The Seven Deadly Sins of BioinformaticsProfessor Carole Goble

carole.goble@manchester.ac.uk

The University of Manchester, UK

The myGrid project http://www.mygrid.org.uk

myExperiment http://myexperiment.org

OMII-UK http://www.omii.ac.uk

We’ve been developing software and ontologies and data and stuff with and

for Bioinformatics and Bioinformaticians for a long time.

“though it took two years before we understood each other!”

Andy Brass,

Professor of Bioinformatics

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myGrid Taverna Workflow Workbench

http://www.mygrid.org.ukhttp://taverna.sourceforge.net

5http://myexperiment.org

The Seven Deadly Sins of Bioinformatics

BOSC 2007

Slideshare.net 8091views (02-June-2008)http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics/

From an original idea by Stevens and Lord

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Methodology• Email a handful of bioinformaticans.• Stand well back.• Collect.• Edit.

• Therapy on the cheap. • We all felt better.

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I am grateful to…• Phil Lord (University of Newcastle)• Anil Wipat (University of Newcastle)• Matthew Pocock (University of Newcastle)• Robert Stevens (University of Manchester)• Paul Fisher (University of Manchester)• Duncan Hull (Manchester Centre for Systems Biology)• Norman Paton (University of Manchester)• Marco Roos (University of Amsterdam)• Rodrigo Lopez (EBI)• Tom Oinn (EBI)• Andy Law (Roslin Institute)• Graham Cameron (EBI)

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The Traditional Sins….

1. Lust

2. Gluttony

3. Greed

4. Sloth

5. Wrath

6. Envy

7. Pride

http://en.wikipedia.org/wiki/Seven_deadly_sins

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The Sins of Bioinformatics

1. Parochialism and Insularity

2. Exceptionalism

3. Autonomy or death!

4. Vanity: Pride and Narcissism

5. Monolith Meglomania

6. Scientific method Sloth

7. Instant Gratification

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Parochialism• “being provincial, being narrow in scope, or

considering only small sections of an issue.” http://en.wikipedia.org/wiki/Parochialism

Insularity• “a person, group of people, or a community

that is only concerned with their limited way of life and not at all interested in new ideas or other cultures.” http://en.wikipedia.org/wiki/Insularity

Sin 1: Parochialism and Insularity

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Reinvention of the Wheel• Rediscovering the same old problems,

techniques, methods.• Creating…Yet another …

– identity scheme. – representation mechanism for data. – ontology. – data warehouse.– integration framework. – query or ontology or workflow language.

• Result? Misery. But more work for the boys….

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Q92983O00275O00276O00277O00278O00279O00280O14865O14866P78507

• WSL-1 protein• Apoptosis-mediating receptor DR3 • Apoptosis-mediating receptor

TRAMP • Death domain receptor 3 • WSL protein • Apoptosis-inducing receptor AIR • Apo-3 • Lymphocyte-associated receptor of

death • LARD• GENE: Name=TNFRSF25

Q93038 = Tumor necrosis factor receptor superfamily member 25 precursor

P78515Q93036 Q93037 Q99722 Q99830 Q99831 Q9BY86 Q9UME0 Q9UME1 Q9UME5

Annotation history:

http://www.expasy.org/uniprot/Q93038

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Andy Law's Third Law• “The number of unique identifiers assigned to

an individual is never less than the number of Institutions involved in the study”... and is frequently many, many more.

http://bioinformatics.roslin.ac.uk/lawslaws.html

Bioinformatics is about mapping one schema to another, one format to another, one id scheme to

another.

Comparative Genomics? Comparative Bioinformatics!

What a waste of time. But a handy distraction from doing some Real

Science™.

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Andy Law’s First (Format) Law “The first step in developing a new genetic

analysis algorithm is to decide how to make the input data file format different from all pre-existing analysis data file formats.”

http://bioinformatics.roslin.ac.uk/lawslaws.html

crimapfemale male

Keightly

Knott and Haley

0 1

1 0

1 2

When they use ‘3’ and ‘4’ we will know they are doing it deliberately.

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• Organism databases• 250+ metabolic pathway

databases.• Generic Model Organism

Database Toolkit.

FlyBase, WormBase, SGD, BeeBase and many other large and small community databases

Yet another database …

Yet another Integration Platform…• Warehouses, Views,

Mashups, Workflows …• Goble and Roberts, The

state of the nation in data integration in the Life Sciences. JBI.

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BioBabel• bioperl • biojava • biopython • bioruby • biophp• biosql • biouml• bioeclipse• biofoo• biobar• bio*

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Computer Science? No thanks!

Ontology and Knowledge Representation Languages

Database Schemas

Workflow systems

Integration platforms

Programming Languages

Tools

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Why don’t biologists modularise OWL*

ontologies properly?

Er, well, like how should we do it “properly” and where are the tools to help us?

We don’t know and we haven’t got any. But here are some

vague guidelines.

W3C Semantic Web for Life Sciences mailing list, 2005

*The W3C Web Ontology Language

A few months in the laboratory can save a few hours in the library.

Westheimer's Law

A few years at the computer can save a few hours on Google.

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Reuse Rocks. Collaboration through workflow and web services

VL-e Project• “instant collaboration”

with Martijn Schuemie (Rotterdam) through a web service that discloses their protein synonym data.

• Exchanging services and (sub)workflows with food scientists.

• Web services make that easier.

This isn’t the workflow – its just a picture of one!

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Of Mice and Cows• A Trypanosomiasis in Cattle workflow

(by Paul*) reused without change for Trichuris muris Infection (by Jo).

• Identified the biological pathways believed to be involved in the ability of mice to expel the parasite.

• Workflows are memes. Scientific commodities. To be exchanged and traded and vetted and mashed.

• Users add value.

*Fisher P et al A systematic strategy for large-scale analysis ofgenotype–phenotype correlations: identification of candidate genes involved in African trypanosomiasis, Nucleic Acids Research, 2007, 1–9

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Sin 2: Exceptionalism• Biologist exceptionalism

• Biological exceptionalism

• Biology exceptionalism

A root cause of Reinvention Syndrome

“Bioinformatics is special”

“Our domain specific outcomes requires-specific approaches and technologies”

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Biologist exceptionalism

• I know there is already a gene name for that gene, but, I don't like it and it doesn't fit in with my schema.

• It would be better if I wrote the script I need so I know what it does, how it does it and how to modify it later because I haven’t specified what it was supposed to do in the first place.

I’m different. We are all individuals.

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I am considerably more complex than you…

• “There are proteins, and there are records about proteins. Records come in different formats. If I make a statement using this url, is it about the record? or the protein?” Alan Ruttenberg

• “[Usually] we have one entry per gene. We have several entries for a single gene when description of variations are too complicated to describe in FT lines (of course, this criteria depends on the annotator). For viruses, it is much more messy, due to ribosomal frame-shifts. Formalise that!” Eric Jain UniProtDB

er…decomposition and untangling?

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Biology Exceptionalism• Drawing graphs of data

sets over time.• Stop it.• The real problem is

complexity not scale.• The number of data sets,

their diversity and how they overlap.

• How they change. • Their reliability.

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Biological exceptionalism“Biology is all exception.”

• Don’t complicate everyone’s life for the sake of a few esoteric cases.– Cameron’s 5th Commandment of Curation

• Exceptionalism paralysis.• Gather requirements expansively, prune

ruthlessly

• The EMBL/GenBank/DDBJ/Feature Table

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With added churn, indifference to users and

monopoly mentality.

Sin 3: Autonomy or death!

• Compounded by the Early Adopter tendency of the community

• “Hell is other people’s systems” as Jean Paul Sartre would have said if he had been a bioinformatician.

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Autonomy IS death!• I’ll Change my interface / format whenever I feel

like it, despite the fact I wanted lots of users and I have lots of users who depend on this. And I won’t bother to debug either or provide backwards compatibility. So there.– BioMART changed 4 times in the 2007.– NCBI changes as it fancies. – Ensembl relational schema.– Early BioJava.

• Churning for change sake

Professional

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No tool is an island…• Assume

– only we will use it, whatever it may be.– that it will be freestanding and unlinked to anything else.– that it will always work and will keep on working.– That everyone will understand it.

• “Well I know what I mean. And so does my friend. So I don’t need to specify it. Or document it properly. Or keep the metadata up to date.”

• Never mind the interface, just look at my implementation!

• Metadata, Models, Interfaces, Services matter.

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Workflow commodities• Workflow published with

its paper and its data set.

• So what happens when I want to run this workflow again?

• Is the service dead?• Is the dataset still there? • Was it designed to be

reproduced or reused in the first place?

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The myGrid Service DeCrypting Sweatshop notice how tired they look

Franck Tanoh Katy Wolstencroft

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Lincoln Stein said a while ago…“An interface is a contract between data

provider and data consumer”• Document interface; warn if it is unstable• Do not make changes lightly

– Even little fiddly changes break things– Provide plenty of advance warning

• When possible, maintain legacy interfaces until clients can port their scripts

Support as many interfaces as you can• HTML, Text only (better), HTTP, REST, SOAP• Easy Interfaces + Power User Interfaces

…and he could say it again today.

Stability is more important than Standards, Smartness, or the latest

Thing that everybody is talking about.

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Sin 4

Vanity

Pride

Narcissismconceit, egotism or simple selfishness.

Applied to a social group, denotes elitism or an indifference to the plight of others

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I know it all.• Claiming to know everything

about biology and everything about computers.

• Really irritating to biologists AND computer scientists.

• Even they don’t claim to know everything about biology or computer science.

• Computer scientists do know a lot of stuff. And they publish too.

• “Biologists are the experts on everything because we produce the data”

And what would you suggest, Mr. Smartie Pants?

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Think like me! Be like me!

• Designing good experiments is hard.• Workflows are computational experimental protocols.

Ergo….• Writing workflows is hard.• Writing good workflows is really hard.• Writing good reusable workflows is really really hard.

Misunderstanding and disrespecting users• Building interfaces that only you can use.• Not actually using your tools in the field.

I understand workflowsWorkflows are for biologists. My granny can do workflows...

A good User Experience outweighs smart features and cool computing.

Bummer.

Can I use it?

Is the user interface familiar?

Does it fit with my needs?

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Sin 5: Monolith Meglomania• delusions of grandeur. • obsession with grandiosity and extravagance.

• Data mining – “my data is mine, and your data is mine”

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• Integration – the more the merrier. No.– Every link is a potential dead link.– Every dependency finds its way on to your critical path.

• “Uber-tools” and “Uber-databases”– Ensembl, BioMART etc etc….cost

• Apps/libraries in bioinformatics workbenches– with loads of crap bundled in, none of it kept up to date,

none of it properly integrated.

• Put it all in a warehouse. – Lots of warehouses and lots of toolkits…GMOD,

BioWarehouse, BioMART blah blah….– 50% warehouses fail. (Standish Group)

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The trouble with warehouses• Warehouses work? Piffle. They never manage to maintain

synchrony with the source data. Mostly they fall down of their own weight!” Graham Cameron, EMBL-EBI

• "Our ability to capture and store data far outpaces our ability to process and exploit it. This growing challenge has produced a phenomenon we call the data tombs, or data stores that are effectively write-only; data is deposited to merely rest in peace, since in all likelihood it will never be accessed again. Data tombs also represent missed opportunities." Usamma Fayyad Yahoo! Research! Laboratories!

• We believe that attempts to solve the issues of scientific data management by building large, centralised, archival repositories are both dangerous and unworkable” Microsoft 2020 Science report.

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Distributed Annotation System http://www.biodas.org

Reference Server

AC003027AC005122

M10154

Annotation Server

Annotation Server

AC003027M10154

WI1029 AFM820 AFM1126 WI443

AC005122

Annotation Server

Now we call this a mash up

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Sin 6: Scientific Method Sloth• Its easier to think of a new name than use

someone else’s.• I want my own view over data and views are

difficult, so I’ll create my own database.

• Leads to Reinvention, Exceptionalism • Often the result of Instant Gratification

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Ennui• Garbage in, garbage out

– Running analysis over the wrong datasets– E.g. Identifying chicken proteins in mouse cells.

• Configuration traditionalism– Not changing the parameters of BLAST. Ever.

• Top list ennui– If there is a list only looking at the first one.– Look no further than the first Blast hit / first Google hit.

• Arbitrary cut-offs on rank-ordered result list– Absolute truth above, absolute falsehood below– E.g. differentially expressed genes in microarray

analyses.

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Quality Delusions• The bioinformatics does not

have to be sound, because we only trust wet-lab results anyway.

• Worrying about errors in experimental data but believing that derived data is always true.

• Believing Trembl is always right.

• Believing computational gene predictions are always correct.

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Irreproducible Black Box Science• Can you reproduce

bioinformatics analyses?– Not collecting the provenance of

the analysis.– Not testing during software

development.

• UniGene – What is happening during

UniGene clustering? – ‘Human’ descriptions (via NCBI),

are not exact. – The Human Transcriptome Map

project and other microarray analysts ended up reclustering UniGene [Marco Roos].

Stuff In

Stuff Out

“No experiment is reproducible.” Wyszowski's Law

“An experiment is reproducible until another laboratory tries to

repeat it.” Alexander Kohn

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Sin 7: Instant Gratification• Greed? Gluttony?• Always the immediate return.• Never investing for the future.• The quick and dirty fix.• Refusing to model or abstract.• Refusing to plan for recording

and exchanging.• Just getting the next quick fix.• The pressure to deliver now

and pay laterwww.CartoonStock.com .

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Hackery• Deliver now, pay later

– Producing crap, non-reusable, software because only the biological results matter for publication X.

– Collect! Analyse! Er…now what?– NO up to date or useful DOCUMENTATION!

• Spaghetti-ism– Over-indulgence in PERL– Over-indulgence in Ascii Art flat files.– Modelling a system by hacking up XSD fragments on a

whiteboard.– Writing perl scripts that resemble my high-school

BASIC of the 80s.

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Law's Second Law• “Error messages should never be provided”

corollary... “If error messages are provided, they should be utterly cryptic so as to convey as little information as possible to the end user”

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Blind faith in XML (or any mechanism)• “It’s in XML, thus all

data integration problems are solved.”

• “The good thing about XML is that it is human readable”. !!*£!*!

XML

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Blind Faith in Foo.• There's a new thing to

use.• we don't understand it

yet. • so it sucks up all the stuff

we already know we don't understand.

• Lack of appreciation about exactly what the new technology addresses in itself before trying to make it work for us.

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Pioneering development methods• Development by anecdote

– I heard in the pub that the way to go was Foo.– Though I have no idea what Foo is or why it is the

way to go.

• Design by hacking– It would be better if I wrote the script I need so I

know what it does, how it does it and how to modify it later because I haven’t specified what it was supposed to do in the first place.

– Hmmm…..We call that Extreme Programming or Emergent Semantics or Web 2.0 in CS .

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At the other end of the spectrum…• Often found in industry• Over-engineered solutions• Delivered too late• That solve a problem the users didn’t know

they had• Or are hard to use• Or wire their favourite tool in

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Sin Summary

Maybe only one “original sin” in bioinformatics.

Parochialism and Insularity

Autonomy or death!

Vanity: Pride and Narcissism

Monolith Meglomania

Scientific method Sloth

Instant Gratification

Reinvention

Churn

Exceptionalism

Why do these sins exist? Can we become less sinful?

Are bioinformaticians particularly naughty?

No naughtier than Computer Scientists.

And its all very hard.

Though they are naughty…

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Forgive us our sins….1. Reward Culture The Selfish Scientist and Self-promotion, Research vs

Production, Reuse is Hard

2. Mechanism evangelismMore than one mechanism

New and my shiny Gadget syndrome

3. Gang warfareIf you are not with us you are against us

Interdisciplinary mistrust and conflict

4. Blazing the trailInvention, not Reinvention

Delivery bulge

5. Fear and TrustLuddism

Trust (or lack of it)

6. Its HardHybrid Exhaustion

The Selfish Scientist and Self-promotionResearch vs ProductionReuse is Hard

More than one mechanismNew and my shiny Gadget syndrome

If you are not with us you are against usInterdisciplinary mistrust and conflict

Invention, not ReinventionDelivery bulge

LuddismTrust (or lack of it)

Hybrid Exhaustion

Long Jump

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The Selfish, Self-interested Scientist• Reputation. Results right now. Win. More funds!• Fear of dependency. Fear of being left behind.• Understand the incentives and barriers to

adoption.

“A biologist would rather share their toothbrush than their (gene) names”

Mike Ashburner, Professor Genetics, University of Cambridge, UK

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Funding and Social structures Are against shareable reusable software/ontology/database/thing

1. Self-promotion– I can publish every new monolithic thing and I can’t publish if I

reuse someone else’s thing.

2. Novelty vs Standards– Standards are boring “blue collar” science (Quackenbush)

3. Research vs Production Confusion– How do you get funding for production software other than

claiming to be researching stuff? – How do you get a publication out of a bit of research software

without claiming a potential user-base?– I don’t want to be a long-term service provider!

Short Jump

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Reuse is Hard• Writing reusable workflows is hard.

– Local services. Permissions. Licences.

• Writing reusable services is hard.– Predicting the unknown required by the unknown.– Its an effort – What is in it for me? Helping out my competitors! Forcing me

to support others?

• Finding and using “reusable” workflows and services and tools is hard– Where do you go?? What does it DO??

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My New Gadget Syndrome• This is inherited from

Computer Science I fear.

• Sorry.

• A few evangelistic voices, very loud, vested interest, for their application, win.

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More than One Mechanism Muddle

Global Identity naming mechanism for data objects in the Life Sciences. LSIDs and URIs and PURLs. WS-Naming and all its friends.

urn:lsid:uniprot.org:{db}:{id}

http://purl.uniprot.org/{db}/{id}

W3C Semantic Web Health Care and Life Sciences Interest Group public-semweb-lifesci@w3.org

Endurants, Perdurants,Being, Substance, Event

Discipline gang warfareThe Montagues and The Capulets, Comparative and Functional Genomics, 2004

The Ontology War

Short Jump

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Invention, not Reinvention • BioMOBY pre-dates (Semantic) Web service revolution

– Though not 20 years of SOA

• OBO and OBO-Edit pre-dates OWL and Protégé-OWL– Though not 20 years of Knowledge Representation.

• Taverna pre-dates a reliable Open Source BPEL engine– Though not 20 years of lambda and pi calculus and petri nets.

• There ARE features that Bioinformatics needs that other solutions don’t cater for.

• And solutions needed URGENTLY

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Delivery Bulge

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Luddism• Refusing to have

biology go beyond a cottage industry.

• Being scared to do it properly.

• Railing against big science

• The cult of amateurism.

[Stevens]

TrustI don’t trust your codeI don’t trust your data

I don’t trust you will still be around in one yearI don’t trust your workflow

I don’t trust you will use my data / workflow / code properly

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Hybrid exhaustion and pressure.• Biology + Computing + Bioinformatics• It. Is Hard.

What can we do to be less sinful?

Make sharing easierMake sharing rewarded

Think ComponentsOnly standardise the minimum

Embed bioinformaticians and computer scientists

Presume naughtiness

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Think Safe Sharing of Stuff• Understanding outside my expertise. e.g.

sources of error. • A comprehensive catalogue of web services• A Facebook for workflow builders.• Learn from others. Even Computer Science.

And other Sciences.• Try and create a culture of raising quality.

Somehow.

76http://myexperiment.org

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Think Components• Stop building monolithic solutions• Component-ise Bioinformatics

– Loosely coupled systems– Stable APIs, standardised metadata. – Design to combine.– Sort out the naming/id problem– If you can’t agree, agree on the bridge.– Virtualise

• Raise the level of abstraction– Less Perl, more workflows – Enable users to extract the data they need without hassling

you.

• “Standardise messages not structures” Graham Cameron

Web Services

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Think how it Really Is™…• …incremental change • …others use our stuff• …others add value to our stuff• …scientific naughtiness

• deal with it, or expose it.• end to black boxes• workflows as a route to transparency• open notebook science

• scary stuff.

Short Jump

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Think User and Developer together

• Embed Bioinformaticians with Computer Scientists and Biologists

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Think Web 2.0 Design Patterns

• De Roure, D. and Goble, C. (2008) Six Principles of Software Design to Empower Scientists. IEEE Software (to appear). http://eprints.ecs.soton.ac.uk/15032/

1. The Long Tail

2. Data is the Next Intel Inside

3. Users Add Value

4. Network Effects by Default

5. Some Rights Reserved

6. The Perpetual Beta

7. Cooperate, Don't Control

8. Software Above the Level of a Single Device

“Other men's sins are before our eyes; our own are behind our backs”

Seneca

Roman philosopher, mid-1st century AD