ライフサイエンス分野
Persistent Identifiers for Rice Genomics:
Harvests of PRAGMA and RDA
Jason Haga
Cyber-physical Cloud Research Group, AIST, Japan

Quan (Gabriel) Zhou, Inna Kouper, and Beth Plale
Indiana University, USA

Venice Juanillas and Ramil Mauleon
International Rice Research Institute, Philippines

1Motivation} Experiment with recent Recommendations emerging

from Research Data Alliance (RDA) around persistentidentifiers (PIDs)} Apply to real use case, rice genomics analysis} Design solution in modular way so that RDA tools can beused by various groups} Use this experiment as input to RDA working group onminimal metadata for PIDs
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THE RESEARCHDATA ALLIANCE
www.rd-alliance.org

17 FLAGSHIPOUTPUTSof which 4 ICT
TechnicalSpeciﬁcaFons

75 ADOPTION
CASES
across mulFple
disciplines,organizaFons, &countries

84 GROUPS WORKING ON GLOBALDATA INTEROPERABILITY
CHALLENGES
of which 35 Working Groups & 49
Interest Groups

5,121 INDIVIDUAL MEMEBERSFROM 121 COUNTRIES
66% Academia & Research
15% Public AdministraFon
11% Enterprise & Industry
43 ORGANIZATIONAL MEMBERS &8 AFFILIATE MEMBERS

A global, member-based organiza2on focusedon reducing barriers to data sharing andexchange and accelera2ng data driven
innova2on.
VisionResearchers and innovators openly share data
across technologies, disciplines, and countries
to address the grand challenges of society.
MissionRDA builds the social and technical bridges
that enable open sharing of data.
www.rd-alliance.org@resdatall

PRAGMA: A Community of PracticeEnabling the Long Tail of Team Science••••

Began in 2002, NSF funded – AIST is founding partner
Framework for collaboration – people drive activities
Market place of ideas – trusted environment to share
Nurturing environment – support students and participants
to learn and share resources

PRAGMA Members andAffiliates
http://www.pragmagrid.net/

4Genomics Analysis Use-Case} International Rice Research Institute (IRRI), Manila,Philippines, has researchers carrying out genome wideassociation studies (GWAS) of their own phenotypingdata} IRRI has 3000 rice genomes and a common analysis
framework} IRRI is willing to provide analysis framework for free, butwants researchers to share results back to IRRI} Also interested in reproducibility of experimental results} Key concepts: data sharing and data reproducibility (FAIR)

5Typical Workflow Scenarios
Inside Galaxy Workflow
Input dataset(phenotype)

Upload phenotype data

Built-in genotype dataset(3K RG core SNPs)

Select phenotype data to merge;
Merge phenotype to genotype data

6

Pre-computed
kinship matrix

GWAS GLM(parameters specified)

GWAS MLM(parameters specified)

GWAS GLM
Results

GWAS MLM
Results

Modular Pieces to Architecture
Rice genome variant discovery

1

PRAGMA
compute VMs:
Rocks VM roll for
Galaxy workﬂow

Persistent ID Types (PIT)
2 RecommendaFon:
conceptualmodel for structuring typed
information, an applicationprogramming interface foraccess to typed informationand demonstrator
implementing the interface

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Data Type Registry (DTR)
RecommendaFon: aid data
sharing efforts throughimproved data typing,
specifically through afederated registry forregistering data types.

PRAGMA Data Services:

4

Model for extracFng
provenance from RocksVMs and client tool;5

MongoDB data store
for published data;
Repository side service

6 for storing data objects,
creaFng landing pages

Our overall solution

1. End-user

2. Repository Service

3. PID Service

Reuse DO and Reproduce Workflow
IRRI Galaxy VMGalaxy
PortalDataIdentity
PortalGalaxyWorkflow

DataIdentity
clientUser experimental DO
8PRAGMA Data
Repository
MongoDB
DO torepository database

Data Service
DataIdentity
ServerRDA
PITRDADTR

DO assigned persistentidentifier (handle) and landing
pageDemo – Reproducibility in Rice Genomics
Workflow9Success to Date} The PRAGMA Data Services is a user transparent meansof harvesting DOs from applications and assignment ofPIDs to scientific outcomes

10}

Modular architecture, informed by core members of the ricegenomics team}

Software is stable.

}

Built with default PID information types and metadata (RDAinside!)}High-impact, multi-disciplinary effort in the Pacific Rim}

Cross WG interactions in RDA (Rice Data InteroperabilityWG)

Significance} PIDs for all types of data, not necessarily for

publishing of datasets associated with publications}

Imagine a world where PIDs identify just about everything:- Sensors/actuators (IoT)- Movie clips- Pages from digitized books- Baby food containers

When all objects have a PID, imagine an Internet(software) client that is handed a list of a billion IDs.
} How will the client quickly sift through the list to find, forexample, the entities that are medical research data?
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Next Steps} Harden PID services for IRRI rice genomics community} User study} User interface improvements} Define and convey to users policy issues on sharing results} Release anticipated late 2017} Growing US engagement in PID use through RDA} Lead effort (Beth Plale, Tobias Wiegel)} Data Fabric IG} Define minimal metadata for PIDs

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Acknowledgement} Funded in part by:} RDA US - MacArthur Foundation} PRAGMA NSF OCI-1234983} AIST ICT International Team} Special thanks to CNRI for use of Handle V8 serverand NDS for compute resources13