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2011 jeroen vanhoudt_ngs
The document discusses next-generation sequencing (NGS) technologies and NGS targeted re-sequencing. It provides an overview of NGS technologies including their development over time. It then discusses NGS targeted re-sequencing by focusing on specific regions of interest through library enrichment techniques. Finally, it outlines the typical NGS exome sequencing pipeline from sample preparation to data processing, analysis and reporting of results.
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2011 jeroen vanhoudt_ngs
- 2. Next-Generation sequencing(NGS) technologies – overview NGS targeted re-sequencing – fishing out the regions of interest NGS workflow: data collection and processing – the exome sequencing pipeline
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- 4. The automated Sangermethod is considered as a ‘first- generation’ technology, and newer methods are referred to as next- generation sequencing (NGS).
- 5. 1953 Discoveryof DNA double helix structure 1977 ◦ A Maxam and W Gilbert "DNA seq by chemical degradation" ◦ F Sanger"DNA sequencing with chain-terminating inhibitors" 1984 DNA sequence of the Epstein-Barr virus, 170 kb 1987 Applied Biosystems - first automated sequencer 1991 Sequencing of human genome in Venter's lab 1996 P. Nyrén and M Ronaghi - pyrosequencing 2001 A draft sequence of the human genome 2003 human genome completed 2004 454 Life Sciences markets first NGS machine
- 7. Random genome sequencing • 25 Mb •300k reads • 110bp Sanger sequencing • Targeted • 700-1000 bp
- 8. The newertechnologies constitute various strategies that rely on a combination of ◦ Library/template preparation ◦ Sequencing and imaging
- 9. Commercially availabletechnologies ◦ Roche – 454 GSFLX titanium Junior ◦ Illumina HiSeq2000 MySeq ◦ Life – SOLiD 5500xl Ion torrent ◦ Helicos BioSciences – HeliScope ◦ Pacific Biosciences – PacBio RS
- 11. Produce anon-biased source of nucleic acid material from the genome
- 12. Produce anon-biased source of nucleic acid material from the genome
- 13. Produce anon-biased source of nucleic acid material from the genome Current methods: ◦ randomly breaking genomic DNA into smaller sizes ◦ Ligate adaptors ◦ attach or immobilize the template to a solid surface or support ◦ the spatially separated template sites allows thousands to billions of sequencing reactions to be performed simultaneously
- 14. Clonal amplification ◦Roche – 454 ◦ Illumina – HiSeq ◦ Life – SOLiD Single molecule sequencing ◦ Helicos BioSciences – HeliScope ◦ Pacific Biosciences – PacBio RS
- 15. In solution– emulsion PCR (emPCR) ◦ Roche – 454 ◦ Life – SOLiD Solid phase – Bridge PCR ◦ Illumina – HiSeq
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- 26. The majoradvance offered by NGS is the ability to cheaply produce an enormous volume of data The arrival of NGS technologies in the marketplace has changed the way we think about scientific approaches in basic, applied and clinical research
- 27. fishing out theregions of interest
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- 29. Library/template preparation Library enrichment for target Sequencing and imaging
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- 36. In solution • Relativelycheap • High throughput is possible • Small amounts of DNA sufficient Solid phase • Straightforward method • Flexible • Higher amounts of DNA
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- 40. • 48.48 AccessArray
- 41. • 48.48 AccessArray
- 42. • 48.48 AccessArray
- 44. data collection andprocessing – the exome sequencing pipeline
- 45. The humangenome ◦ Genome = 3Gb ◦ Exome = 30Mb ◦ 180 000 exons Protein coding genes ◦ constitute only approximately 1% of the human genome ◦ It is estimated that 85% of the mutations with large effects on disease-related traits can be found in exons or splice sites
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- 47. 1/01/2010 1/08/2010 1/01/2011 1100 860 300 5900 2600 1000 7000 3460 1300 exomecapture Seq - 2.5Gbases total cost
- 48. HiSeq specifications: ◦2 flow cells ◦ 16 lanes (8 per flow cell) ◦ 200-300 Gbases per flow cell ◦ 10 days for a single run Exome throughput ◦ 96 @ 60x coverage per run ◦ 3000 @ 60x coverage per year
- 49. Data formatting &QC Mapping & QC Variant calling Variant annotation Variant filtering/comparison
- 52. DATA STORAGEDATA GENERATIONDATA PROCESSING REPORTING & VALIDATION RESULTS INTERPRETATION
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- 56. Sequence Data 10-15 Gb/ exome DATA STORAGEDATA GENERATION DATA PROCESSING Image processing Base calling
- 58. 1 •Mapping 2 •Duplicate marking 3 •Local realignment 4 •Basequality recalibration 5 •Analysis-ready mapped reads
- 59. Sequence Data 10-15 Gb/ exome DATA STORAGEDATA GENERATION DATA PROCESSING Image processing Base calling QC sequencing Mapping sequences QC capture exp
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- 62. Sequence Data 10-15 Gb/ exome DATA STORAGE Mapping results 5 Gb / exome DATA GENERATION DATA PROCESSING Image processing Base calling QC sequencing Mapping sequences QC capture exp Variant Calling Variant Annotation
- 66. Sequence Data 10-15 Gb/ exome DATA STORAGE Mapping results 5 Gb / exome Variant Calls 100Mb / exome DATA GENERATION DATA PROCESSING Image processing Base calling QC sequencing Mapping sequences QC capture exp Variant Calling Variant Annotation
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- 68. 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 stopgain SNV nonsynonymous SNV nonframeshiftinsertion nonframeshift deletion non-coding frameshift insertion frameshift deletion
- 69. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 synonymous SNV stoploss SNV stopgainSNV nonsynonymous SNV nonframeshift insertion nonframeshift deletion frameshift insertion frameshift deletion
- 70. 0 50 100 150 200 250 300 350 400 450 500 stoploss SNV stopgain SNV nonframeshiftinsertion nonframeshift deletion frameshift insertion frameshift deletion
- 71. Sequence Data 10-15 Gb/ exome DATA STORAGE Mapping results 5 Gb / exome Variant Calls 100Mb / exome DATA GENERATION DATA PROCESSING Image processing Base calling QC sequencing Mapping sequences QC capture exp Variant Calling Variant Annotation Database known Variants Public & Private Variant Filtering
- 73. Sequence Data 10-15 Gb/ exome DATA STORAGE Mapping results 5 Gb / exome Variant Calls 100Mb / exome DATA GENERATION DATA PROCESSING Image processing Base calling QC sequencing Mapping sequences QC capture exp Variant Calling Variant Annotation Database known Variants Public & Private Variant Filtering REPORTING & VALIDATION RESULTS Validated variants in candidate genes INTERPRETATION
Editor's Notes
- #7 2005