Bioinformatics in Gene Research
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Intended for a mixed/general audience of Clinicians, Business Interests, and Research Scientists. No audio, however the event was recorded and posted to youtube by Genome Atlantic: http://www.youtube.com/watch?v=FLVjwOngu-Q I
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Bioinformatics in Gene Research
- 1. Bioinformatics in Genetics Research Genetics Noon Symposium Series Daniel Gaston, PhD Dr. Karen Bedard Lab, Department of Pathology November 21st, 2012
- 2. IGNITE Orphan Diseases: Identifying Genes and Novel Therapeutics to Enhance Treatment Identify causative genetic variations in orphan diseases with an emphasis on Atlantic Canada Develop animal and cell culture models Identify and develop novel therapeutics igniteproject.ca
- 3. IGNITE Orphan Diseases: Identifying Genes and Novel Therapeutics to Enhance Treatment Identify causative genetic variations in orphan diseases with an emphasis on Atlantic Canada Develop animal and cell culture models Identify and develop novel therapeutics igniteproject.ca
- 4. Outline Introduction Bioinformatics in Disease Genomics Next-Generation Sequencing Genomics in Research and the Clinic The Data Deluge and its Solutions Bioinformatic Methods for Analyzing Genomic Data Case Studies Conclusion
- 5. Bioinformatics in Disease Genomics Handling and long-term storage of raw data (sequencing, gene expression, etc) Maintenance and support of computational infrastructure Experimental design Data analysis Methods development Analysis pipelines Statistical analyses Algorithm design
- 6. Bioinformatics in Disease Genomics Handling and long-term storage of raw data (sequencing, gene expression, etc) Maintenance and support of computational infrastructure Experimental design Data analysis Methods development Analysis pipelines Statistical analysis techniques Algorithm design
- 7. ‘Next-Generation’ Sequencing and Disease Genomics
- 9. Disease Genomics: Hunting Down Pathogenic Genetic Variation Referenc Exon 1 Intron 1 Exon 2 e Start TAA Stop
- 10. Disease Genomics: Hunting Down Pathogenic Genetic Variation Splice Sites Referenc Exon 1 Intron 1 Exon 2 e Start TAA mRNA coding for protein Stop
- 11. Disease Genomics: Hunting Down Pathogenic Genetic Variation Splice Sites Referenc Exon 1 Intron 1 Exon 2 e Start TAA mRNA coding for protein Stop Patient Exon 1 Intron 1 Exon 2
- 12. Disease Genomics: Hunting Down Pathogenic Genetic Variation Splice Sites Referenc Exon 1 Intron 1 Exon 2 e Start TAA mRNA coding for protein Stop TAC Tyr Patient Exon 1 Intron 1 Exon 2
- 13. Disease Genomics: Hunting Down Pathogenic Genetic Variation Splice Sites Referenc Exon 1 Intron 1 Exon 2 e Start TAA mRNA coding for protein Stop TAC Tyr Patient Exon 1 Intron 1 Exon 2
- 14. Disease Genomics: Hunting Down Pathogenic Genetic Variation Splice Sites Referenc Exon 1 Intron 1 Exon 2 e Start TAA mRNA coding for protein Stop TAC Tyr Patient Exon 1 Intron 1 Exon 2
- 15. Disease Genomics: Research vs Clinic Still predominantly research oriented Complex/Common disease Mendelian disorders Cancer genomics
- 16. Disease Genomics: Research vs Clinic Still predominantly research oriented Complex/Common disease Mendelian disorders Cancer genomics Clinical genomics starting to gain traction Cancer genomics Cancer subtype identification Personalized medicine and predicting outcomes Mendelian disorders Early diagnosis Cost effectiveness
- 17. Clinical Genomics Children’s Mercy Hospital NICU In the US >20% of infant deaths due to genetic disease Serial sequencing of candidate genes too slow
- 18. Children’s Mercy Hospital NICU 50-hour differential diagnosis of monogenic disease Sample preparation and sequencing: 30.5 hours Automated bioinformatics analysis: 17.5 hours Previous high-throughput sequencing methods: 19 days Test on seven infants, two previously diagnosed using standard methods, five undiagnosed
- 19. Children’s Mercy Hospital NICU 50-hour differential diagnosis of monogenic disease Sample preparation and sequencing: 30.5 hours Automated bioinformatics analysis: 17.5 hours Previous high-throughput sequencing methods: 19 days Test on seven infants, two previously diagnosed using standard methods, five undiagnosed Caveats Bioinformatics portion not available outside of hospital Requires thorough clinical phenotyping using a controlled vocabulary Generates a large amount of data
- 20. The Data Deluge 4 million genetic variants 2 million associated with protein-coding genes 10,000 possibly of disease causing type 1500 <1% frequency in population
- 21. Surviving the Data Deluge Reducing the Search Space: Exome Sequencing
- 22. Exome Sequencing Exome: Portion of genome composed of protein- coding exons and functional RNA sequences 1.5 - 2% of human genome (50 Mb) > 85% of monogenic diseases due to variants in exome Complete exome sequencing: ~ $1000/sample
- 23. Caveats Incomplete and non-uniform coverage of exome Systematic bias (GC content) Random sampling Not all genetic variants amenable to discovery Non-coding variants Structural variants
- 24. Surviving The Data Deluge Bioinformatics
- 25. Typical Bioinformatics Workflow QC of Raw Data Map to Reference QC Find Variants QC Annotate Filter
- 26. It Sounds simple but… For every stage there are multiple programs available and published in the literature
- 27. It Sounds simple but… For every stage there are multiple programs available and published in the literature For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always
- 28. It Sounds simple but… For every stage there are multiple programs available and published in the literature For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always Best combinations of programs and options not well understood
- 29. It Sounds simple but… For every stage there are multiple programs available and published in the literature For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always Best combinations of programs and options not well understood Protocols changing rapidly as new technologies and methods developed
- 30. It Sounds simple but… For every stage there are multiple programs available and published in the literature For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always Best combinations of programs and options not well understood Protocols changing rapidly as new technologies and methods developed Different centres and groups use slightly different workflows with similar, but not identical results
- 31. Typical Bioinformatics Workflow QC of Raw Data Map to Reference QC Find Variants QC Annotate Filter
- 33. If a problem cannot be solved, enlarge it. --Dwight D. Eisenhower
- 34. Annotations Associated with Genomic Variants Is variant in a known protein-coding gene? What does the gene do? What molecular pathways? What protein-protein interactions? 4 million genetic variants What tissues is it expressed in? 2 million associated with protein-coding genes When in development? 10,000 possibly Has this variant been seen before? of disease causing type 1500 <1% What population(s)? With what frequency? frequency in population Has it been seen in local sequencing projects? Is there any known clinical significance? What is the effect of the variation? Does it change the resulting protein? How?
- 37. Potential Pitfalls with Annotation Sources Databases often overlap and agree, but there may be disagreements Source of information: Predicted versus experimental Incorrect and out-of-date information Large-scale un-validated versus manually curated datasets
- 38. Bioinformatics Analyses of Genomic Variants Combining Data Sources and Filtering
- 39. IGNITE Data Pipeline and Integration Gene Annotations Annotated Genomic Variants Mapped Gene Region(s) Definitions Filter Sort Prioritize Known Genes Pathway and Interactions
- 40. Filtering the Data: Categorization 4 million variants Intronic Exonic Intergenic Amino Acid Unknown Splice Site Silent Mutation Splice Site Changing Potential Potential Disease Disease Causing Causing Known Genetic Amino Acid Amino Acid Known Stop Loss / Disease Change Likely Change Likely Polymorphism Stop Gain Variant Pathogenic Benign in Population
- 41. Filtering the Data: Common or Rare? Variants in dbSNP – Typically known polymorphisms, unlikely to be associated with rare disease Variants with relatively high frequency in control populations (1000 Genomes, HapMAP, EVS, 2800 Exomes) Number of times variant previously seen at sequencing centre/locally
- 42. Notes on Filtering and Variant Annotation Very important to be aware of population when referencing frequency of a variant. Incorrect background leads to incorrect assumptions on prevalence
- 43. Notes on Filtering and Variant Annotation Very important to be aware of population when referencing frequency of a variant. Incorrect background leads to incorrect assumptions on prevalence Reasonably well-sampled local populations are better than any other reference
- 44. Notes on Filtering and Variant Annotation Very important to be aware of population when referencing frequency of a variant. Incorrect background leads to incorrect assumptions on prevalence Reasonably well-sampled local populations are better than any other reference Strike a balance between hard filtering for variants of largest potential effect and being inclusive to not miss variants
- 45. Notes on Filtering and Variant Annotation Very important to be aware of population when referencing frequency of a variant. Incorrect background leads to incorrect assumptions on prevalence Reasonably well-sampled local populations are better than any other reference Strike a balance between hard filtering for variants of largest potential effect and being inclusive to not miss variants Some genes acquire large effect variants (stop loss / stop gain, etc) frequently. Some genes can be lost without causing disease
- 46. Applications to Real Data Charcot-Marie-Tooth Disease and Cutis Laxa
- 47. IGNITE Data Pipeline and Integration Gene Annotations Annotated Genomic Variants Mapped Gene Region(s) Definitions Filter Sort Prioritize Known Genes Pathway and Interactions
- 48. Charcot-Marie-Tooth: Genetic Mapping Chromosome 9: 120,962,282 - 133,033,431
- 49. Cutis Laxa: Genetic Mapping Chromosome 17: 79,596,811- 81,041,077
- 50. Charcot-Marie-Tooth Cutis Laxa 143 genes in region 52 genes in region 13 known genes in 5 known genes in genome genome ATP6V0A2 MPZ ELN PMP22 FBLN5 GDAP1 EFEMP2 KIF1B SCYL1BP1 MFN2 ALDH18A1 SOX EGR2 DNM2 RAB7 LITAF (SIMPLE) GARS YARS LMNA
- 51. Pathway and Interaction Data 37 pathways 10 pathways Clathrin-derived vesicle Phagosome budding Collecting duct acid Lysosome vesicle secretion biogenesis Lysosome Endocytosis Protein digestion and Golgi-associated vesicle absorption biogenesis Metabolic pathways Membrane trafficking Oxidative Trans-Golgi network phosphorylation vesicle budding Arginine and proline Primarily LMNA or metabolism DNM2 Primarily ATP6V0A2
- 52. Results: Charcot-Marie-Tooth 8 Genes Prioritized Gene Interactions Pathway LRSAM1 Multiple Endocytosis DNM1 DNM2 - FNBP1 DNM2 - TOR1A MNA - STXBP1 Multiple Five SH3GLB2 - Endocytosis PIP5KL1 - Endocytosis FAM125B - Endocytosis For more information Guernsey et al (2010) PLoS Genetics. 6(8): e1001081
- 53. Results: Cutis Laxa 10 genes prioritized Gene Interactions Pathway HEXDC Multiple Phagosome HG5 - Phagosome HG5 Multiple Lysosome, Protein digestion SIRT7 Multiple Metabolic Pathways FASN - Metabolic Pathways DCXR - Metabolic Pathways PYCR1 - Metabolic Pathways, Arginine/Proline PCYT2 - Metabolic Pathways ARHGDIA - Oxidative Phosphorylation For more information Guernsey et al (2009) Am J Hum Genet. 85(1): 120-9
- 54. Conclusions
- 55. Conclusions Bioinformatics is involved at every stage of genomic research from experimental design through to final analysis Standards and best practices do exist, but are rapidly evolving as new technologies and methods are developed Progress towards automatic generation of clinically interpretable genomics studies Annotation, filtering, and prioritization of genetic variants crucial Balance between false positive calls and false negatives
- 56. Where Are We Headed? Integration of more data sources Gene expression More annotation sources Controlled phenotype vocabularies Gene Ontology terms Predictive models Recessive versus Dominant inheritance and Penetrance “New” and Emerging Technologies RNA-Seq (Gene Expression) ChIP-Seq (Protein-DNA binding) Single-Molecule Sequencing
- 57. Acknowledgements Dalhousie University McGill/Genome Quebec Dr. Karen Bedard Dr. Jacek Majewski Dr. Chris McMaster Jeremy Dr. Andrew Orr Schwartzentruber Dr. Conrad Fernandez Dr. Marissa Leblanc Dr. Sarah Dyack Mat Nightingale Dr. Johane Robataille Bedard Lab Genome Atlantic IGNITE