Introduction to Data Management Arllet M. Portugal Integrated Breeding Platform Breeding Management System Intensive Workshop on Data Management Jan. 26, 2015 Rice Gene Discovery Unit, Kasetsart University, Kamphangsaen

Importance of Data Management Data that are properly managed are:

Importance of Data Management Data that are properly managed are: = Shareable more accessible to research partners national and global sharing & linking of data

Importance of Data Management Data that are properly managed are: = Available enables reliable analysis & conclusions leads to better science & more sophisticated research

Importance of Data Management Data that are properly managed are: = Re-usable more likely to be used again for different purposes

Importance of Data Management Data that are properly managed are:  important for historically significant data  scientific method changed to: hypothesize, then look up answer in database = Preservable

Importance of Data Management Data that are properly managed are: areable vailable e-usable reservable

3 Types of Data 1) Genealogy or pedigree data parents unique identification of germplasm through Germplasm IDs (GID) names

2) Phenotypic data 3 Types of Data observable characteristics or traits environmental data across studies are linked via controlled trait vocabularies / standard terms

3) Genotypic data 3 Types of Data  usually with reference to a specific trait under consideration genetic composition

Principles of DM for Integrated Breeding (IB) IB requires high standards of sample and pedigree identification, It requires integration of field and lab data, and quality is of paramount importance. Data collected during breeding processes has immediate value for breeders and it also has cumulative value over years and populations.

The Crop Databases

The Crop Databases …  The germplasm has a unique identifier which is the key link among the different data in the database  The evaluation data will allow across study query or time-series analysis

Curation of Germplasm Information  The germplasm is described by the following:  The breeding method on how the germplasm was developed  The parents of the germplasm if it is developed by generative method such as single cross  The cross and the source if it is developed by derivative method such as single plant selection  The date and location of its development  The different names given to it with one as the preferred name

Curation of Germplasm Information …  Protocol should be established in naming Nursery or Trial list e.g. RYT2012W The cross e.g. CAAS 1001 The selection or population line e.g. - CAAS

Curation of Evaluation Data  Describe the study or trial  Provide clear specification of the traits to be measured which includes the measurement unit and the method of measurement  Provide the field design

Description of trait

Trait Dictionary  A trait dictionary contains a list of traits with full description about them  GCP established trait dictionaries for ten crops which consist of traits regularly measured in a breeding program  They are established through a set of processes which involves validation of the initial list of traits, ranking of the traits, analysis of the ranking and complete documentation of the important traits for breeding. This is done in consultation with 5 to 10 breeders within the crop community.

Crop Ontology  The Crop Ontology provides validated trait names used by the crop communities of practice for harmonizing the annotation of phenotypic and genotypic data and thus supporting data accessibility and discovery through web queries.  An important feature is the cross referencing of CO terms with the Crop database trait ID and with their synonyms in Plant Ontology and Trait Ontology. Web links between cross referenced terms in CO provide online access to data annotated with similar ontological terms,  The established Trait Dictionaries are uploaded in the Crop Ontology

Goal Use BMS in keeping data namely germplasm and phenotype data (nursery, trial) of several crops (rice, cassava, maize, vegetables, etc.) that were generated in breeding programs and possibility of establishing a central database at station and institute levels.

Objectives To provide training and support as part of the capacity building component of IBP for a broader adoption and use of BMS in the overall breeding programme of NARS and companies working on plant breeding. To use actual data in training to identify specific component(s) of IBP that needs customization to properly accommodate data specific to a crop generated in breeding program