WP4: Technical Integration

BioMedBridges Julie McMurry, Technical Project Lead

On behalf of WP4 partners

Goal: Technical Integration• WP4 delivers

connectors for data integration - built on modern web standards

• WP4 focusses on 11 prioritized use-cases that address real needs within BioMedBridges

• WP4 pilots employ a common technology “stack” which can be reused for the future

Main achievements Six deliverables complete (Use cases report, integration

paths report, webservices, user interfaces, semantic web scaleability)

Eleven pilot projects addressing BMB needs Impact:

Discovery Harmonisation Access Analysis Reuse

Nine BMS RI connected All partners have raised data integration readiness

Funded by FP7 Capacities Specific Programme, grant agreement no. 284209

Visualising and leveraging ontologies in queries

Sharing and integrating medical imaging data

Integrating mouse phenotype data for studying diabetes and obesity

Sharing protein engineering knowledge

Federating biobank queries for translational research

Biosample information integration and discovery

Leveraging the utility of compound screening functional assays

Connectivity-Based searching in UniChem

Connect clinical and lab workflows using TranSMART, Galaxy

Visualising ecological context for genome sequencing data**

Linking clinical trials to the drugs, genes, and results they reference

Gene

s

Prote

ins

Orga

nisms

Bios

ample

s

Dise

ases

Drug

s

Trial

sMe

dical

imag

ing

Use

-cas

e dr

iven

pilo

ts

inte

grat

e di

vers

e da

ta d

omai

ns

4.4, 4.7, 4.6 RDF, SemWeb

4.5 User interfaces &

query BioJS4.3

Programmatic access

Parallel-path developmentbuilt on common technology stack

of modern web standards

4.1, 4.2 Landscape and

planning“simple object

queries”

SemWeb Pilot PhasesDel # Due Deliverable

focus Nature of the activities

Prep D4.4 2013 Planning Overall roadmap

Phase I D4.7 Dec-14 SemWebscaleability

ELIXIR establishes mature services and guidelines. Benchmarks technology and assesses scaleability.

Phase 2 D4.6 Jun-15 Data integration Parallel pilots, according to respective roadmaps, using above guidance

Phase 3 D4.8 Dec-15 Report

Why Semantics and Standards(Ontologies, Identifiers, Formats)

Subject uniprot protein P15056 uniprot protein P15056

Relationship (aka ‘Predicate’)

is_product_of is_reagent_in

Object gene X reaction pathway Y

Tech

nolo

gies

Visualising and leveraging ontologies in queries

Sharing and integrating medical imaging data

Integrating mouse phenotype data for studying diabetes and obesity

Sharing protein engineering knowledge

Federating biobank queries for translational research

Biosample information integration and discovery

Leveraging the utility of compound screening functional assays

Connectivity-Based searching in UniChem

Connect clinical and lab workflows using TranSMART, Galaxy

Visualising ecological context for genome sequencing data**

Linking clinical trials to the drugs, genes, and results they reference

REST GUI RDF SrcCode BioJS

Biolo

gical

mode

ls

Drug

s and

othe

r ch

emica

ls

Gene

expr

essio

n da

ta

Molec

ular

inter

actio

ns &

pa

thway

s

Prote

in se

quen

ces

Biolo

gical

Samp

les

Gene

varia

tion

data

Geno

mic d

ata

Expe

rimen

tal

prote

in pr

oduc

tion

Mous

e

phen

otype

data

Clini

cal tr

ials

Funding for conversion

App

Progress on RDF conversions

Triples

Phase

prod prod prod prod prod prod

11.4M 375M 447M 12.5M 25B 102M 316M 80M* * 50000 2700Linked Open Data Cloud

Linked Open Data Cloud

prod prod prod prod prod prod

RDF platform Released Nov 2013 >16 billion RDF triples

Linked Open Data

Jupp et al (2013). The EBI RDF Platform: Linked Open Data for the Life Sciences. Bioinformatics.

Technical lessons LOD best practice

Collect use cases Publish the scheme Publish a SPARQL endpoint Provide representative queries Link to other datasets by using their URIs Communicate your data release cycle Test and validate your dataset Define the scope and supported use-cases Provide a license and citation info Have a strategy for long-term sustainability

https://github.com/dbcls/bh14/wiki/Ten-simple-rules-for-publishing-Linked-Data-for-the-Life-Sciences

Simplifying complex relationships supports data search and integration

http://tinyurl.com/linkeddataprovenance

Key lesson learnedStrategies that are:

driven by community use cases implement standards consider reusability from the start

…are inherently more sustainable

Funded by FP7 Capacities Specific Programme, grant agreement no. 284209

Sustainability and ReuseReuse Src code Hosting Bug fixes New

features Expertise Funding

Visualising and leveraging ontologies in queries CTTV, Euro BioImaging

Sharing and integrating medical imaging data CTMM-TRAIT

Integrating mouse phenotype data for studying diabetes and obesity CTTV

Sharing protein engineering knowledge

Federating biobank queries for translational research CTTV

Biosample information integration and discovery CTTV

Leveraging the utility of compound screening functional assays CTTV

Connectivity-Based searching in UniChem CTTV

Connect clinical and lab workflows using TranSMART, Galaxy

CTMM-TRAIT

Linking clinical trials to the drugs, genes, and results they reference ?

Most of the pilots (and lessons) are already being put to use within translational research

projects (eg. CTTV, CTMM-TRAIT)

Data Domains Technologies

Remaining work Continue parallel-path development of pilot projects

according to their respective roadmaps ErasmusMC: Clinical and imaging integration using TransMart EMBL: Semantic Search Widget HMGU: RDF transformation, Query TUM-MED: RDF transformation, Query STFC: RDF transformation, Query UDUS: RDF transformation, Query VUMC: Clinical and laboratory integration using TransMart

Make outputs of work more visible, accessible, and reusable (eg. BioJS widgets)

Prepare for end-of-project transition Open source repositories Documentation Reporting

Collaborators

17

ECRIN ELIXIR EMBRC BBMRI EATRIS, BBMRIUDUS CSC Stazione Zoologica Anton Dohrn - Napoli TUM-MED VUMC

Benjamin Braasch Tommi Nyrönen Remo Sanges Raffael Bild Ward Blonde

Christian Krauth EMBL-EBI Max Plank Institute Sabine Brunner Jeroen Belien

Christian Ohmann Ewan Birney Renzo Kottman Klaus Kuhn Freek de Bruijn

Martin Eckert Niklas Blomberg Ashish Lamichhane Jan Willem Boiten

Töresin Karakoyun Tony Burdett Willi Mann InfrafrontierNathalie Conte UMCG HMGU

Euro-BioImaging Adam Faulconbridge Dennis Hendriksen Philipp Gormanns

EMBL Jon Ison Morris Swertz Christoph Lengger

Jan Ellenberg Andrew Jenkinson Holger Maier

EMBL-EBI Rafael Jiminez EU-OPENSCREENGabriella Rustici Simon Jupp EBI Instruct

ErasmusMC James Malone Jon Chambers STFC

Stefan Klein Julie McMurry John P. Overington Narayanan Krishnan

Wiro Niessen Helen Parkinson Chris Morris

Erwin Vast Francis Rowland Martyn Winn

Drashtti Vasant

Danielle Welter

WP4 Acknowledgements

Funding: European Union Seventh Framework Programme, grant agreement number 284209

Bridging imagingand clinical data

BioMedBridgesStefan Klein, Erwin Vast, Wiro Niessen

Erasmus MC

Volume GM = 790 mlVolume WM = 497 ml

Medical imaging data

www.xnat.org

XNAT – Imaging platform

Bridging imaging and clinical data

Grey Matter Volume: 790 ml

MR image

No AlzheimerAlzheimer

Genetics + age

Derived data

Grey matter volume

Ris

k sc

ore

TranSMART

Bridging XNAT and TranSMART Created open-source TranSMART plugin to import

XNAT data

Bridging XNAT and TranSMART

New TranSMART administration panel.

XNAT data shown in TranSMART

explorer.

Conclusion XNAT: storing and sharing medical imaging

(derived) data TranSMART: analysis of translational research data Bridging: import image-derived data from XNAT into

TranSMART

Future work: Harmonize storage of image derived data Write documentation