Musabase breeding database overview at the Plant and Animal Genome (banana genomics session)

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