2013 11-13-SoftwareSustainabilityInstitute@Manchester
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The document discusses genomics tools for studying ant and other emerging model organisms. It highlights several sequencing technologies like 454, Illumina, and Solid that have made sequencing accessible to any lab. Major questions in ant genomics include identifying genes involved in social behavior and longevity. The site Antgenomes.org and tool SequenceServer.com are recommended for genomic work on ants and other emerging model organisms.
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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.