Attacking the Biobank Bottleneck

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Attacking the Biobank Bottleneck

Professor Jan-Eric Litton

BBMRI-ERIC

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Big Data meets research

biobanking

Big data is volume, velocity and

high-variety information assets that demand

cost-effective, innovative forms of information

processing for enhanced insight and decision

making.

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Big Data meets research

biobanking

• Access to high quality human biological

samples and associated data

• BioBankCloud

3 © B B M R I-E R IC

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Seven Pillars of BBMRI-ERIC

• Scientific excellence

• One type of infrastructure for Europe

• Access to high quality human biological

samples and associated data

• Access to high quality Biomolecular

resources

• Ethical and legal compliance

• Long-term sustainability

• International integration

WORK PROGRAMME 2014

Prepared for the Assembly of Members 2nd Session in Vienna on 2014/04/29 Final Version 3

HQ BBMRI-ERIC 2014/04/15

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BBMRI-ERIC get legal status

Founding Members of BBMRI-ERIC

Austria

Belgium

Czech Republic

Estonia

Finland

France

Germany

Greece

Italy

Malta

Netherlands

Sweden

Official Observers of BBMRI-ERIC

Norway

Poland

Switzerland

Turkey

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BBMRI-ERIC is today the largest

health oriented ERIC ever

launched In Europe !

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BBMRI-ERIC has a total

population of:

408 Million individuals

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How many samples??

1.6 billion tissues samples

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24 oktober 2014 9

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Libraries of Flesh: The Sorry State of Human Tissue storage

Every year, billions of dollars’ worth of research into the genetic underpinnings of autism, schizophrenia, diabetes, Alzheimer’s disease, and other devastating

disorders hinges on scientists’ ability to tap industrial quantities of cells and tissue. But Compton found that while our technology for decoding the inner workings of life is advancing dramatically, the protocols for collecting and storing specimens of human flesh have barely evolved in decades. At the same time, innovation in the field of biobanking has stalled for lack of funding and interest. The science of bio-preservation is still considered an arcane, musty specialty, more akin to taxidermy than medicine. “You might have thought that doing the science would be the biggest challenge of a massive undertaking like the Cancer Genome Atlas,” Compton told me last fall. “But acquiring the biospecimens turned out to be the hardest part, bar none. It’s the Wild West out there.”

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Libraries of Flesh: The Sorry State of Human Tissue storage

One bank at a major university claimed to have more than 12,000 samples of glioblastoma in its collection.

Only 18 of those were good enough to use.

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Nature Medicine, #3 March

2013

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Catalogue

13 © B B M R I-E R IC WORK PROGRAMME 2014

Prepared for the Assembly of Members 2nd_{Session in Vienna on 2014/04/29}

Final Version 3 HQ BBMRI-ERIC 2014/04/15

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24/10/2014 15 © B B M R I-E R IC

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MIABIS 2.0

• MIABIS has been slimmed down 52 –> 37 items to

make it easier to implement (Biobank, Sample

collection, Study)

• MIABIS is modularized to make it easier to propose

extensions / adhere to

• MIABIS is now accepted by large community of

BBMRI-ERIC countries

• MIABIS has a governance process to evolve it

• MIABIS is implemented in a wide range of

applications

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MIABIS 2.0

24/10/2014 17 © B B M R I-E R IC Biobank Sample Collection/Study Donors (Aggregated) Samples (Aggregated) Biobank Sample Collection Study Omics Participant Sample

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An Emerging framework for

Sample-related standard

24/10/2014 19 © B B M R I-E R IC

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CEN working documents: Molecular in vitro

diagnostic examinations – Specification for

pre-examination processes

• For blood – Circulating cell free DNA

• For blood – Genomic DNA

• For blood – Cellular RNA

• For metabolomics in urine, serum and plasma

• For FFPE tissue – DNA

• For FFPE tissue –RNA

• For FFPE-Proteins

• For frozen tissue – RNA

• For frozen tissue - Proteins

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SOP-mapping

24/10/2014 21 © B B M R I-E R IC

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CoBiBa (Connectiong BioBanks)

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Big Data meets research

biobanking

• Access to high quality human biological

samples and associated data

• BioBankCloud

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A Platform-as-a-Service for Biobanking

www.biobankcloud.com

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The Biobank Bottleneck

We will soon be generating more

genomic data than we can

currently securely store and

process

Urgent need for better systems

to store and process genomic

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Numbers

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Cost

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Cross-linking Genomics with Clinical Data

Imagine you have genomic data for hundreds of thousands of

Biobank samples

Imagine you can potentially cross-link the genomic data to

high-quality databases containing individualized data

concerning common diseases.

Phenotype

Data

Genomic

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Running on lots of machines…

PaaS

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Scalable storage on commodity hardware

Many genomics research groups still run expensive SANs

(storage area networks) or file systems that require expensive

interconnects.

Reliable Storage Service

Unreliable Commodity Servers

My data

please…

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Data Model

What immutable data should we store?

Raw Reads

All Reads (BAM file)

Variations (VCF file)

View of Reads/Variants?

What mutable data?

Meta-data

Stored compressed data?

Referential compression

Other compression?

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Flexible Computation Model

Ideally, we would like a computation model that is flexible

enough to address many different stages of analysis:

Sequence alignment

Gene querying

• Individual-level operations

• Population-level operations

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Summary

New systems needed to solve the Biobank Bottlenecks:

• The BBMRI-ERIC is attacking this problem by building an

Gateway for Health – to find samples fit for purpose.

• The BiobankCloud Project is attacking this problem by

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