Systems biology is becoming a data-intensive science due to the exponential growth of genomic and biological data. Large projects now produce petabytes of data that require new computational infrastructure to store, manage, and analyze. Cloud computing provides elastic resources that can scale to support the increasing data needs of systems biology. Case studies show how clouds are used for large-scale data integration and analysis, running combinatorial analysis over genomic marks, and enabling reanalysis of biological data through elastic virtual machines. The Open Cloud Consortium is working to provide open cloud resources for biological and biomedical research through testbeds and proposed bioclouds.

1. Is Systems Biology Becoming a Data Intensive Science? Assuming So, Are You Ready?December 7, 2009Robert GrossmanLaboratory for Advanced ComputingUniversity of Illinois at Chicago1

2. Part 1Biology as a Data Intensive Science.2Two of the 14 high throughput sequencers at the Ontario Institute for Cancer Research (OICR).

3. Growth of Genomic DataENCODEHGP20032001197719952005Sanger SequencingMicroarray technology454, Solexa sequencing10^10Genbank10^510^8

4. Growth of Genomic DataSequence individualsAWS HadoopGFSSequence environment200620082003Sequence speciesENCODEHGP20032001197719952005Sanger SequencingMicroarray technology454, Solexa sequencing10^10Genbank10^510^8

5. The Challenge is to Support Cubes of High Throughput Sequence DataEach cell in data cube can be ChIP-chip, ChIP-seq, RNA-seq, movie, etc. data set.Different developmental stagesDifferentconditionsPerturb the environment

6. We Have a Problem…vsMore and more of your colleagues produce so much data that they cannot easily manage & analyze it. Large projects build their own infrastructure.Every else is on their own.

7. 200310x-100x197610x-100xdatascience1670250xsimulation science160930xexperimental science

8. To Answer today’s biological questionsPoint of ViewAnalytic infrastructureAnalytic algorithms & statistical modelsData

9. Part 2What is a Cloud?9

10. What is a Cloud?10Software as a Service

11. Is Anything Else a Cloud?11Infrastructure as a Service – based upon scaling Virtual Machines (VMs)

12. Are There Other Types of Clouds?12web search & ad targeting Large Data Cloud Services

13. What is Virtualization?13

14. Idea Dates Back to the 1960s14AppAppAppCMSCMSMVSIBM VM/370IBM MainframeNative (Full) VirtualizationExamples: Vmware ESXVirtualization first widely deployed with IBM VM/370.

15. What Do You Optimize?Goal: Minimze latency and control heat.Goal: Maximze data (with matching compute) and control cost.

16. 16Scale is new

17. Elastic, Usage Based Pricing Is New17costs the same as1 computer in a rack for 120 hours120 computers in three racks for 1 hourElastic, usage based pricing turns capex into opex.

18. Clouds can be used to manage surges in computing.Simplicity Offered By the Cloud is New18+.. and you have a computer ready to work.A new programmer can develop a program to process a container full of data with less than day of training using MapReduce.

19. 19Clouds vs Grids

20. Part 3Case Studies

21. Case Study 1Cistrack Large Data Cloud21www.cistrack.org

22. CistrackResource for cis-regulatory data.It is open source and based upon CUBioS.Currently used by the White Lab at University of Chicago for managing ModENCODE fly data.Contains raw data, intermediate, and analyzed data from approximately 240 experiments from Agilent, Affy and Solexa platforms.

23. CUBioS ApplicationsFront EndsCUBioSBowtie, TopHAT, R pipelines, etc… IngestionCistrack is an instance of CUBioS.RNA seqChIPseqDNA captureetc.

24. Chromatin Developmental Time-CourseH3K4me1 enhancersH3K4me3 promoters & enhancersH3K9Ac activationH3K9me3 heterochromotinH3K27Ac activationH3K27me3 repressionPolII transcript. & promotersCBP HAT- enhancersTotal RNA expressionX12 time points for which chromatin and RNA have been collected (Carolyn Morrison & Nicolas Negre)8 antibodies used for ChIP experiments (Zirong Li and Bing Ren)

25. Cistrack Supports Multi-Dim. Cubes…Drosophila regulatory elements from Drosophila modENCODE.ChIP-chip data using Agilent 244K dual-color arrays.Six histone modifications (H3K9me3, H3K27me3, H3K4me3, H3K4me1, H3K27Ac, H3K9Ac), PolII and CBP. Each factor has been studied for 12 different time-points of Drosophila development.

26. … Each Cell in a Cube Can Be Three ChIP-Seq Datasets from a SolexaCistrack integrates with large data clouds.Cistrack uses the Sector/Sphere large data cloud.

27. Hadoopvs Sector27Source: Gu and Grossman, Sector and Sphere, Phil. Trans. Royal Society A, 2009.

28. Cistrack Web Portal & WidgetsCistrack Elastic Cloud ServicesCistrack DatabaseAnalysis Pipelines & Re-analysis ServicesCistrack Large Data Cloud ServicesIngestion Services

29. Case Study 2: Combinatorial Analysis of Marks

30. Active Gene - MethodK4Me3 to TSS distanceGene Activeness: Label a transcript t as XYZX=1 if a H3K4Me3 binds in [-1800, min(2200, TranscriptLength)]Y=1 if a Pol II binds in [-1800, min(2200, TranscriptLength)]Z=1 if at least one exon has ≥30% covered by RNA, and in total ≥10% covered by RNA.Pol II to TSS distanceSource: Jia Chen et. al. (ModENCODE)

31. Promoters: Use H3K4me3, PolII & RNA to Map Active GenesSource: Jia Chen et. al. (ModENCODE)

32. Active Genes (cont’d)A.B.C.PolIIH3K4me3141833275361046804821350RNAbp from TSSbp from TSSSource: Jia Chen et. al. (ModENCODE)

33. Interesting Combinatorial Combination of MarksProbes along genome…MarksItem-sets formed by sliding moving window along genome. A-prior algorithm generates interesting itemsets.Post-processing retains itemsets of biological relevance.

34. Case Study 3Cistrack Elastic Cloud

35. Cistrack Elastic Cloud A rack in Cistrack’s Elastic Cloud contains 128 Cores and 128 TB.Multiple racks form a data center.Virtual machines can run pipelines.Virtual machines have access to large data services.No need to move large datasets in and out of Amazon public cloud.

36. Use VMs to Support ReanalysisReplaceCloudVMVMVMAt anytime, you can launch one or virtual machines (VMs) to redo pipeline analysis and persist results to a database and VM to a cloud.

37. Comparing Peak Calling Algorithms for ModENCODEWe’re using the Cistrack Elastic Cloud to rerun peak calls for fly data using the worm pipeline.Also running the worm peak calling pipeline on the fly data.

38. Case Study 4Ensembles of Trees on Clouds100 tree modelsdata10,000??? tree modelsWenxuanGao, Robert Grossman, Philip S. Yu, YunhongGu, Why Naïve Ensembles Do Not Work in Cloud Computing, Proceedings of LSDM, 2009.

39. Ensembles of Trees for CloudsTop-k ensemblesEach node builds single random tree with local data.Central node picks k best random trees to predict.Lower cost with corresponding lower accuracy.Shuffling data can improve accuracy.Skeleton ensemblesCentral node builds k skeletons of random trees.Each local node fills in the skeletons.Central node merges all trees from local nodes.Greater cost, but more accurate.

40. Experimental StudiesPerformed experimental studies on 4 racks (104 nodes) of Open Cloud Testbed.Standard ensemble based models are more expensive than proposed approaches and can overfit.Skeleton ensembles are more accurate but more expensive to build.Shuffling improves accuracy of top-k algorithm.For KDDCup99 dataset top-k ensembles with shuffling 0.1% of data matches accuracy of skeleton method.For UCI Census income dataset, 20% shuffle required, which is more expensive than top-k ensemble.Without knowledge of uniformity of dataset, recommend skeleton ensembles.

41. KDDCup99 datasetCensus income dataset

42. Part 5.Open Cloud ConsortiumBiocloud

43. Open Cloud TestbedC-WaveCENICDragonPhase 29 racks250+ Nodes1000+ Cores10+ Gb/sHadoop

44. Sector/Sphere

45. Thrift

46. KVM VMs

47. Eucalyptus VMsMREN43

48. Open Science Data Cloudsky cloudadditional projects in planning…biocloud44

49. OCC Condominium CloudsIn a condominium cloud, you buy your own rack or bunch of racks.The racks are managed and operated by the condominium association, in this case the OCC.If your rack is 120 TB, you get the rights to approx. 40 TB of storage in the cloud. The rest is a shared resource. 45

50. Acknowledgements

51. To Get InvolvedThe Cistrack resource for transcriptional data: www.cistrack.orgSector/Sphere cloud: sector.sourceforge.net

52. Thank YouFor more information: blog.rgrossman.com or www.rgrossman.com