eScience-School-Oct2012-Campinas-Brazil

The buzz around reproducible bioscience data:
the policies, the communities and the standards

Susanna-Assunta Sansone, PhD
Principal Investigator and Team Leader,
University of Oxford e-Research Centre, Oxford, UK

Slides at:
http://www.slideshare.net/SusannaSansone

SPSAS e-SciBioEnergy Sao Paolo School of Advanced Science on
e-Science for Bioenergy Research, 22-26 Oct, 2012, Campinas, Brazil

Lab scientist!

Data scientist!

Team Leader! Consultant!

Oxford e-Research Centre

Providing research
computing, high-
performance
computing
Integrating with
national and
international
infrastructure

Supporting leading
edge facilities through
education and training

Oxford e-Research Centre

Collaborating with European and wider
international groups in, e.g.:
•  energy,
•  radio astronomy,
•  biological data federation,
•  life sciences simulation,
•  biodiversity,
•  computational chemistry,
•  neuroscience,
•  digital humanities tools,
•  digital music analysis

Research in
•  computation,
•  data infrastructure and analysis,
•  visualisation

My team’s activities and groups we work with
data management and biocuration, collaborative development
of software and database, standards and ontology

•  environmental genomics •  stem cell discovery
•  metabolomics •  system biology
•  metagenomics •  transcriptomics
•  nanotechnology •  toxicogenomics
•  proteomics •  environmental health

env
agro

tox/pharma
health

http://www.flickr.com/photos/12308429@N03/4957994485/ CC BY

Outline

“The buzz around reproducible bioscience data:
the policies, the communities and the standards”

“The reality from the buzz:
how to deliver reproducible bioscience data”

Preserve
institutional /
corporate
memory
Harmonize collection across sites
Find matching studies
Data dissemination
Long-term data stewardship
10

Utilize
public data

Identify suitable data
Retrieve
Curate and harmonize
Re-analyze

11

Address
reproducibility /
reuse
of public data

12

Address
reproducibility /
reuse
of public data

13

Address
reproducibility /
reuse
of public data

Ioannidis et al., Repeatability of published microarray
gene expression analyses. Nature Genetics 41(2),
14
149-55 (2009) doi:10.1038/ng.295

Address
reproducibility /
reuse
of public data
15

15

Address
reproducibility /
reuse
of public data

16

16

Address
reproducibility /
reuse
of public data
17

17

http://www.flickr.com/photos/notbrucelee/8016189356/ CC BY

COMPREHENSIBLE
INTEROPERABLE
REPRODUCIBLE
REUSABLE

http://www.flickr.com/photos/notbrucelee/8016189356/ CC BY

Growing, worldwide movement for reproducible research

Shared, annotated research data and methods offer new discovery
opportunities and prevent unnecessary repetition of work.
Improved data sharing underpins science of the future

“Publicly-funded research data are a public good,
produced in the public interest”
“Publicly-funded research data should be openly available
20
to the maximum extent possible”
The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone

Growing, worldwide movement for reproducible research

esoteric formats comprehensible?
lack of sufficient interoperable?
contextual
information reusable?
hoc or proprietary
terminology reproducible?

§  Researchers and bioinformaticians in both academic and commercial
science, along with funding agencies and publishers, embrace the
concept that community-developed standards are pivotal to structure
and enrich the annotation of
•  entities of interest (e.g., genes, metabolites, phenotypes) and
•  experimental steps (e.g., provenance of study materials,
technology and measurement types)

Structure and enrich description of the experiments
§  Describe and communicate the information in an unambiguous,
human and machine readable manner

Seven week old C57BL/6N mice were treated
with low-fat diet.
Liver was dissected out, RNA prepared…etc.

Age value
Unit
Strain name
Subject of the experiment
Type of diet and Type of protocol - sample treatment
experimental condition Type of protocol - nucleic acid extraction
Anatomy part

Structure and enrich description of the experiments
§  Describe and communicate the information in an unambiguous,
human and machine readable manner

Figure: credit to
OBI consortium

Reproducible &
Reusable
Bioscience Research

reasoning visualization
analysis browsing integration
exchange retrieval

Well-annotated &
Structured Data

Reproducible &
Reusable
Bioscience Research

reasoning visualization
analysis browsing integration
exchange retrieval

Community Software
Standards Tools
Well-annotated &
Structured Data

Reproducible &
Reusable
Bioscience Research

http://www.flickr.com/photos/lamerentertainment/1581770980/sizes/m/in/photostream/

Today’s bioscience research
Publications
Experimental
and
computational
data

§  Is interdisciplinary and integrative in character
•  need to deal with new and existing datasets
•  deal with a variety of data types
§  ‘How the organism works’ is the focus
•  Twenty years ago data was the center
Source of the figure: EBI website

29 The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone
Source: http://ebbailey.wordpress.com
www.ebi.ac.uk/net-project

Example from the toxicogenomics domain

Study looking at the effect of a
compound inducing liver damage
by characterizing/measuring
- the metabolic profile by MS and
NMR
- protein expression in liver by MS
- gene expression by DNA
microarray
-  conducting genetic and
phenotypical analysis
Information contributing to the
construction and validation of
system biology models

Example of experiments by
InnoMed PredTox
31 The International Conference on Systems Biology (ICSB), 22-28 August, 2008 a FP6 public-private consortium
Susanna-Assunta Sansone

Structured description of datasets

§  Capture all salient features
of the experimental workflow
§  Make annotation explicit and
discoverable
§  Structure the descriptions
for consistency, tracking
§  independent variables
§  dependent variables
using
§  cross reference and
resolvable identifiers

Not too much, not too little, just ‘right’

§  We must strike a balance
between
•  depth and breadth of
information; and
•  sufficient information
required to reuse the data

Information intensive experiments

Information intensive experiments

To make the experiments
comprehensible and reusable,
underpinning future
investigations, we need
common ways to report and
share the experimental details
and the associated data.

Consistent reporting will have a
positive and long-lasting impact
on the value of collective
scientific outputs.

Common ways to report and share

§ The challenges we face
•  Large in volume: lots of data types and metadata!
•  Lots of free text descriptions: hard to mine, subject to mistakes!
•  Babel of terminologies: lack of definitions, hard to map!
•  Heterogeneous file formats: software lock-in!
§ Need for reporting standards
•  Minimal reporting descriptors
- Report the same ‘core essentials’
•  Controlled vocabularies or ontology
- Use the same word and mean the same thing
•  Common exchange formats
- Make tools interoperable, allow data exchange and integration

Reporting standards – the benefits

§  Describe and communicate the information to others, in an
unambiguous manner
§  To unlock the value in the data
•  Compare, query and evaluate data
- Facilitate scientific validation of the findings
•  Understand variability within/between different technologies and
protocols
-  Facilitate technical validation
-  Enable optimization of the experimental designs
-  Identify critical checkpoints and develop quality metrics
§  To define submission and/or publication requirements
•  Journals
•  Databases
§  To ensure data integrity, reproducibility and (re)use

Escalating number of standardization efforts in bioscience,
e.g.:
Genomics Standards
Genome annotation Consortium (GSC)
www.geneontology.org gensc.org

Functional Enzymology data
Genomics Data standards
Society (FGED) www.strenda.org
www.fged.org

HUPO- Proteomics
Standards Initiative (PSI) Systems modelling
http://www.psidev.info standards
www.co.mbine.org
Cheminformatics
www.ebi.ac.uk/chebi
Pathways
www.biopax.org

Metabolomics Standards Initiative (MSI)
http://www.metabolomicssociety.org

Different community, different norms and standards, e.g.:

use the same word and
allow data to flow from report the same core,
refer to the same ‘thing’
one system to another essential information

Challenges:
lack of coordination, fragmentation and uneven coverage

Is this ‘general mobilization’ good or bad?


§  Difference in structures and processes:
•  organization types (open, close to members, society, WG…)
•  standards development (how to design, develop, evaluate, maintain…)
•  adoption, uptake, outreach (link to journals, funders, commercial sector…)
•  funds (sponsors, memberships, grants, volunteering…)

Is this ‘general mobilization’ good or bad?


§  Fragmentation of the standards is a major issue
•  Being focused on particular communities’ interests, be their individual
technologies or biological/biomedical disciplines, leads to duplication of effort,
and more seriously, the development of (largely arbitrarily) different standards
•  This severely hinders the interoperability of databases and tools and ultimately
the integration of datasets

Fragmentation of the databases and data, e.g.

Access
Storage
Submission

Three EBI
omics systems

Fragmentation of the databases and data, e.g.

Access
DIFFERENT
Download formats

DIFFERENT
- Core requirements
Storage
represented
- Representation of the
studies and related
samples
- Curation practices

DIFFERENT
Submission

Formats, terminologies and
tools

Three EBI
omics systems

To integrate data we need interoperable standards
epidemiology
plant biology microbiology

Biologically-delineated
views of the world

Generic features ( common core )
- description of source biomaterial
- experimental design components

MS MS

Arrays Gels NMR Technologically-delineated
Columns FTIR
views of the world
Scanning Arrays &
Scanning Columns

transcriptomics metabolomics
transcriptomics

Need to address the fragmentation

§  Promote synergies
•  Among basic academic (omics) research but also regulatory- or
healthcare-driven initiatives
§  Much could be learned from exchange of ideas and practices
•  Although, regulatory- or healthcare-driven initiatives have far stricter
guidelines
•  Although, often SDOs have ‘close’ discussions, require membership
§  Create interoperable standards
•  Fit neatly into a jigsaw, resolving inconsistency and filling gaps
§  Overcome several barriers
•  Technical
•  Funding issue
•  Sociological......

Eloquent quotes

“Biologists would rather share their toothbrush
than their gene name”
Michael Ashburner, Professor Genetics,
University of Cambridge, UK

“Any customer can have a car painted any
colour that he wants so long as it is black”

Henry Ford, you know who he is…


Standards – an old issue, e.g. engineering in 1850

§  Buying nuts and bolts is easy today
•  But in the 19th century it was very complicated!

Standards – an old issue, e.g. engineering in 1850

§  Buying nuts and bolts is easy today
•  But in the 19th century it was very complicated!
§  Nuts and bolts were custom made
•  Products from different shops were incompatible
•  Craftsmen liked the monopoly
- Customers were ‘locked in’ !!
§  In 1864 William Sellers initiated the standardization
•  Mass production
•  Get interchangeable parts
•  Standardized way to make nuts and bolts
§  Generally adopted only after WWII, though …. !!

Social engeneering


Ownership of open standards
can be problematic in broad,
grass-root collaborations; it
requires improved models, to
encourage maintenance of and
contributions to these efforts,
supporting their evolutions


The extensive community
liaison needs to be managed
and funded; rewards and
incentives need to be identified
for all contributors


The cost of implementing a
standards-supported data
sharing vision is as large as the
number of stakeholders that
must operate synchronously


1. Funders actively developing data policies

§  Several data preservation, management and sharing policies have
emerged in response to increased funding for omics domains
§  Even if in general terms, standards are recognized as necessary ‘tools’ to
unambiguously represent, describe and communicate research data

2. Similar trend in the regulatory arena

§  “… lack of standardized data affects CDER’s review processes by curtailing a
reviewer’s ability to perform integral tasks such as rapid acquisition, storage,
analysis......efficient management of a portfolio of standards projects will
require coordinated efforts and clear roles for multiple participants within/outside
FDA”

3. Publishes have become strong advocators

§  Continue to support the development of open standards and tools
•  to support sharing of sufficiently well annotated datasets
59 •  to enable comprehensible, reusable, www.ebi.ac.uk/net-project research
reproducible
The International Conference on Systems Biology (ICSB), 22-28 August, 2008
Susanna-Assunta Sansone

….the rise of data-driven journals, e.g.:

partnering with:

The rise of data-driven journals, e.g.:

partnering with:

4. Similar trend in the commercial sector

§  R&D has invested heavily in procedures and tools that integrate external
information with their own data to enhance the decision-making process
•  Now joining forces to streamline non-competitive elements of the life
science workflow by the specification of common standards, business
terms, relationships and processes

....their information landscape is evolving

Yesterday Today Tomorrow
Proprietary
Public content
content provider
provider

Big Life
Science Big Life CRO
Academic
Company Science
group
Company
Regulatory
authorities

Service provider
Software vendor

Yesterday Today Tomorrow
Innovation Innovation inside Searching for Innovation Heterogeneity of collaborations; part of
the wider ecosystem
Model
IT Internal apps & data Struggling with change Cloud, services
security and trust

Data Mostly inside In and out Distributed

Portfolio Internally driven and owned Partially shared Shared portfolio

Credit to: Pistoia Alliance

http://www.flickr.com/photos/idiolector/289490834/ CC BY

Take home messages


u  Contribute to the reproducible research movement

u  Learn about open community-standards in your area

u  Consider data science as a career path

How do we achieve this? Is it possible to achieve a common,
structured representation of diverse bioscience experiments
that:
•  “The buzz around reproducible bioscience data:
follows the appropriate community standards and
COMPREHENSIBLE
•  the policies, E R Ocommunities research?standards”
delivers I N T the P E R A B L E and the
REPRODUCIBLE
REUSABLE


Growing number of reporting standards

MAGE-Tab! AAO! miame!
GCDML! MIAPA!
CHEBI!
SRAxml! OBI! MIRIAM!
VO!
SOFT! MIQAS!
FASTA! PATO! MIX!
CML! ENVO! REMARK!
DICOM! MIGEN!
GELML! MOD!
SBRML! MIAPE! MIQE!
TEDDY!
MITAB! MzML! XAO! CIMR! CONSORT!
BTO!
ISA-Tab! SEDML…! DO PRO! IDO…! MIASE! MISFISHIE….!

Growing number of reporting standards
+ 303

+ 150
+ 130

Source: MIBBI,
Source: BioPortal

EQUATOR
Estimated

Databases,
annotation,
curation
tools
GCDML! MIAPA!
CHEBI!
VO!
SOFT! MIQAS!
FASTA! PATO! MIX!
CML! ENVO! REMARK!
DICOM! MIGEN!
GELML! MOD!
SBRML! MIAPE! MIQE!
TEDDY!
BTO!

But how much do we know about these standards

GCDML! MIAPA!
CHEBI!
VO!
SOFT! MIQAS!
FASTA! PATO! MIX!
CML! ENVO! REMARK!
DICOM! MIGEN!
GELML! MOD!
SBRML! MIAPE! MIQE!
TEDDY!
BTO!

Which tools and I use high throughput
databases sequencing technologies,
implement which which one are applicable
standards? to me?

How can I get
What are the
involved to
criteria to evaluate
propose
their status and
extensions or
value?
modifications?

Which one are I work on plants,
mature enough for are these just for
me to use or biomedical
recommend? applications?


§  A bewildering array of standards is available, but
•  these are hard to find, at different levels of maturity; in
some areas duplications or gaps in coverage also exist

§  Standards are just a ‘means to an end’, therefore
•  we want to make them discoverable and accessible,
maximizing their use to assist the virtuous data cycle,
from generation to standardization through publication to
subsequent sharing and reuse

(2007) Vol 25 No 11

obofoundry.org

Towards Lego-like ontologies

§  Compound terms should be formed out of simpler constituents:
•  Body weight
weight (quality ontology, PATO)
that
inheres_in (relation ontology, RO)
whole_organism (anatomy ontology, CARO)

•  Xylene contaminated soil
soil (environmental ontology, EnvO)
that
has_contaminated (relation ontology, RO)
xylene (chemical ontology, ChEBI)


(2008) Vol 26 No 8

mibbi.og

§ Serves researchers, biocurators, journal editors
and reviewers, and funders to
§  discover checklists for a particular domain
§  monitor progress of extant efforts
§  facilitate collaborations

Science
(2009),Vol 326, 234-236

http://biosharing.org

A catalogue to map the
landscape of standards and the
systems implementing them:
Over 400 bio-standards
(public and in curation)
Field*, Sansone* et al., Omics data sharing. Science
326, 234-36 (2009) doi:0.1126/science.1180598

•  A coherent, curated and searchable catalogue of data sharing resources
•  Bioscience standards and associated data-sharing policies, publications, tools and databases
•  Assessment criteria for usability and popularity of standards
•  Relationships among standards
•  Encouragement for communication & interaction among groups
•  Promoting interoperability & informed decisions about standards

Smith et al, 2007


Smith et al, 2007

Taylor, Field, Sansone et al, 2008


List of databases, linked to standards a collaboration with Database Issue


Major challenge: define ‘relations’ among standards

CREDIT:
The relationship among popular standard formats for pathway information Demir, et al., The BioPAX
BioPAX and PSI-MI are designed for data exchange to and from databases and community standard for
pathway and network data integration. SBML and CellML are designed to pathway data sharing,
support mathematical simulations of biological systems and SBGN represents 2010.
pathway diagrams.

Example of multi-assays study – how many ‘standards’
are applicable to this?

An exemplar approach to the status quo

§  A grass-root collaborative that works to facilitate collection, curation
and sharing of experiments using a common, structured representation
of the experiments that
•  transcends individual biological and technological domains and
•  can be ‘configured’ to implement (several of) the community
standards

TOWARDS INTEROPERABLE BIOSCIENCE DATA doi:10.1038/ng.1054

Sansone SA, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O, Fang H, Neumann
S, Tong W, Amaral-Zettler L, Begley K, Booth T, Bougueleret L, Burns G, Chapman B,
Clark T, Coleman LA, Copeland J, Das S, de Daruvar A, de Matos P, Dix I, Edmunds S,
Evelo C, Forster M, Gaudet P, Gilbert J, Goble C, Griffin J, Jacob D, Kleinjans J, Harland
L, Haug K, Hermjakob H, Sui S, Laederach A, Liang S, Marshall S, Merrill E, McGrath A,
Feb 2012
Reilly D, Roux M, Shamu C, Shang C, Steinbeck C, Trefethen A, Williams-Jones B,
www.biosharing.org www.isacommons.org
Wolstencroft K, Xenarios J, Hide W.
www.isacommons.org

An exemplar approach to the status quo

§  A grass-root collaborative that works to facilitate collection, curation
and sharing of experiments using a common, structured representation
of the experiments that
•  transcends individual biological and technological domains and
•  can be ‘configured’ to implement (several of) the community
standards

metadata tracking framework

user community


General-purpose, configurable format,
designed to support the use of several
standards checklists, terminologies and
conversions to (a growing number of) other
metadata formats, used by public
repositories, e.g.

MAGE-Tab Pride-xml

SRA-xml SOFT

ISA software suite: supporting standards-compliant experimental
annotation and enabling curation at the community level
(Rocca-Serra et al, 2010)
a collaborative effort of international research/service groups:
University of Oxford, EBI, Harvard School of Public Health, NERC Environmental
Bioinformatics Centre, Genomic Standards Consortium, US FDA Center for
Bioinformatics, Leibniz Institute of Plant Biochemistry and more….

Create template(s) to ﬁt the type of
experiments to be described

Create templates detailing the steps to be
reported for different investigations, complying
to community standards, e.g. configuring the
value(s) allowed for each field to be
•  text (with/without regular expression testing),
•  ontology terms,
•  numbers etc.

1

Describe, curate your experiment

with geographically- distributed
collaborators

Report and edit the description of the
investigation using customized Google Spreadsheets
(importing the ‘template’ created by the ISA
conﬁgurator) enabled with ontology search and
term-tagging features.

2a

Or describe, curate your experiment
using a desktop-based tool

Report and edit the description using this tool,
(also customized using the templates) with a
spreadsheet like look and feel, packed with
functionalities such as

• ontology search (access via )

• term-tagging features

• import from spreadsheets etc…

2b

ISMB tag:
#PP44

To mint DOIs


empowering researchers to use standards

Perform data analysis

We are building relevant ISA modules for GenomeSpace,

R-based BioConductor and Galaxy tools

3

Share your experiments with the
world as Linked Open Data

Through conversion to RDF; work in
collaboration with the W3C HCLSIG

4

Share your experiments with the
world as Linked Open Data

Through conversion to RDF; work in
collaboration with the W3C HCLSIG

4

Tim Berners-Lee’s 5-star deployment scheme for Linked Open Data

Submit your experiments to public repositories

Directly in ISA-Tab or reformatting using the ISAconverter

5

Create your own repository

Store the investigations in the database, assign access rights and
conduct maintenance tasks.

Share, browse, query and view investigations, their
descriptions and access associated data ﬁles.

6

Maguire E, Rocca-Serra P, Sansone SA, Davies J and Chen M.
Taxonomy-based Glyph Design -- with a Case Study on Visualizing
Workflows of Biological Experiments,
IEEE Transactions on Visualization and Computer Graphics, volume 18, 2012
(in press)

A growing ecosystem of over 30 public and internal resources
using the ISA metadata tracking framework (ISA-Tab and/or
format) to facilitate standards-compliant collection, curation,
management and reuse of investigations in an increasingly diverse
set of life science domains, including:
•  environmental health •  stem cell discovery
•  environmental genomics •  system biology
•  metabolomics •  transcriptomics
•  metagenomics •  toxicogenomics
•  nanotechnology •  also by communities working to build
•  proteomics, a library of cellular signatures

Implementations at Harvard

Importance of a local community

Implementations at Harvard

data sharing
in ISA-Tab

Importance of a local community

Implementation at the EBI


Extensions of the

Nanotechnology
Informatics Working Group


Open source code

Community involvement and uptake!
1st ISA-Tab workshop! 3rd ISA-Tab workshop! User workshops/visits - start! 1st public instance: !
2nd ISA-Tab workshop! Other tools implement ! Harvard Stem Cell ! Growing number of
ISA-Tab! Discovery Engine! systems starts to adopt
ISA framework!

Core developments!
Conversions to ! Links to
Pride-XML/SRA-XML/! analysis tools
Strawman ISA-Tab spec! ISA software v1! MAGE-Tab and more! starts!
Final ISA-Tab spec! Database instance !
at EBI! RDF format starts!

Publications!
Stem Cell !
ISA-Tab and ! Discovery ! ISA Commons!
Omics data sharing!
Workshop reports! ISA software suite! Engine!
(Science)! (Nature Genetics)!
(Bioinformatics)! (NAR)!

2007 2008 2009 2010 2011 2012
Development timeline

Final remarks



Your research and all (publicly
funded) research should make
make an … impact

http://www.flickr.com/photos/equinoxefr/2620239993/ CC BY

…..the biggest possible impact!

http://www.flickr.com/photos/webhamster/2582189977/ CC BY


http://www.flickr.com/
photos/andrevanbortel/
3745527869/sizes/m/in/
photostream/

We must increase the level of annotation

Notes in Lab Books Spreadsheets and Tables Facts as RDF statements
(information for humans) ( the compromise) (information for machines)

•  Invest in curating and manage data at the source using:
•  a common metadata tracking framework, such as ISA
•  publicly available and community-developed terminologies
•  recording sufficient contextual information of the experimental steps
§  Progressively datasets will become more comprehensible, interoperable,
reproducible and (re)usable, underpinning future investigations

eScience-School-Oct2012-Campinas-Brazil

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