Centro de Investigación ProS

Applying Capability Modelling in the Genomics Diagnosis Domain: Lessons Learned

Francisco Valverde and Maria José Villanueva

2nd International Workshop on Capability-oriented Business Informatics (CoBI 2015). 16th of July, 2015

Agenda

1. Motivation

2. Applying CDD in a genomics SME

3. Lessons Learned

4. Conclusions

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Motivation

Context

Innovative services for Digital Enterprises with ORCA Capability as a Service for Digital Enterprises

Apply CaaS results into innovative case studies from a Spanish region

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Motivation

Case study: Geneticists from a SME (IMEGEN) provide their disease diagnosis services using genetic information

They provide a portfolio of genetic tests to be carried out (around 1.000 different tests)

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Motivation

Genetics is a continuously evolving context:✘There is no an standard software solution because easily

will become outdated✘Lack of Software Engineering / Conceptual modelling

practices for supporting evolution✘Need of novel and powerful infrastructure

… but the underlying process remains the same

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Motivation

Impact in Geneticists’ work:

Addressing tedious programming tasks to customize tools

Spending more time learning computer science issues

Making mistakes due to lack of knowledge and manual

procedures.

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Motivation

Impact in business

Could you provide a genetic test for the novel disease X?

Predict expected delivery time for a test

Compliance with new laboratory ISO regulations

Reducing costs by infrastructure outsourcing (Cloud

technologies or external provider)

They don’t really know if they can provide their capabilities in

the near future!!!

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Motivation

This works deals with this issue from two

perspectives

Business: using CDD to formalize the genetic

diagnosis capability they must provide

Technical: analyzing Bioinformatics Workflow

Management Systems (BWMS) to support the

capability deployment

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Applying CDD

According to CaaS Project: the ability and capacity that enables an enterprise to achieve a business goal in a certain operational context

The goal to accomplish

The ability to engineer a bridge

The capacity such as money or tools to build a bridge

The context in which the bridge must be build

(location)

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Applying CDD

Why CDD?• Enterprise context clearly affects the service delivery in

this use case• Know-how reuse is feasible in the domain as pipeline (data

processing workflows)• Lack of widely accepted conceptual models / standards to

express genetic data

In our view, CDD addresses these three main concerns using a sound approach

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Applying CDD

We interviewed with 3 geneticists from the SME to:• Define a domain model as a conceptual schema• Understand goals, KPIs and current bioinformatics context• Formalize their current process model• Understand the technological tools involved in the process• Detect current bottlenecks

We specified its business as capabilities following a

template

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Applying CDD

Capability template (from CaaS)

Goal: Desired state of affairs that needs to be attained.

Goal KPI: KPI) or monitoring the achievement of a goal.

Context: Information characterizing the situation in which a

business capability should be provided.

Capacity: Availability of resources for delivering the capability

Ability: Level of available competence of a enterprise to

accomplish a goal.

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Applying CDD

Provide a disease genomic Diagnosis (Main capability)

Goal: Provide a accurate diagnosis regarding a genomic

disease

Capacity: NGS machine and technological infrastructure

(Server, Disk Array etc.)

Ability: geneticists with knowledge about data sources

with trustful information and the genomic diagnosis

process

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Applying CDD

Overall Process

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Applying CDD

IMEGEN Process

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Applying CDD

This capability is easy to manage as four sub-capabilities:

1. Provide a Genomic Diagnosis

1.1 Provide integrated information from public data source

1.2 Support novel bioinfomatics services

1.3 Management of new genomic data

1.4 End-user (friendly reports physician)

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Applying CDD

1. Provide integrated information from public data sources • Context: New datasets to be included and updated

versions• KPI: number of supported datasets

2. Support novel bioinformatics services• Context: New algorithms, new sequencing technologies,

data processing utilities, novel IS architectures• KPI: number of supported services, response-time

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Applying CDD

3. Management of new genomic data• Context: New discoveries about genomic mechanisms and

disease• KPI: disease knowledge

4. End-user (friendly reports physician) • Context: New laws and standards (ISO) regarding clinic

analyses• KPI: Law/Certification compliance and trust

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Applying CDD

Bioinformatics Workflow Management Systems:

describe workflows made up of software

components that manipulate genetic data.

End-user oriented: they provide some guidance for

creating experiments, such as visual notations or

wizards

Three analyzed: Taverna, Galaxy, e-bioflow

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Applying CDD

Example of a BWMS (Galaxy)

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Applying CDD

Support for each CapabilityCapability Taverna Galaxy eBioFlow

Integrated information Partial Partial Partial

Support novel bioinfomatics services Yes Partial No

Data management Partial No Partial

End-user friendly reports Partial Partial No

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Lessons Learned

Regarding BWMS:• Taverna is the most complete but does not take into

account domain knowledge• Galaxy, simpler workflow notation and provides a lot of

functionality out-of-the box• eBioflow, provides a good workflow language in terms of

expressivity and a user-friendly interface but lacks of advanced functionality

Galaxy was selected because of the advanced functionality provided

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Lessons Learned

Geneticists state that capabilities specification are a

nice and organized documentation of their process.

CDD overcomes the worfklow-oriented vision in

bioinformatics

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Lessons Learned

CDD proposed conceptual models are useful for data

information retrieval and domain modelling

Re-using of know-how to address novel genetic

diseases

CDD + BWMS offers a clear improvement over

current practices and future evolution

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Conclusions

We have present the potential benefits of applying

CDD in a novel domain

Problem specification (Capability) is decoupled from

implementation (BWMS)

As further work we will evaluate in practice the

analyzed capabilities using Galaxy

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Questions/Comments

{fvalverde, mvillanueva}@pros.upv.es