Bio IT World europe 2010

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  • 1. Cloud Computing and Genomics Richard Holland BioITWorld Europe 2010 Cloud Computing Workshop
  • 2. Cloud Computing and Genomics ● Company background ● Who we support ● Sequence analysis in the cloud ● Genomic data in the cloud ● Cloud summary
  • 3. Raison d'être ● Founded by 3 ex- Ensembl staff. ● Commercial services for open-source bioinformatics. ● Support open- source through collaborations. ● RedHat of bioinformatics.
  • 4. What we do ● Help you choose the right solution for the problem. ● Right tools for the right jobs. ● For any chosen open-source system: ✔ Support and train. ✔ Modify, extend, and improve. ✔ Integrate private data/systems. ✔ Manage private mirrors.
  • 5. What we do ● Genomic data analysis pipelines ✔ SNP calling ✔ miRNA detection ✔ Probe mapping ✔ Assembly and annotation ✔ Etc. ● Results integrated into other systems.
  • 6. Open-source ● Used wherever possible. ● Produced wherever possible. ● Contribute all changes. ● Cutting-edge. ● Quality of concept (not necessarily code). ● Rapidly adjustable.
  • 7. Cloud ● Low-cost for ad-hoc work. ● Scalable. ● Low-maintenance. ● More secure. ● Accessible. ● No licences. ● No installation.
  • 8. Cloud Computing and Genomics ● Company background ● Who we support ● Sequence analysis in the cloud ● Genomic data in the cloud ● Cloud summary
  • 9. Ensembl ® ● Not only a genome browser, but also: ● Genome databases. ● Perl API and BioMart data warehouse. ● Collaboration agreement with EMBLEM. ● Exchange knowledge. ● Share revenue. ● EBI are working with Amazon. Ensembl is a registered trademark of Genome Research Ltd., and is developed by WTSI/EBI.
  • 10. Taverna ● Workflow design and execution. ● Collaboration agreement with the University of Manchester (in progress). ● Exchange knowledge. ● Share revenue. ● Taverna are actively researching cloud deployment.
  • 11. TraitTag ● Trait detection in seedlings. ● Avoid waiting for adult plants. ● Collaboration agreement with Plant Bioscience Ltd. (John Innes Centre) ● Exchange knowledge. ● Share revenue. ● Already cloud-capable.
  • 12. Others ● Other knowledge areas: ● O|B|F Bio* projects – e.g. BioJava, BioPerl, BioSQL, etc. ● BioMart ● DebianMed ● No formal collaborations with these.
  • 13. Big Pharma ● Drug development. ● Pharmacogenomics. ● Software-as-a-service. ● Operational bioinformatics. ● Traditionally suspicious of both open-source and the cloud. This is changing.
  • 14. Agri-biotech ● Chemical development. ● Seed development. ● Software-as-a-service. ● Operational bioinformatics. ● Into open-source (DuPont has BioPerl people), ready to explore the cloud.
  • 15. Biotech SMEs ● Anything and everything. ● Highly specialised. ● Poorly funded. ● Lack in-house bioinformatics. ● Many don't even know what it is. ● One-off analyses and consulting projects. ● Love anything that saves them money.
  • 16. Others ● CROs. ● Universities. ● Government institutes. ● Animal health an emerging area. ● Public bodies are hardest to convince about open-source and the cloud. ● Funding agencies often the cause.
  • 17. Cloud Computing and Genomics ● Company background ● Who we support ● Sequence analysis in the cloud ● Genomic data in the cloud ● Cloud summary
  • 18. eHive ● Used for all Eagle's data analysis pipelines. ● Developed by the Ensembl Compara project. ● Generic framework (yet another one?). ● Perl. ● Ensembl-aware but independent. ● Scalable and robust. ● Open-source.
  • 19. eHive Severin et al. eHive: An Artificial Intelligence workflow system for genomic analysis. BMC Bioinformatics 2010, 11:240
  • 20. eHive ● Works out-of-the-box on LSF. ● Can run standalone in a single machine. ● Modified to work on Condor and SGE. ● Modified to work on Amazon EC2 without needing Condor/SGE/LSF etc. (crafty trick)
  • 21. eHive Standalone pipelines Packaged into VMs (black-box appliances) Cumbersome to Doesn't scale well distribute What about the cloud?
  • 22. eHive ● Easy to set up same pipeline and interface on EC2. ● But doesn't solve scaling, and clusters are hard to implement in the cloud. ● Crafty trick – self-replicating self- terminating instances. ● Scales to as many parallel instances as required (up to a preset limit). ● Keeps idle instances alive to just short of the hour just-in-case.
  • 23. eHive on EC2 – an example ● A big pharma customer had some microarrays. ● Probe sequence data provided by vendors (Affymetrix, Agilent, etc.). ● Need high-quality mapping with quality scores and other metadata. ● Some chips have public mappings but not to required standard/format. ● Many chips have no public mappings at all.
  • 24. eHive on EC2 – an example eHive+EC2 Ens funcgen db Reports Probe sequences Extended Ens API Library of mapping pipelines
  • 25. eHive on EC2 – an example ● Getting data in/out no problem – very small. ● Mix-up with keys lead to key-per-project. ● Blackboard and results MySQL performance. ● Needed to prove that the job management is working. ● Not firing up too many machines. ● Not forgetting to shut them down.
  • 26. eHive on EC2 – an example ● Scales nicely to about 50 machines. ● Beyond that MySQL is the bottleneck. ● Performance-tuning MySQL raises limit. ● Deals better with lots of small input files and/or input as a database table. ● S3 slow but still plenty fast enough for projects like this (1 or 2 extra hours in the context of 100s is not much). ● Really need multiple-mount-RO EBS.
  • 27. eHive on EC2 – an example ● Customer data was sent to us encrypted, transferred to Amazon encrypted, only decrypted once inside Amazon. ● Results transferred out in the same way. ● All processing internal to Amazon. ● Usual steps to prevent access – firewall, stop unused daemons, disable logins, etc.
  • 28. Cloud Computing and Genomics ● Company background ● Who we support ● Sequence analysis in the cloud ● Genomic data in the cloud ● Cloud summary
  • 29. Hosted data services ● Genomic data can be big, but can also be small. ● Analysed data vs. raw data. ● Generic repositories vs. specialised resources. ● Organisations still sensitive about query intercept and log mining. ● Leads to in-house or secured third-party hosting.
  • 30. Hosted data services ● Simplest resources can run in a VM. ● Ensembl web browser (but not database). ● Functional, effective, if a bit slow. ● VM files slow to download, expensive to ship. ● Can achieve the same effect using the cloud. ● Distribute AMIs, or host instances?
  • 31. Hosted data services – an example ● A big pharma needs Ensembl in-house. ● Fed up with in-house maintenance. ● Wanted to try it in the cloud. ● Must run entirely within their own Amazon account.
  • 32. Hosted data services – an example Customer a/c Public Ens DB Eagle a/c Database Instance Database AMI Private Ens DB Browser AMI Browser instance
  • 33. Hosted data services – an example ● Ensembl public data in us-east region. ● Migration to other regions can be slow and expensive. ● No guaranteed update schedule. ● Updating it ourselves slow and expensive. ● How to avoid having to maintain multiple instances for multiple customers?
  • 34. Pistoia Sequence Services ● Pistoia Alliance is an industry alliance of big pharma and related companies. ● Sharing pre-competitive resources. ● Pistoia Sequence Services proof-of-concept ● To share Ensembl and related services. ● Eagle in partnership with Cognizant Technology Solutions were successful participants.
  • 35. Pistoia Sequence Services ● Already had the solution – an Ensembl AMI. ● Need to make it capable of running more than just Ensembl – easy. ● Some of the extra services need small amounts of private data, secured and partitioned – fairly easy. ● Need to secure it, and scale it – pretty hard.
  • 36. Pistoia Sequence Services Users Users Users Zeus Usage load balancer and with SSL Billing Mapping Ensembl PlasMapper etc. service Public mirror Private data
  • 37. Pistoia Sequence Services ● All user connections via SSL. ● Authentication using SSO against customer auth servers. ● OpenAM TrafficScript plugin for Zeus. ● SAML2 to LDAP, ActiveDirectory, etc. ● App servers firewalled. ● Load balancer connections only. ● No inter-connection except to RDBMS.
  • 38. Pistoia Sequence Services ● In future could add almost any sequence- related service that is/can be web-enabled. ● PoC runs until Christmas, with free access for all Pistoia members. ● Sowmyanarayan.Srinivasan@cognizant.com ● Simon.J.Thornber@gsk.com ● Full commercial service launches Q1 2011 subject to demand and PoC success.
  • 39. Cloud Computing and Genomics ● Company background ● Who we support ● Sequence analysis in the cloud ● Genomic data in the cloud ● Cloud summary
  • 40. Cloud is bad ● Expensive (always-on solutions). ● Slow data transfer in/out (it's the internet). ● Not big (S3 5GB limit). ● Not fast (HTC not HPC). ● Myths and misconceptions. ● Over-hyped. ● Misused.
  • 41. Cloud is good ● Scalable. ● Upgradeable. ● Secure. ● Low-cost (for ad-hoc work). ● Accessible. ● Standardised. ● Fast enough. ● Big enough.
  • 42. Choose your tools to suit the job ● Don't assume your paradigm will translate. ● Rephrase or optimise. ● Do it right, reap the rewards. ● Do it wrong, better off not bothering.
  • 43. Thanks ● Jason Stowe at CycleComputing. ● Matt Wood at Amazon. ● Cambridge Healthtech Institute. ● Our partners at Cognizant. ● Our collaborators at the EBI, WTSI, JIC, and University of Manchester. ● All producers of open-source bioinformatics software and data.
  • 44. www.eaglegenomics.com info@eaglegenomics.com