The computational requirements of next generation sequencing is placing a huge demand on IT organisations . Building compute clusters is now a well understood and relatively straightforward problem. However, NGS sequencing applications require large amounts of storage, and high IO rates. This talk details our approach for providing storage for next-gen sequencing applications. Talk given at BIO-IT World, Europe, 2009.

1. New sequencing Storage Guy Coates Wellcome Trust Sanger Institute [email_address]

5. Recent initiatives: 1 1000 Genomes

7. 2. The data explosion

8. 3. Building flexible systems

9. 4. Future directions

10. Scramble for Next-gen sequencing

16. Will we be CPU bound or IO bound?

19. Easy to scale.

20. Small unit of failure; system failure should only affect one sequencer.

21. Hard to right-size, especially if things change. CPU Disk Seq CPU Disk Seq CPU Disk Seq

23. Complicated. Clustered storage at scale is hard.

24. Eliminating SPOF is hard.

25. Scale out can be expensive. CPU FARM Cluster filesystem Seq Seq Seq Network Storage fabric

27. 384 cores for the sequencers, the rest for other analysis.

29. Lots of performance if we need it.

31. Compute farm analysis/QC pipeline suckers Data pull ... Final Repository (Oracle) 100TB / yr staging area 300 TB Lustre EVA Seq 1 Seq 38

33. The Data Explosion

34. The scary graph Instrument upgrades Peak Yearly capillary sequencing

39. ... Data pull ... Compute farm analysis/QC pipeline suckers Final Repository (Oracle) 100TB / yr staging area 300 TB Lustre EVA Seq 1 Seq 38 Compute Farm Compute farm disk Collaberators / 3 rd party sequencing Unmanged LIMS managed data

42. We get a good heads up for changes/plans.

44. Moving data is hard and slow.

47. Projects can outgrow compute + storage.

48. Fast storage is always full.

49. Cannot afford to buy more. network network Farm 1 Filesystem Farm 2 Filesystem Farm 1 Farm 2

50. Another Scary Graph Time Storage 4PB  Compute 5K cores  Sequencing output (xTBases)  Headcount  Budget 

51. Building Flexible Systems

53. bi-weekly updates to pipeline.

54. Evolutionary process.

57. 2. Start managing data

58. 3. Tie it all together into a flexible infrastructure

63. These are exotic and expensive.

64. Expect to break them and expect to spend time on care and feeding.

65. But you need them at scale.

68. Shortly followed by space and power footprints.

69. Power and space are constraints for us.

70. Dense disk shelves.

71. MAID functionality.

73. Use disk -> disk replication.

77. Work with the researchers to identify data and then map it onto storage.

78. Works well with the power users.

83. Assume from day 1 we will be adding more.

84. Expand simply by adding more blocks. LSF Fast scratch disk Archival / Warehouse disk

92. However, they are scientists, not sys-admins.

93. Their data is our responsibility.

94. Cloud

96. Bundle data and analysis pipelines for others to use.

100. Acknowledgements