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- 1. © 2009 Illumina, Inc. All rights reserved. Illumina, illuminaDx, Solexa, Making Sense Out of Life, Oligator, Sentrix, GoldenGate, GoldenGate Indexing, DASL, BeadArray, Array of Arrays, Infinium, BeadXpress, VeraCode, IntelliHyb, iSelect, CSPro, and GenomeStudio are registered trademarks or trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. COMPANY CONFIDENTIAL – INTERNAL USE ONLY Genome Informatics Alliance 2013 Defining Genomic Big Data and its Impact on Scientific Progress
- 2. 2 COMPANY CONFIDENTIAL – INTERNAL USE ONLY From Whence We Came… ATGCCGTTT… CCGGTTAAT… GAATTGCAG… 6:A2567C 12:C123T 20:T4678A 30-40TB ˜5TB 600GB ˜20GB
- 3. 3 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Genomic Big Data Large amounts of data generated in genomics; multiple samples, size of data, etc Integration of digital data to enrich context of samples; DNA, RNA, methylation, time courses, spatial distributions with samples, … Fusion of digital data and categorical data; combination rules (categories), extraction from unstructured inputs, … Tools and techniques appropriate for resultant data sets; visualization, model building, exploration, … Advances require data mining rather than the one-at-a- time hypothesis testing approaches of today
- 4. 4 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Genomic Big Data and Personal Genome Information PERSONAL SEQUENCE (owned by individual/doctor) Issued: 01 MAR 07 Recommended next check: 28 FEB 10 PGI id: 5910322 – 61215923014 RISK VARIANTS (approved for clinical use) Human Genome Clinical studies Populations SequencingFunctional annotation 3: 12,300 3: 12,400 ( kb ) PPARg GENOMIC ANNOTATION (in public domain) Variant: C3 : 12,450,610 : T0.7/C0.3 : PPARG : Pro/Leu : Medical consequence: Associated with severe insulin resistance, diabetes mellitus, hypertension Pharmacological consequence: Resistant to thiazolidinediones CLINICAL DECISION Consultation Consent Clinical assessment Selected risk information
- 5. 5 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Sequencing a 17-member three-generation pedigree. – Ultra deep sequencing improves sensitivity – Leveraging inheritance information improves accuracy – Data and results made publicly available Identifying ultra accurate genomic variants is enabling rapid improvements in technology and software This data will allow us to assess accuracy for many FDA submissions We are collaborating with NIST & CDC to develop a public resource for quantifying sequencing accuracy Platinum Genomes as a Truth Reference Creating a catalogue of highly-accurate SNPs, indels & SVs
- 6. 6 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Reduction from 40 Q-scores to 8 Q-scores becoming accepted Sequencing output is still increasing exponentially therefore further compression is likely to be required Platinum genome work suggest ~95% of genome is consistently called (this 95% is known as the platinum regions) Regions which are reliably called may not need 8 Q-scores resolution – we can reduce “well sequenced” regions to 2 Q- scores Start with 8 Q-score bam file: – Reduce the platinum regions to 2 Q-scores (keep non- platinum at 8 Q-scores) – Reduce the platinum regions to 1 Q-score – Whole genome 2 Q-score – Reduce platinum region to 2 Q-scores but also keep original Q-scores of mismatches (MM) and anomalous reads – ~40Gb (20Gb CRAM) Data Reduction Via Vertical Compression (NA12882) Build Total SNPs (>Q20) SNPs diff genotype (>Q20) Not called in Q-score compressed build (>Q20) Not called in 8 Q-score build (>Q20) 8 Q-score 3,735,575 (3,627,165) - - - 8 Q-score technical replicate 3,734,849 (3,626,485) 45,584 (22,400) 80,131 (29,211) 79,405 (28,845) Platinum Genome 2 Q- score 3,732,568 (3,620,612) 3,255 (161) 3417 (63) 410 (127) Platinum Genome 1 Q- score 3,764,928 (3,626,468) 4002 (584) 2605 (75) 31,958 (2964) Whole Genome 2 Q-score 3,712,636 (3,598,400) 25,175 (1912) 24,237 (166) 1298 (112) Platinum 2 q- score keep MM and anom. reads 3,735,684 (3,627,226) 197 (123) 142 (35) 251 (102)
- 7. 7 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Faster Data – DNA to Result in <2 Days 12 core server 64Gb RAM Sequence Analyze AnnotateSample 27 hr 8 hr HiSeq2500 Isaac analysis overnight 40 hr Fast turnaround is required for clinical applications 4.5 hr PCR Free library
- 8. 8 COMPANY CONFIDENTIAL – INTERNAL USE ONLY WGS reveals somatic mutations in TERT gene promoter of melanoma patients Form a novel transcription factor binding motif Recurrence in melanoma is as high as any known coding mutation Importance of Non-coding Mutations – Bigger Data! -200 -100 TERT gene 0 +100 +200 Gene (mutation) Incidence in melanoma TERT (promoter) 52% BRAF (V600E) 53% CDKN2A 50% NRAS (Q61R) 28% TERT (coding) 1% Horn et al. & Huang et al., Science 2013
- 9. 9 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Complexity of Data
- 10. 10 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Surveillance of Leukaemia (CLL) – More Data Complexity! 0 6463 65 6662 Event Timeline Sequencing Birth DeathTreatmentDiagnosis TreatmentTreatment 0 50 100 150 200 250 a b c d e NORMAL CLASS 4 CLASS 3 CLASS 2 CLASS 1 Time points Abundance Changing subclonal populations 0 1 2 3 4 5 c NO CL CL CL CL “Remission” has disease Schuh et al., Oxford
- 11. 11 COMPANY CONFIDENTIAL – INTERNAL USE ONLY A Deeper Complexity of Genomic Data
- 12. 12 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Utility Requires Complex Composite Information iPad Plug and Play Cloud Allele Frequency in populations www.1000genomes.org Medical/Risk data (with expert review) Hgmd, pharmgkb Genetic Variants dbSNP Functional Effects ensembl.org, genome.ucsc.edu, encode.org Disease association genome.gov ANNOTATED GENOME ( gVCF) <1Gbyte Ancestry Tissue type Risk Carrier status Diagnosis Drug response Annotate DisseminateInterpret
- 13. 13 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Apps Public Genomic Databases Users EMR Support & Engineering Instruments Genomic Big Data Ecosystems
- 14. 14 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Genomic Big Data Status Researcher Treatment choice Clinician Patient Knowledge Information
- 15. 15 COMPANY CONFIDENTIAL – INTERNAL USE ONLY Challenges for this Meeting to Address What data frameworks and models are required? How will genomes (DNA, RNA, methylation states, etc) be aggregated and compared? How will collaboration and data sharing evolve? Where will the technology go and how must the community respond to lever the benefits Brainstorming of ideas Sessions from groups that have experiences from many fields Next steps!! Actively participate and enjoy the entire experience!