DDP Stage 1 Presentation

Dual Degree Project Stage 1 Presentation

Transcript

1. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS Pattern Recognition In Clinical Data Saket Choudhary Dual Degree Project Guide: Prof. Santosh Noronha C G C A T C G A G C T C G C G T C G A G C T October 29, 2013
2. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS INTRODUCTION INTRODUCTION Objective SIGNIFICANT MUTATIONS Motivation Next Generation Sequencing Computational Methods for Driver Detection VIRAL GENOME DETECION Next Generation Sequencing REPRODUCIBILITY Reproducibility CONCLUSIONS Wrapping up
3. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
4. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
5. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
6. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
7. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
8. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
9. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
10. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
11. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
12. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
13. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
14. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
15. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS OBJECTIVE Next Generation Sequencing & Cancer Research Driver & Passenger Mutation Detection Literature Survey Galaxy Tools Galaxy Tools Viral Genome Integration Galaxy Workflow Reproducible Research Errors in Bio- informatics Galaxy Bench- marking Alignment tools BWA v/s BWA- PSSM
16. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS DRIVERS AND PASSENGERS I Who Cares? Cancer is known to arise due to mutations Not all mutations are equally important! Identify driver mutations −→ better therapeutic targets Somatic Mutations Set of mutations acquired after zygote formation, above the germline mutations Driver Mutations Mutations that confer growth advantages to the cell, being selected positively in the tumor tissue
17. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS DRIVERS AND PASSENGERS Drivers are NOT simply loss of function mutations, but more than that: Loss of function: Inactivate tumor suppressor proteins Gain of function: Activates normal genes transforming them to oncogenes Drug Resistance Mutations: Mutations that have evolved to overcome the inhibitory effect of drugs
18. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS DRIVERS AND PASSENGERS Drivers are NOT simply loss of function mutations, but more than that: Loss of function: Inactivate tumor suppressor proteins Gain of function: Activates normal genes transforming them to oncogenes Drug Resistance Mutations: Mutations that have evolved to overcome the inhibitory effect of drugs
19. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS DRIVERS AND PASSENGERS Drivers are NOT simply loss of function mutations, but more than that: Loss of function: Inactivate tumor suppressor proteins Gain of function: Activates normal genes transforming them to oncogenes Drug Resistance Mutations: Mutations that have evolved to overcome the inhibitory effect of drugs
20. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS NEXT GENERATION SEQUENCING C G C G T C G A G C T
21. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS DRIVERS AND PASSENGERS I The functional changes affecting a mutated protein sequence can be: Change in stability: Mutated protein might be unstable leading to lower steady state levels Change in interaction with other proteins,ligands: A mutated proteins interaction with other proteins/ligands is affected too Passenger mutations are neutral from the point of cancer cell fitness and hence an impact on protein can be present or absent
22. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS Figure: Driver mutation and evolution, Credits: Cancer Research UK
23. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS COMPUTATIONAL METHODS FOR DRIVER DETECTION Three approaches: Machine Learning: With knowledge of previous data, predict Functional Impact: Predict if the mutation can cause cell to proliferate Background Mutation rate: Different(higher) mutation rates in genes
24. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS COMPUTATIONAL METHODS FOR DRIVER DETECTION Three approaches: Machine Learning: With knowledge of previous data, predict Functional Impact: Predict if the mutation can cause cell to proliferate Background Mutation rate: Different(higher) mutation rates in genes
25. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS COMPUTATIONAL METHODS FOR DRIVER DETECTION Three approaches: Machine Learning: With knowledge of previous data, predict Functional Impact: Predict if the mutation can cause cell to proliferate Background Mutation rate: Different(higher) mutation rates in genes
26. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS MACHINE LEARNING I Two datasets: Training: Labeled dataset, containing a table of features with mutations labelled as ”drivers/passengers” Test: ’Learning’ from training dataset, test the prediction model Table: Training Dataset Chromosome Position Ref Alt Type 1 27822 A G Driver 1 27832 T G Driver 2 47842 G C Passenger . . . . . . . . . . . . . . .
27. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS MACHINE LEARNING II Table: Test Dataset Chromosome Position Ref Alt Type 1 27824 A G ? 1 47832 T G ?
28. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS FUNCTIONAL IMPACT I If a certain mutation confers an advantage to the cell in terms of replication rate, it is probably going to be selected while all those mutations that reduce its fitness have a higher chance of being eliminated from the population. Certain residues in a MSA of homologous sequences are more conserved than others. A highly conserved if mutated is possibly going to cost a lot since what had ’evolved’ is disturbed! Scores can be assigned based on this ”conservation” parameter.
29. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS FUNCTIONAL IMPACT II Figure: SIFT algorithm
30. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS ACCOUNTING FOR GERMLINE MUTATIONS I Effect of an amino acid substitution is ultimately on the functioning of the cell depending on the protein modification, which possibly confer a selective advantage to cancer cells for proliferation. Since all the nsSNVs that inhibit development have been eliminated by natural selection, the remaining nsSNVs in any gene define a ’baseline tolerance’ level that survive without affecting the cell fitness Genes can be clustered by annotating, for e.g all genes that regulate cell death These clusters can then be assigned a impact score by pooling in all the nsSNVs from curated databases
31. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS ACCOUNTING FOR GERMLINE MUTATIONS II A scaled impact score can be calculated, two mutations affecting the affecting two entirely different germline tolerance should result in a higher score for mutation affecting gene with low tolerance Low tolerance conserved nature
32. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS ACCOUNTING FOR GERMLINE MUTATIONS III Figure: TransFIC algorithm
33. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS FRAMEWORK FOR COMPARING VARIOUS TOOLS I Different tools use different formats, give different outputs for similar input Running analysis on multiple tools −→ keep shifting data formats Concordance? Polyphen2 Input chr1:888659 T/C chr1:1120431 G/A chr1:1387764 G/A chr1:1421991 G/A chr1:1599812 C/T chr1:1888193 C/A chr1:1900186 T/C
34. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS FRAMEWORK FOR COMPARING VARIOUS TOOLS II SIFT Input 1,888659,T,C 1,1120431,G,A 1,1387764,G,A 1,1421991,G,A 1,1599812,C,T 1,1888193,C,A 1,1900186,T,C Solution?: Galaxy, an open source web-based platform for bioinformatics, makes it possible to represent the entire data analysis pipeline in an intuitive graphical interface
35. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS FRAMEWORK FOR COMPARING VARIOUS TOOLS III Figure: Galaxy Workflow polyphen2 algorithm
36. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS Compare all tools in one go: Figure: Compare all tools
37. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS NGS I Figure: Sanger Sequencing
38. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS NGS II Figure: Shotgun Sequencing
39. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS
40. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS VIRAL GENOME DETECION Cervical cancers have been proven to be associated with Human Papillomavirus(HPV) Cervical cancer datasets from Indian women was put through an analysis to detect : 1. Any possible HPV integration 2. Sites of HPV integration Who Cares? Prognosis Replacing whole genome sequencing, by targeted sequencing at the sites where these virus have been detected in a cohort of samples, thus speeding up the whole process.
41. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS VIRAL GENOME DETECION Cervical cancers have been proven to be associated with Human Papillomavirus(HPV) Cervical cancer datasets from Indian women was put through an analysis to detect : 1. Any possible HPV integration 2. Sites of HPV integration Who Cares? Prognosis Replacing whole genome sequencing, by targeted sequencing at the sites where these virus have been detected in a cohort of samples, thus speeding up the whole process.
42. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS Figure: Detecting Virus Genomes
43. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS Figure: Aligned HPV genomes
44. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS REPRODUCIBILITY In pursuit of novel ’discovery’, standardizing the data analysis pipeline is often ignored, leading to dubious conclusions Analysis should be reproducible and above all, correct Parameter’s values can change the results by a big factor, they need to be documented/logged Garbage in, Garbage out
45. INTRODUCTION SIGNIFICANT MUTATIONS VIRAL GENOME DETECION REPRODUCIBILITY CONCLUSIONS CONCLUSIONS With the Galaxy tool box for identification of significant mutations and the study of the science behind the methods, the next steps would be to: Open source the toolbox to the community: A tool makes little sense if it is not in a usable form, community feedback will be used to add more tools and improve the existing ones A new method for driver mutation prediction: all the methods have low level of concordance. A new method that takes into account the available data at all levels : mutations, transcriptome and micro array data is possible. With the Galaxy toolbox in place, it would be possible to integrate information at various levels