1.
Making data useful without direct sharing:
Cafe Variome and Omics browsing

2.
• CANNOT: Data owners may not have time nor funding to
manually submit data, and/or submission process and
requirements too complicated
• WILL NOT: Data owners receive little or no recognition or
reward for releasing data, hence little incentive to try
• MUST NOT: Data owners may have good reasons for not
sharing data (ethical, legal, competitive edge)
Issues that restrict sharing data

3.
DATA SHARING
IS IMPORTANT
BUT DIFFICULT !

4.
SO DO
SOMETHING
ELSE
(AS WELL)

5.
Share the ‘existence’ rather than the ‘substance’ of data
This technology (or similar) sits atop/alongside existing
local DBs to bring the discoverability and connectivity,
without replacing or altering the local solutions

6.
Use Cases/Collaborating Networks
• Designed to be flexible for a number of use cases
• Various groups using the tool in different ways
– Rare disease (variant is the “entity”)
– Patient centric (patient is the “entity”)
– Aggregate frequency (i.e. mutation seen with a
frequency of X in population

7.
Café Variome Features
• Café Variome is not a database but is a searchable 'menu’
• The platform enables data custodians to specify which users
can search for, display counts of, or display details of, which
subsets of records and record fields, using various search
parameters
• Results can be returned to users:
- as core data
- as links to data at source
- by computationally facilitating data access requests

8.
• Federated Café Variome network
• Nodes populated with local data
• Data discovery/sharing options
under control of each source
• Data remain at each source
• Search interface enables real-time data/subject discovery
• Each discovered record reported in one of 3 ways, dependant
on users permissions and data source settings
Open Access Restricted Access Linked Access
Data provided Interface facilitates
email to request
data, followed by
data supply if/when
approved
No data provided,
only link to data source
Source DB
• Each source can
control which
data fields are
searchable
and which fields
are (potentially)
then returned

9.
Simple Query Interface

10.
Complex Query Interface

11.
Query Builder in action

12.
Controlled Display of Matched Record Counts
& Data (if permitted)

13.
Phenotype Semantics
• Allow the phenotypic consequences of genetic entities to be
described using public ontologies
– Many terms from many ontologies can be associated
with one entity
• Allow the phenotypic consequences of genetic entities to be
described using a local vocabulary or list
• Enable hierarchical viewing and querying of the phenotype
ontology data

14.
Node Search Options
• Searches are performed
through one nominated
head node
• Searches can be performed from
any node in the network
External searches
Head node
Internal searches

15.
Installation wizard
Appearance preferences, content
management system and statistics
reporting
Core system settings, defining
displayed and searchable
fields, bulk import template
configuration
Record and source management
User, group and
record access
control management
Multiple
Admin Options

16.
OmicsConnect:
• Enables collaborators, and (optionally) additional
researchers, to view/explore 'omics' datasets
• Provides a mechanism for visual data discovery
• Provides a unified browser view of ‘omics’ data
• Separates data sharing into open, controlled and
discoverable
• Cope with different data sources and formats
• Easy to setup and use

17.
GWAS Central
(www.gwascentral.org)
- comprehensive genetic association database
- aggregate data & extensive metadata
- links to data sources for primary data

18.
Eg. Visual meta-analysis:
Compares and contrasts 8 different studies

19.
The Browser

20.
OmicsConnect browser
Local
Data
Remote
Data

21.
Data
Sources
(DAS, GFF3, BED,
wiggle BigWig,
BigBed)
Files
FASTA
GFF3
GTF
Access Local Databases
MySQL
SQLite
Access Online Resource's
GWAS Central
Ensembl
UCSC
Display
Data
Simple Interface
Use new technologies
Low Demands on Resources
Platform Independent
No dependencies
OmicsConnect browser (Dalliance)

22.
Track authentication
DAS track enabled when passphrase entered
DAS track not enabled as no passphrase entered

23.
• Allows researchers to controllably serve their own omics data
– Authentication (public/private)
– User accounts
• Can returns the available features for a specific file/genome segment
• Intuitive interface for upload and management of data, including
validation
• Stylesheets: Instructions on how to format the data for viewing.
• Additional feature implementations to the DAS protocol
- ‘Types’: Returns what data types exist in the DAS track
- ‘Summary’: Returns a summary of the data features per segment
- ‘Search’: Returns features based on a keyword given by the user
• Can be installed independently from OmicsConnect
eDAS ‘gate keeper’

24.
Other Genome
Browsers
Enhanced
Distributed
Annotation
System
(eDAS)
Raw Files
Local
Databases
Online
Resource's
Remote
Access
Local Access
OmicsConnect
Browser
Online
Resource's
OmicsConnect & eDAS

25.
Acknowledgements
The research leading to these results has received funding from the EC
under the 7th Framework Programme (FP7/2007-2013) grant agreements
261433 (BioSHaRE-EU) and 200754 (GEN2PHEN), and the IMI projects
grants 115372 (EMIF) and 115736 (EPAD)
Tim Beck Robert Hastings
Charalambos Chrysostomou Robert Free
Adam Webb Owen Lancaster
Dhiwagaran Thangavelu Colin Veal
Morris Swertz et al
Alliance
Consortium