Musabase is a phenotyping and breeding database for bananas that aims to connect data within and between banana breeding projects. It digitizes field data collection through tools like barcoding and digital forms. The database integrates data from various sources like field trials, surveys, sequencing data and tissue culture. It links this in situ breeding data to ex situ conservation resources to create a "digital ecosystem" for banana breeders and researchers. Future work will expand collaboration and train local partners to continue developing open-source tools to improve banana improvement for smallholder farmers.

1. MUSABASE
Guillaume Bauchet
Mueller lab
gjb99@cornell.du
Plant and Animal Genome, San Diego, 2018
Musabase:
A Phenotyping and Breeding
Database for Bananas

2. https://btiscience.org/lukas-mueller/#lab-members
Mueller lab
- Bioinformatics
- Genomics
- Databases
MUSABASE

3. + University of Queensland, Australia
+ National Banana Breeding Program, India
+ University of Malaya, Malaysia
Improvement of
banana
for smallholder
farmers
in the Great Lakes
Region
of Africa
Projectscope

4. Genomic Selection
(Moses presentation)
But also:
-> Conventional breeding
-> Plant pathology
-> Parmer preferences
-> Germplasm Management
->Tissue culture
Projectscope

5. Projectscope
multiple data types…
- Phenotyping experiments
- Participatory trials
- Farmer surveys
- Tissue culture
- Sequencing data
…and a wealth of biological
specificities!
- Various ploidy levels
- Germplasm groups
- Complex pedigrees
- Plant and field size
- Life cycle length
=> different tools and approaches!

6. => Need for an “in situ” resource, a breeding information repository
The banana “digital ecosystem”
Ex situ conservation
Molecular data
Semantic data

7. Musabase
MUSABASE http://musabase.org/

8. MUSABASE
Field data collection: a demanding process…

9. MUSABASE
Field data collection: digitalize it!

10. Field data collection: digitalize it!
MUSABASE

11. Connecting dots between and
within projects…
MUSABASE
Field
Lab
MGIS
Crop ontology

12. Field data collection: digitalize it!
MUSABASE
Barcoding
https://musabase.org/barcode

13. Field data collection: digitalize it!
MUSABASE
Ontologies: streamline management
http://submit.rtbbase.org/
Banana ontology Pipeline
Credits: EM-A Laporte, . Arnaud (Bioversity)

14. Field data collection: digitalize it!
MUSABASE
Ontologies: postcomposing
https://musabase.org/tools/compose
https://musabase.org/search/traits

15. Field data collection: digitalize it!
MUSABASE
Field geolocations
https://musabase.org/breeders/locations

16. Field data collection: digitalize it!
MUSABASE
Pedigree
David Lyon’s presentation

17. Field data collection: digitalize it!
MUSABASE
Surveys
https://odk.ona.io/
-> Need for dynamic data collection processes
-Farmer surveys
-Field procedure (crosses)
-Lab procedure (tissue culture)

18. Field data collection: digitalize it!
MUSABASE
Wish list + Crossing tool
http://btract.sgn.cornell.edu/
https://musabase.org/breeders/crosses/
Credits: Margaret Karanja, Trushar Shah (IITA)

19. MUSABASE
-> Link ex situ (MGIS) vs in situ (musabase)
data
-> Link additional molecular resources
(genome hub, gobii)
-> Additional tools for banana breeders
Perspectives: reach the “digital ecosystem”
https://brapi.org/

20. -> On site trainings
-> Data managers
MUSABASE
Perspectives: keep building partnership
Arusha Tanzania NARO Uganda IITA Uganda
BTI Cornell
-> Collaborations with Bioversity/Crop
ontology colleagues + new partners

21. Connecting dots between and
within projects…
MUSABASE
Questions?
gjb99@cornell.edu
http://slideshare.net/solgenomics
Field
Lab
MGIS
Crop ontology