Web Science 2.0 - in silico science

Web Science 2.0

Conducting in silico research in the Webfrom hypothesis to publication

Mark Wilkinson

Isaac Peral Senior Researcher in Biological InformaticsCentro de Biotecnología y Genómica de Plantas, UPM, Madrid, Spain

Adjunct Professor of Medical Genetics, University of British ColumbiaVancouver, BC, Canada.

Context

Multiple recent surveys of high-throughput biology

reveal that upwards of 50% of published studies

are not reproducible

- Baggerly, 2009

- Ioannidis, 2009

Context

“the most common errors are simple,

the most simple errors are common”

- Baggerly, 2009

Context

These errors pass peer review

The researcher is unaware of the error

The process that led to the error is not recorded

Therefore it cannot be detected during peer-review

Context

Discovery of such errors have resulted in retractions

and even shut-down clinical trials

Context

In March, 2012, the US Institute of Medicine said

“Enough is enough!”

ContextInstitute of Medicine Recommendations

For Conduct of High-Throughput Research:

Evolution of Translational Omics Lessons Learned and the Path Forward. The Institute of Medicine of the National Academies, Report Brief, March 2012.

1. Rigorously-described, -annotated, and -followed data management procedures

2. “Lock down” the computational analysis pipeline once it has been selected

3. Publish the workflow in a formal manner, together with the full starting and result datasets

Achieving these recommendations

requires integration of existing technologies

and invention of new ones

“While it took 2,300 years after the first report of angina for the condition to be commonly taught in medical curricula, modern discoveries are being disseminated at an increasingly rapid pace.”

The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey (Editor), 2009

Slide adapted with permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USAJune 22, 2012.

Context

http://www.amazon.com/s/ref=ntt_athr_dp_sr_1?_encoding=UTF8&sort=relevancerank&search-alias=books&ie=UTF8&field-author=Tony%20Hey



The Healthcare Singularity and the Age of Semantic Medicine, Michael Gillam, et al, The Fourth Paradigm: Data-Intensive Scientific Discovery Tony Hey (Editor), 2009Slide Borrowed with Permission from Joanne Luciano, Presentation at Health Web Science Workshop 2012, Evanston IL, USAJune 22, 2012.

“The Singularity”

The X-intercept is where, the moment a discovery is made, it is immediately put into practice

(not only medical practice, but any research endeavour...)




The technology required to achieve this does not yet exist

Scientific research would have to be conducted within a medium that

immediately interpreted and disseminated the results...

You Are

Here

...in a form that immediately (actively!) affected the research of others...

You Are

Here

...without requiring them to be aware of these new discoveries.

You Are

Here

I’d like to show you how close we now are to this vision

and how we got there

Web Science 2.0

We wanted to duplicatea real, peer-reviewed, bioinformatics analysis

simply by building a model in the Webdescribing what the answer

(if one existed)

would look like

...the machine had to make every other decision

on it’s own

Brief Digression

“in” the Web??

How we use The Web today

By clicking here you cause this incredibly powerful computational tool called The Web to retrieve a chunk of text and images that

can only be understood by a human...

The Web is not a pigeon!

To achieve this vision

We must learn how to do research IN the Web

Not OVER the Web

Resume Speed

We wanted to duplicatea real, peer-reviewed, bioinformatics analysis

simply by building a model in the Webdescribing what the answer

(if one existed)

would look like

...the machine had to make every other decision

on it’s own

This is the study we chose:

Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).

Original Study Simplified

Using what is known about interactions in fly & yeast

predict new interactions with your human protein of interest

Given a protein P in Species X

Find proteins similar to P in Species Y

Retrieve interactors in Species Y

Sequence-compare Y-interactors with Species X genome

(1) Keep only those with homologue in X

Find proteins similar to P in Species Z

Retrieve interactors in Species Z

Sequence-compare Z-interactors with (1)

Putative interactors in Species X

“Pseudo-code” Abstracted Workflow

Modeling the answer...

OWL

Web Ontology Language (OWL) is the language approved by the W3C

for representing knowledge in the Web


Note that every word in this diagram is, in reality, a URL (because it is OWL)

The model of the answer is published in The Weband borrows ideas from other models published in The Web

ProbableInteractor is homologous to ( Potential Interactor from ModelOrganism1…)

and

Potential Interactor from ModelOrganism2…)

Probable Interactor is defined in OWL as a subclass of Potential Interactor that requires homologous pairs of interacting proteins to exist in both

comparator model organisms.

(Effectively, an intersection)


Publish our OWL model of a Probable Interactor

in the Web

In a local data-file

provide the protein we are interested in

and the two species we wish to use in our comparison

taxon:9606 a i:OrganismOfInterest . # humanuniprot:Q9UK53 a i:ProteinOfInterest . # ING1taxon:4932 a i:ModelOrganism1 . # yeasttaxon:7227 a i:ModelOrganism2 . # fly

Running the Web Science Experiment

The tricky bit is...

In the abstract, the search for homology is

“generic” – ANY Protein, ANY model

system

But when the machine does the experiment, it

must use specific of resources because the

answer requires information from two

declared species

taxon:4932 a i:ModelOrganism1 . # yeasttaxon:7227 a i:ModelOrganism2 . # fly

PREFIX i: <http://sadiframework.org/ontologies/InteractingProteins.owl#>

SELECT ?proteinFROM <file:/local/workflow.input.n3>WHERE {

?protein a i:ProbableInteractor .}

This is the question we ask:(the query language here is SPARQL)

The reference (URL) to our OWL model of the answer

Our system then derives (and executes) the following workflow automatically

These are differentWeb services!

...selected at run-time based on the same model

There are four very cool things about what you just saw...


The system was able to create a workflow based on an OWL model (ontology)


The system was able to create a COMPUTATIONAL workflow

based on a BIOLOGICAL model


The workflow it created (i.e. the services chosen)

differed depending on context


The choice of tool-selection was guidedby the encoded knowledge of domain-experts

worldwide

We got the answer

“simply” by designing a model of the answer!

How did we do that?

A “Smart” Biomedical Resource Representation System

A Web application that answers SPARQL-DL queries

Query-answering Enhanced by SADI

Demo #1

Imagine a “virtual database”

all of the data from all databases

+result of

every conceivable analysis

How can we query that database?

What is the phenotype of every allele of the Antirrhinum majus DEFICIENS gene

SELECT ?allele ?image ?desc

WHERE { locus:DEF genetics:hasVariant ?allele . ?allele info:visualizedByImage ?image .

?image info:hasDescription ?desc }

What is the phenotype of every allele of the Antirrhinum majus DEFICIENS gene

SELECT ?allele ?image ?desc

WHERE { locus:DEF genetics:hasVariant ?allele . ?allele info:visualizedByImage ?image .

?image info:hasDescription ?desc }

Note that there is no “FROM” clause!We don’t tell it where it should get the information, The machine has to figure that out by itself...

Enter that query into SHARE

Click “Submit”...

...and in a few seconds you get your answer.

The query results are live hyperlinksto the respective Database or images

Neither SADI nor SHARE

know anything about

plant biology or genetics

What pathways does UniProt protein P47989 belong to?

PREFIX pred: <http://sadiframework.org/ontologies/predicates.owl#>PREFIX ont: <http://ontology.dumontierlab.com/>PREFIX uniprot: <http://lsrn.org/UniProt:>SELECT ?gene ?pathway WHERE {

uniprot:P47989 pred:isEncodedBy ?gene . ?gene ont:isParticipantIn ?pathway .

}




}




}

Note again that there is no “From” clause…

I have not told SHARE where to look for the answer, I am simply asking my question


Two different providers of gene information (KEGG & NCBI); were found & accessed

Two different providers of pathway information (KEGG and GO); were found & accessed

The results are all links to the original data


know anything about

proteins or biochemical pathways

Recapwhat we just saw

We posed, and answered ~complex multi-database queries

WITHOUT A DATA WAREHOUSE

An example from the Clinical domain

Demo #2

Show me the latest Blood Urea Nitrogen and Creatinine levelsof patients who appear to be rejecting their transplants

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {

?patient rdf:type patient:LikelyRejecter .?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat .

}

Show me the latest Blood Urea Nitrogen (BUN) and Creatinine levels of patients who appear to be

rejecting their transplants

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX patient: <http://sadiframework.org/ontologies/patients.owl#> PREFIX l: <http://sadiframework.org/ontologies/predicates.owl#> SELECT ?patient ?bun ?creatFROM <http://sadiframework.org/ontologies/patients.rdf>WHERE {

?patient rdf:type patient:LikelyRejecter .?patient l:latestBUN ?bun . ?patient l:latestCreatinine ?creat .

}

Likely Rejecter:

A patient who has creatinine levelsthat are increasing over time

- - Mark D Wilkinson’s definition

Likely Rejecter:

…but there is no “likely rejecter” column or table in our database…

only blood chemistry measurementsat various time-points

Likely Rejecter:

So the data required to answer this questionDOESN’T EXIST!

?


Now…

Two “magical” events occur…

The machine decides

by itself

that it needs to do a Linear Regression analysis

on the blood creatinine measurementsin order to answer your question

The machine decides

by itself

how and where that analysiscan be done

and does it automatically!

http://www.impactlab.net/2009/03/22/improve-your-brain-power/

The SHARE system utilizes SADI to discover analytical services on the Web that do linear regression analysis

and sends the data to be analysed

VOILA!


know anything about

blood chemistry, or mathematics

So how does the machine know what to do??

Ontologies

Ontologies explicitly define the kinds of things that (can) exist…

…and what those things are “like”

i.e. what properties they have (color, weight, shape, texture, temperature, “state”)

and what relationships they have to one another (inside-of, adjacent-to, part-of, binds-to, controls, inhibits,

degrades, etc.)

So we create ………….ontologies about biology

and health

We* publish them on the Web

* We… or anybody! Anybody can publish an ontology!

My definition of a Likely Rejecter is encoded in a machine-readable document written in the OWL Ontology language

Basically:

“the regression line over creatinine measurements should have an increasing slope”

Our ontology refers to other ontologies (possibly published by other people)to learn about what the properties of “regression models” are

e.g. that regression models have slopes and intercepts and that slopes and intercepts have decimal values

SHARE examines the query

Looks on the Web for ontologies that describe the problem it is trying to solve,

and “reads” them

then uses that “knowledge” to figure out which data-sources and analytical tools it

needs to answer the query

The way SHARE “interprets” data varies depending on the context of the query

(i.e. which ontologies it reads – Mine? Yours?)

and on what part of the query it is trying to answer at any given moment

(which ontological concept is relevant to that clause)

Data exhibits “late binding”

Late binding:

“purpose and meaning”of the data is

not determined untilthe moment it is required

a.k.a The “semantics” of the data

Benefitof late binding

Data is amenable toconstant re-interpretation

Example?

Blood Creatinine measurements

were not dictated to be (only)

Blood Creatinine measurements

Example?

The data had the ‘qualities/properties’ that

allowed one machine to interpret

that they were Blood Creatinine measurements

(e.g. to determine which patients were rejecting)

Example?

But the data also had the ‘qualities/properties’ that

allowed another machine to interpret them as

Simple X/Y coordinate data

(e.g. the Linear Regression calculation tool)

Benefitof late binding

Data is amenable toconstant re-interpretation

http://www.flickr.com/people/faernworks/

And that brings us to...

Gordon, P.M.K., Soliman, M.A., Bose, P., Trinh, Q., Sensen, C.W., Riabowol, K.: Interspecies data mining to predict novel ING-protein interactions in human. BMC genomics. 9, 426 (2008).

We built a model of the proposed answer

Our system converted the model into the experiment

The analytical tools chosen for that experiment changed depending on

context

even though the biological model driving their selection was the same

i.e.

The published model is re-usable

i.e.

The published model is re-usable

In different contexts... By different researchers

and because the model IS the experiment

the published EXPERIMENT is re-usable!!

simply point the same query at your own dataset...

The publication is an executable document!

Every component of the model

Every component of the input data

Every component of the output data

is a URL

Therefore the question, the experiment, and the answer, are immediately published IN the Web

Every component of the model

Every component of the input data

Every component of the output data

is a URL

The answer, and the knowledge derived from it, is immediately available to search engines

and moreover, can affect the outcome of other Web Science experiments

You Are NowHere!!!

Final thoughts

An experiment... based on a hypothesis

An experiment... based on a hypothesis

now modeled in OWL

Does this OWL Class represent the Hypothesis?

I think it does!

We modeled the answer......but the answer was hypothetical

Change the way we think of “hypotheses”

In Web Science 2.0

Model what the world would “look like”if your hypothesis were true

Then ask “is there any data that fits that model?”

Like the blind men examining an elephant

Seemingly different aspects of researchwhen viewed from the perspective of Web Science

become the same “thing”

The Model

Our vision of Web Science 2.0

Hypothesis

Ontology

Query

Workflow

Result

These can be automatically derived through provenance information during workflow execution

Materials & Methods

Please join us!

SADI and SHARE are Open-Source projects

http://sadiframework.org

http://sadiframework.org/

My New Home!

Luke McCarthy – Lead Dev.Everything...

Benjamin VanderValk SHARE & SADI & Experimental modeling & myHeath Button

Soroush Samadian Cardiovascular data modeling and queries

University of British Columbia

Edward Kawas SADI Service auto-generator

Ian WoodExperimental modeling project

U of New Brunswick

Dr. Chris BakerAlexandre Riazanov

Carleton University

Dr. Michel DumontierMarc-Alexandre NolinLeonid ChepelevSteve EtlingerNichaella KiethJose Cruz

C-BRASS Collaborators at other sites

Microsoft Research

Technology

Web Science 2.0 - in silico science