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2013 11-13-SoftwareSustainabilityInstitute@Manchester
- 1. Ant genomics & Bioinformatics for emerging model organisms @yannick__ http://yannick.poulet.org
- 2. © Alex Wild & others
- 3. Animal biomass (Brazilian rainforest) Soil fauna excluding earthworms, ants & termites Spiders Earthworms Mammals Ants & termites Other insects Birds Reptiles Amphibians from Fittkau & Klinge 1973
- 7. Any lab can sequence anything! 454 Illumina Solid... This changes everything.
- 9. Major questions Which genes are involved in social behaviour?
- 10. Major questions Which genes are involved in social behaviour? Which genes make it possible for queens to live up to 30 years?
- 11. We need great tools.
- 12. Tools for genomics work on emerging model organisms
- 13. Tools for genomics work on emerging model organisms Antgenomes.org
- 14. Tools for genomics work on emerging model organisms Antgenomes.org SequenceServer.com BLAST made easy
- 17. Gene prediction Dozens of software algorithms: dozens of predictions Yandell & Ence 2013 NRG TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT
- 18. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Yandell & Ence 2013 NRG Evidence
- 19. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence Yandell & Ence 2013 NRG Consensus:
- 20. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: Yandell & Ence 2013 NRG Consensus:
- 21. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces Yandell & Ence 2013 NRG Consensus:
- 22. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces Yandell & Ence 2013 NRG Consensus:
- 23. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging Yandell & Ence 2013 NRG Consensus:
- 24. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging •incorrect splitting Yandell & Ence 2013 NRG Consensus:
- 25. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging •incorrect splitting Visual inspection... and manual fixing required. Yandell & Ence 2013 NRG Consensus:
- 26. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging •incorrect splitting Visual inspection... and manual fixing required. 1 gene = 20 minutes to 3 days Yandell & Ence 2013 NRG Consensus:
- 27. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging •incorrect splitting Visual inspection... and manual fixing required. 1 gene = 20 minutes to 3 days 15,000 genes * 20 species = impossible. Yandell & Ence 2013 NRG Consensus:
- 31. Gene prediction Dozens of software algorithms: dozens of predictions TTTTtACCTGTTTTtGAAAAGGTAATTTTCTTTAGATATATACAGTTTGTAATaTTAGGTATTTTATAAACAGTGTGTATATTTCTTACAATATAAAAGACACAATTGCAAACTAGCATGATTGTAAACAATTGCTAAACGGATCAATATAAATTAAAATTGTAATATTAAGTATCAAACCGATAATTTTT Evidence 20% failure rate: •missing pieces •extra pieces •incorrect merging •incorrect splitting Visual inspection... and manual fixing required. 1 gene = 20 minutes to 3 days 15,000 genes * 20 species = impossible. Yandell & Ence 2013 NRG Consensus:
- 32. Crowd-sourcing the visual inspection + correction. Anurag Priyam http://afra.sbcs.qmul.ac.uk
- 33. Crowd-sourcing the visual inspection + correction. Begin Curate Being curated Submit Curate Curate Being curated Submit Being curated Submit “ create nex Anurag Priyam http://afra.sbcs.qmul.ac.uk
- 34. Crowd-sourcing the visual inspection + correction. Scientific software + Facebook + Points + Badges + Redundancy Begin Curate Being curated Submit Curate Curate Being curated Submit Being curated Submit “ create nex Anurag Priyam http://afra.sbcs.qmul.ac.uk
- 36. My aim is to make bioinformatics software:
- 37. My aim is to make bioinformatics software: Shared
- 38. My aim is to make bioinformatics software: Shared Robust
- 39. My aim is to make bioinformatics software: Shared Robust User-friendly
- 40. With the fellowship I will:
- 41. With the fellowship I will: • Organize a local Software-Carpentry-type event.
- 42. With the fellowship I will: • Organize • Include a local Software-Carpentry-type event. software & reproducibility best practices on two new MSc & existing BSc.
- 43. With the fellowship I will: • Organize a local Software-Carpentry-type event. • Include software & reproducibility best practices on two new MSc & existing BSc. • Promote/lobby in line with SSI’s mission (internally/talks/conf// networking/publications/web).
- 44. With the fellowship I will: • Organize a local Software-Carpentry-type event. • Include software & reproducibility best practices on two new MSc & existing BSc. • Promote/lobby in line with SSI’s mission (internally/talks/conf// networking/publications/web). • Seek funds to create great sustainable bioinformatics software.