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Genomics, Bioinformatics, and Pathology
This document discusses next-generation sequencing and its applications in genomics and pathology. It begins with an overview of common NGS terms and technologies. It then covers the typical NGS analysis workflow including quality control, mapping reads to a reference genome, variant calling and annotation. Challenges such as data storage, sharing and reporting are also addressed. The document concludes that clinical sequencing is becoming established but requires ongoing collaboration between pathologists, geneticists and bioinformaticians to realize its potential.
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Genomics, Bioinformatics, and Pathology
- 1. Genomics, Bioinformatics, and Pathology DR. DANGASTON BEDARD LAB DEPARTMENT OF PATHOLOGY DALHOUSIE UNIVERSITY MAY 13TH, 2015
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- 7. NGS: Next-GenerationSequencing. A group of different sequencing technologies defined by high throughput and low cost A Short Primer on Common Terms
- 8. NGS: Next-GenerationSequencing. A group of different sequencing technologies defined by high throughput and low cost Short Read: The output from most NGS sequencing technologies. Range from 30bp to 300bp A Short Primer on Common Terms
- 9. NGS: Next-GenerationSequencing. A group of different sequencing technologies defined by high throughput and low cost Short Read: The output from most NGS sequencing technologies. Range from 30bp to 300bp Mapping: Placing sequencing reads on to a reference genome A Short Primer on Common Terms
- 10. NGS: Next-GenerationSequencing. A group of different sequencing technologies defined by high throughput and low cost Short Read: The output from most NGS sequencing technologies. Range from 30bp to 300bp Mapping: Placing sequencing reads on to a reference genome Variant Calling: Identifying sites of genetic variation between a sample and a reference genome A Short Primer on Common Terms
- 11. A Short Primeron Common Terms NGS: Next-Generation Sequencing. A group of different sequencing technologies defined by high throughput and low cost Short Read: The output from most NGS sequencing technologies. Range from 30bp to 300bp Mapping: Placing sequencing reads on to a reference genome Variant Calling: Identifying sites of genetic variation between a sample and a reference genome Paired End: Two short reads from the same fragment of the genome, one from each end
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- 16. Illumina Sequencing: TheBasics
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- 20. The Data: FastQFormat Read ID Sequence Quality line
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- 23. Unpacking the BlackBox Quality assurance of primary data
- 24. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference
- 25. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process
- 26. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process Identify genetic variation (mutations, translocations, etc)
- 27. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process Identify genetic variation (mutations, translocations, etc) Quality assurance of variants
- 28. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process Identify genetic variation (mutations, translocations, etc) Quality assurance of variants Variant annotation
- 29. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process Identify genetic variation (mutations, translocations, etc) Quality assurance of variants Variant annotation Variant filtering
- 30. Unpacking the BlackBox Quality assurance of primary data Map short reads to a reference Quality assurance of mapping process Identify genetic variation (mutations, translocations, etc) Quality assurance of variants Variant annotation Variant filtering Reporting (Text, Visualization)
- 31. Genomic Oncology Tumour Sample DNA Non-Tumour Sample DNA Databasesand Annotations Sequence Tumour Specific Mutations Tumour Classification Drugs
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- 33. Short Read Mapping AGCTGGGATTTCGGAAAAGTCCGATCCCTTTAAGCGAA AGCTGGGAT GATTTCGGAAAA TCGGAAAAGTCTTTAAGCGAA TCCCTTTAAG GTCCGATCCC GAAAAGTCCGATCC TGGGATTTCGG TTCGGAAAAG CGATCCCTTTAAGC AAAGTCCGATCC
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- 36. Variant Databases Public Databases ClinicalTesting Databases Pharmaceutical Company Databases
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- 38. Timeline to Action Day0 Day 30 ?
- 39. Timeline to Action Day0 Day 14?
- 40. Timeline to Action Day0 Day 7?
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- 57. Levels of Evidence/Support Algorithmic Prediction+Gene of Clinical Significance Algorithmic Prediction
- 58. Levels of Evidence/Support Algorithmic Prediction+Gene of Clinical Significance Algorithmic Prediction Clinical Variant in Other Malignancy
- 59. Levels of Evidence/Support Algorithmic Prediction+Gene of Clinical Significance Algorithmic Prediction Clinical Variant in Other Malignancy Clinical Variant in Malignancy
- 60. Algorithmic Prediction+ Geneof Clinical Significance Algorithmic Prediction Variants of Unknown Significance
- 61. Incidental Findings ACMGRecommendations 56 Genes Report on known pathogenic mutations for all Report on suspected (predicted) pathogenic for some Based on actionability Allow for patient opt-out?
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- 63. Monitoring For Cancer ChemotherapyResistance
- 64. Field Sequencing andReal-Time Analysis
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- 68. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge
- 69. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge Future proofing
- 70. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge Future proofing Be comfortable with genetics
- 71. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge Future proofing Be comfortable with genetics Make friends with your friendly local bioinformatician
- 72. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge Future proofing Be comfortable with genetics Make friends with your friendly local bioinformatician Leveraging 'Big Data' to make big decisions
- 73. Conclusions Clinical sequencingis here Bit of a learning curve but pay-off is potentially huge Future proofing Be comfortable with genetics Make friends with your friendly local bioinformatician Leveraging 'Big Data' to make big decisions Future: Clinical trails of size 1
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