Promoting Coherent Minimum Reporting Guidelines for Biological & Biomedical Investigations: The MIBBI Project

Chris Taylor, EMBL-EBI & NEBC chris.taylor@ebi.ac.uk

MIBBI [www.mibbi.org] HUPO Proteomics Standards Initiative [psidev.sf.net] Research Information Network [www.rin.ac.uk]

On standards bodies

What defines a standards-generating body?— A beer and an airline (Zappa)— Formats, reporting guidelines, controlled vocabularies— Regular open attendance meetings, discussion lists,

etc.e.g., MGED (transcriptomics), PSI (proteomics), GSC (genomics)

Hugely dependent on their respective communities— Requirements gathering (What are we doing and why?)— Development (By the people, for the people)— Testing (No it isn’t finished, but yes I’d like you to use

it…)— Uptake by stakeholders

— Publishers, funders, vendors, tool/database developers

— The user community (capture, store, search, analyse)

Technologically-delineated views of the world A: transcriptomics B: proteomics C: metabolomics …and…

Biologically-delineated views of the world A: plant biology B: epidemiology C: microbiology …and…

Generic features (‘common core’) — Description of source biomaterial — Experimental design components

Arrays

Scanning Arrays &Scanning

Columns

GelsMS MS

FTIR

NMR

Columns

Modelling the biosciences

Modelling the biosciences (slightly differently)

Assay: Omics and miscellaneous techniques

Investigation:

Medical syndrome, environmental effect, etc.Study: Toxicology, environmental science, etc.

Multiple all that by three (kinds of standard)

What biologists need

Diverse community-specific extensions

Generic Features (origin of biomaterial)

Generic Features (experimental design)

Diverse community-specific extensions

Generic Features (origin of biomaterial)

Generic Features (experimental design)

Well-oiled cogs meshing perfectly (would be nice)

How well are things working?—Cue the Tower of Babel analogy…—Situation is improving with respect to standards—But few tools, fewer carrots (though some

sticks)

Why do we care about that..?—Data exchange—Comprehensibility of work—Scope for reuse (parallel or orthogonal)

“Publicly-funded research data are a public good, produced in the public interest”

“Publicly-funded research data should be openly available to the maximum extent possible.”

Investigation / Study / Assay (ISA) Infrastructurehttp://isatab.sourceforge.net/

Ontology of Biomedical Investigations (OBI)http://obi.sourceforge.net/

Functional Genomics Experiment (FuGE)http://fuge.sourceforge.net/

Rise of the Metaprojects

Reporting guidelines — a case in point

MIAME, MIAPE, MIAPA, MIACA, MIARE, MIFACE, MISFISHIE, MIGS, MIMIx, MIQAS, MIRIAM, (MIAFGE, MIAO), My Goodness…

‘MI’ checklists usually developed independently, by groups working within particular biological or technological domains

— Difficult to obtain an overview of the full range of checklists

— Tracking the evolution of single checklists is non-trivial— Checklists are inevitably partially redundant one against

another— Where they overlap arbitrary decisions on wording and

sub structuring make integration difficult

Significant difficulties for those who routinely combine information from multiple biological domains and technology platforms

— Example: An investigation looking at the impact of toxins on a sentinel species using proteomics (‘eco-toxico-proteomics’)

— What reporting standard(s) should they be using?

The MIBBI Project (mibbi.org)

International collaboration between communities developing ‘Minimum Information’ (MI) checklists

Two distinct goals (Portal and Foundry)—Raise awareness of various minimum reporting

specifications—Promote gradual integration of checklists

Lots of enthusiasm (drafters, users, funders, journals)

31 projects committed (to the portal) to date, including:—MIGS, MINSEQE & MINIMESS (genomics, sequencing) —MIAME (μarrays), MIAPE (proteomics), CIMR

(metabolomics)—MIGen & MIQAS (genotyping), MIARE (RNAi), MISFISHIE

(in situ)

Nature Biotechnol 26(8), 889–896 (2008)

http://dx.doi.org/10.1038/nbt.1411

The MIBBI Project (www.mibbi.org)

[†] Denotes that a specification is provided as a suite of related documents

CONCEPT SPECIALISATION ● C

IMR [†]

● M

IACA

● M

IAM

E

● M

IAM

E/E

nv

● M

IAM

E/N

utr

● M

IAM

E/P

lant

● M

IAM

E/T

ox

● M

IAPA

● M

IAPE [†]

● M

IARE

● M

IFlo

wCyt

● M

IGen

● M

IGS/M

IMS

● M

IMIx

● M

IMPP

● M

INI

study inputs study design ●generic organism ●

cells / microbes

plant

animal

mouse

human

population

environmental sample

environment / habitat

in silico model

study procedures organism maintenance

animal husbandry

cell / microbe culture

plant cultivation

acclimation

preconditioning / pretreatment ●organism manipulation

assay inputs generic study input

organism part ●organism state

organism trait

biomolecule

synthetic analyte ●silencing RNA reagent

Version 0.7 (2008-04-10)

Comparison of MIBBI-registered projects [21] ● Release

Granularity Coarse Medium Fine

Maturity ● Planned ● Drafting

The MIBBI Project (www.mibbi.org)

The MIBBI Project (www.mibbi.org)

Interaction graph for projects (line thickness & colour saturation show similarity)

The MIBBI Project (www.mibbi.org)

MICheckout: Supporting Users

Why should I dedicate resources to providing data to others?

—Pro bono arguments have no impact—‘Sticks’ from funders and publishers get the bare minimum

This is just a ‘make work’ scheme for bioinformaticians—Bioinformaticians get a buzz out of having big databases—Bioinformaticians benefitting from others’ work

I don’t trust anyone else’s data — I’d rather repeat work—Problems of quality, which are justified to an extent—But what of people lacking resource for this, or people who

want to refer to proteomics data but don’t do proteomics

How on earth am I supposed to do this anyway..?—Perception that there is no money to pay for this—No mature free tools — Excel sheets are no good for HT—Worries about vendor support, legacy systems (business

models)

The objections to fuller reporting

Data sharing is more or less a given now, and tools are emerging

—Lots of sticks, but they only get the bare minimum—How to get the best out of data generators?—Only meaningful credit will work

Need central registries of data sets that can record reuse—Well-presented, detailed papers get cited more frequently—The same principle should apply to data sets—So, OpenIDs for people, DOIs for data?

Side-benefits, challenges—Would also clear up problems around paper authorship—Would enable other kinds of credit (training, curation, etc.)—May have to be self-policing — researchers ‘own’ their

credit portfolio (though an enforcement body would also be useful)

—Problem of ‘micro data sets’ and legacy data

Credit where credit’s due