The New Genomics

Transcript

1. The New Genomics matthew@amazon.com Dr. Matt Wood

2. Hello

3. Hello

4. Data

5. DNA

6. Chromosome 11 : ACTN3 : rs1815739

7. Chromosome X : rs6625163

8. Chromosome 19 : FUT2 : rs601338

9. +0.25 Chromosome 15 : rs2472297

10. Chromosome 2 : rs10427255

11. TYPE II Chromosome 10 : rs7903146

12. Chromosome 1 : rs4481887

13. I know this, because...

14. None

15. A T C G G T C C A G

    G

16. A T C G G T C C A G

    G A G C C A G G U C C Transcription

17. A T C G G T C C A G

    G A G C C A G G U C C Translation Ser Glu Val Transcription
18. None
19. None

20. Chromosome 11 : ACTN3 : rs1815739

21. Chromosome X : rs6625163

22. Chromosome 19 : FUT2 : rs601338

23. +0.25 Chromosome 15 : rs2472297

24. Chromosome 2 : rs10427255

25. TYPE II Chromosome 10 : rs7903146

26. Chromosome 1 : rs4481887

27. I know all that, because...

28. Human Genome Project

29. 40 species ensembl.org

30. Compare species

31. Biological importance

32. Step change

33. Less time. Lower cost.

34. None
35. None

36. Compare individuals

37. None

38. Data generation costs are falling (pretty much everywhere)

39. Sequencing challenge X

40. Amazona vittata

41. Analytics challenge

42. Lots of data, Lots of uses, Lots of users, Lots

    of locations

43. Cost

44. Analytics challenge X

45. Accessibility challenge

46. The New Genomics

47. Graceful. Beautiful.

48. Impossible to re-create

49. Snowflake Science

50. Reproducibility

51. Reproducibility scales science

52. Reproduce. Reuse. Remix.

53. Value++

54. None

55. How do we get from here to there? 5PRINCIPLES REPRODUCIBILITY

    OF

56. 1. Use the gravity of data 5 PRINCIPLES REPRODUCIBILITY OF

57. Increasingly large data collections

58. 1000 Genomes Project: 200Tb

59. Challenging to obtain and manage

60. Expensive to experiment

61. Large barrier to reproducibility

62. Data size will increase

63. Data integration will increase

64. Move data to the users

65. Move data to the users X

66. Move tools to the data

67. Place data where it can consumed by tools

68. Place tools where they can access data

69. None
70. None
71. None

72. Canonical source

73. None

74. More data, more users, more uses, more locations

75. Cost and complexity

76. Cost and complexity kill reproducibility

77. Utility computing

78. Availability

79. Intel Xeon E5 NVIDIA Tesla GPUs

80. 90 - 120k IOPS on SSDs

81. Pay-as-you-go

82. 100% Reserved capacity

83. 100% Reserved capacity On-demand

84. 100% Reserved capacity On-demand

85. Spot instances

86. Name-your-price

87. None

88. 2. Ease of use is a pre-requisite 5 PRINCIPLES REPRODUCIBILITY

    OF

89. http://headrush.typepad.com/creating_passionate_users/2005/10/getting_users_p.html

90. Help overcome the suck threshold

91. Easy to embrace and extend

92. Choose the right abstraction for the user

93. $ ec2-run-instances

94. $ starcluster start

95. None
96. None

97. Package and automate

98. Package and automate Amazon machine images, VM import

99. Package and automate Amazon machine images, VM import Deployment scripts,

    CloudFormation, Chef, Puppet

100. Expert-as-a-service

101. None
102. None

103. 1000 Genomes Cloud BioLinux

104. None

105. Your HiSeq data Illumina BaseSpace

106. DNA and RNA sequences Genomespace, Broad Institute at MIT

107. Data as a programmable resource

108. 3. Reuse is as important as reproduction 5 PRINCIPLES REPRODUCIBILITY

     OF

109. Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics

110. Seven Deadly sins of Bioinformatics: http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics

111. Infonauts are hackers

112. They have their own way of working

113. The ‘Big Red Button’

114. Fire and forget reproduction is a good first step, but

     limits longer term value.

115. Monolithic, one-stop-shop

116. Work well for intended purpose

117. Challenging to install, dependency heavy

118. Inflexible

119. Embrace infonauts as hackers

120. Small things. Loosely coupled.

121. Easier to reuse

122. Easier to integrate

123. Scale out

124. Cancer drug discovery: 50,000 cores < $1000 an hour Schrödinger

     and CycleServer

125. 4. Build for collaboration 5 PRINCIPLES REPRODUCIBILITY OF

126. Workflows are memes

127. Reproduction is just the first step

128. Bill of materials: code, data, configuration, infrastructure

129. Full definition for reproduction

130. Utility computing provides a playground for data science

131. Code + AMI + custom datasets + public datasets +

     databases + compute + result data

132. Code + AMI + custom datasets + public datasets +

     databases + compute + result data

133. Code + AMI + custom datasets + public datasets +

     databases + compute + result data

134. Code + AMI + custom datasets + public datasets +

     databases + compute + result data

135. Package, automate, contribute.

136. Utility platform provides scale for production runs

137. 5. Provenance is a first class object 5 PRINCIPLES REPRODUCIBILITY

     OF

138. Versioning becomes really important

139. Especially in an active community

140. Doubly so with loosely coupled tools

141. Provenance metadata is a first class entity

142. Distributed provenance

143. 5PRINCIPLES REPRODUCIBILITY OF

144. Remove constraints 5PRINCIPLES REPRODUCIBILITY OF

145. Accelerate science 5PRINCIPLES REPRODUCIBILITY OF

146. Chromosome 11 : ACTN3 : rs1815739

147. Chromosome X : rs6625163

148. Chromosome 19 : FUT2 : rs601338

149. +0.25 Chromosome 15 : rs2472297

150. Chromosome 2 : rs10427255

151. TYPE II Chromosome 10 : rs7903146

152. Chromosome 1 : rs4481887

153. Thank you aws.amazon.com @mza matthew@amazon.com