerations1: Genomic Data

7

Genomics Big Data Considerations2: Security and Access

8

Genomics Big Data Considerations3: Consent

9

Genomics Big Data Considerations4: Geography

10

Genomics Big Data Considerations5: Phenotypic data

11

Genomics Big Data Considerations6: Integration with other data

Tissue P

henomics

Flow

Cytom

etry

Screening

Proteom

ics

Transcriptomi

cs

12

Genomics Big Data Considerations7: Global Collaboration

Tissue P

henomics

Flow

Cytom

etry

Screening

Proteom

ics

Transcriptomi

cs

13

Genomics Big Data Considerations8: More integration

Tissue P

henomics

Flow

Cytom

etry

Screening

Proteom

ics

Transcriptomi

cs

14

Its complicated!And a work in progress…

…for all of us!