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Centro de Investigación ProS
Applying Capability Modelling in the
Genomics Diagnosis Domain: Lessons
Learned
Francisco Val...
Agenda
1. Motivation
2. Applying CDD in a genomics SME
3. Lessons Learned
4. Conclusions
Motivation
 Context
3
Innovative services for Digital Enterprises with ORCACapability as a Service for
Digital Enterprise...
Motivation
 Case study: Geneticists from a SME (IMEGEN)
provide their disease diagnosis services using
genetic informatio...
Motivation
 Genetics is a continuously evolving context:
✘There is no an standard software solution because easily
will b...
Motivation
Impact in Geneticists’ work:
 Addressing tedious programming tasks to customize tools
 Spending more time lea...
Motivation
9
Impact in business
 Could you provide a genetic test for the novel disease X?
 Predict expected delivery ti...
Motivation
 This works deals with this issue from two
perspectives
 Business: using CDD to formalize the genetic
diagnos...
Applying CDD
 According to CaaS Project: the ability and capacity
that enables an enterprise to achieve a business goal
i...
Applying CDD
 Why CDD?
• Enterprise context clearly affects the service delivery in
this use case
• Know-how reuse is fea...
Applying CDD
 We interviewed with 3 geneticists from the SME to:
• Define a domain model as a conceptual schema
• Underst...
Applying CDD
Capability template (from CaaS)
 Goal: Desired state of affairs that needs to be attained.
 Goal KPI: KPI) ...
Applying CDD
 Provide a disease genomic Diagnosis (Main capability)
 Goal: Provide a accurate diagnosis regarding a geno...
Applying CDD
16
Overall Process
Applying CDD
17
IMEGEN Process
Applying CDD
 This capability is easy to manage as four sub-capabilities:
18
1. Provide a Genomic
Diagnosis
1.1 Provide i...
Applying CDD
1. Provide integrated information from public data
sources
• Context: New datasets to be included and updated...
Applying CDD
3. Management of new genomic data
• Context: New discoveries about genomic mechanisms and
disease
• KPI: dise...
Applying CDD
 Bioinformatics Workflow Management Systems:
describe workflows made up of software
components that manipula...
Applying CDD
 Example of a BWMS (Galaxy)
22
Applying CDD
 Support for each Capability
23
Capability Taverna Galaxy eBioFlow
Integrated information Partial Partial Pa...
Lessons Learned
 Regarding BWMS:
• Taverna is the most complete but does not take into
account domain knowledge
• Galaxy,...
Lessons Learned
 Geneticists state that capabilities specification are a
nice and organized documentation of their proces...
Lessons Learned
 CDD proposed conceptual models are useful for data
information retrieval and domain modelling
 Re-using...
Conclusions
 We have present the potential benefits of applying
CDD in a novel domain
 Problem specification (Capability...
Questions/Comments
28
{fvalverde, mvillanueva}@pros.upv.es
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Applying Capability Modelling in the Genomics Diagnosis Domain: Lessons Learned

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Because of the evolution of sequencing technologies, tailored software is a must in the genetic diagnosis domain. Bioinformatics Workflow Management Systems (BWMS) are a popular software for geneticists to describe workflows for analysing genomic data. Although these systems improve development productivity, they are far from being widely accepted by this community. The lack of rigorous conceptual modelling-practices explains the complexity to adapt this genetic analysis software to context changes. In order to face this adaptation issue, we propose using the capability notion as a modelling primi-tive for providing a sound conceptual background. This paper analyses, from a capability-driven perspective, how daily practices in a bioinformatics SME could be represented as capabilities. From this real scenario, we state current capabilities and explain how they can be supported using current BWMS. As a lessons learned, we discuss how the introductions of capability-driven de-velopment could improve their daily work.

Authors: Francisco Valverde & Maria José Villanueva

Published in: Software
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Applying Capability Modelling in the Genomics Diagnosis Domain: Lessons Learned

  1. 1. 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
  2. 2. Agenda 1. Motivation 2. Applying CDD in a genomics SME 3. Lessons Learned 4. Conclusions
  3. 3. Motivation  Context 3 Innovative services for Digital Enterprises with ORCACapability as a Service for Digital Enterprises Apply CaaS results into innovative case studies from a Spanish region
  4. 4. 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) 4
  5. 5. 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 6
  6. 6. 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. 8
  7. 7. Motivation 9 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!!!
  8. 8. 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 10
  9. 9. Applying CDD  According to CaaS Project: the ability and capacity that enables an enterprise to achieve a business goal in a certain operational context 11 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)
  10. 10. 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 12 In our view, CDD addresses these three main concerns using a sound approach
  11. 11. 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 13
  12. 12. 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. 14
  13. 13. 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 15
  14. 14. Applying CDD 16 Overall Process
  15. 15. Applying CDD 17 IMEGEN Process
  16. 16. Applying CDD  This capability is easy to manage as four sub-capabilities: 18 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)
  17. 17. 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 19
  18. 18. 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 20
  19. 19. 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 21
  20. 20. Applying CDD  Example of a BWMS (Galaxy) 22
  21. 21. Applying CDD  Support for each Capability 23 Capability 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
  22. 22. 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 24 Galaxy was selected because of the advanced functionality provided
  23. 23. Lessons Learned  Geneticists state that capabilities specification are a nice and organized documentation of their process.  CDD overcomes the worfklow-oriented vision in bioinformatics 25
  24. 24. 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 26
  25. 25. 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 27
  26. 26. Questions/Comments 28 {fvalverde, mvillanueva}@pros.upv.es

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