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Progress report

Progress report. 9.17 PJ Lab. Cross-project. TCGC/ICGC database. "CMDI-UK" "LIHM-FR" "LUSC-CN" "RECA-CN". "PAAD-US". Clinical plot. gridExtra::arrangeGrob(emptyPlot,emptyPlot,plotDD,plotD , ncol= 3 , widths=c(.20,.0 5 ,.80)).

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Progress report

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  1. Progress report 9.17 PJ Lab

  2. Cross-project

  3. TCGC/ICGC database "CMDI-UK" "LIHM-FR" "LUSC-CN" "RECA-CN" "PAAD-US"

  4. Clinical plot

  5. gridExtra::arrangeGrob(emptyPlot,emptyPlot,plotDD,plotD , ncol=3, widths=c(.20,.05,.80)) gridExtra::arrangeGrob(emptyPlot,emptyPlot,plotDD,plotD , ncol=3, widths=c(.05,.20,.80))

  6. Enrichplot

  7. barplot(ego2, showCategory= 6, split='ONTOLOGY', font.size = 8,horiz = FALSE) + facet_grid(.~ONTOLOGY, scale='free') +scale_colour_gradient2(low = "red", mid = "white", high = "blue", midpoint = 7.562602e-05)

  8. To do list • TCGA/ICGC project plot • Enrichplot

  9. Progress report 10.01 PJ Lab

  10. E.L.K

  11. pVac-seq

  12. Question Oncotator or VEP pVac-seq Change the annovar Refseq Use netMHC anlysis

  13. Future work • E.L.K • pVac-seq • Gromacs

  14. Progress report 11.26 PJ Lab

  15. pVac-seq

  16. test file output all_epitopes.tsv Example file output all_epitopes.tsv

  17. pVac-seq output pvacseq run --iedb-install-directory /home/jacky831006/ -e 11 ~/vep_data/VCF/test2.vcf test HLA-A*01:01,HLA-A*01:02 NetMHC ~/pvac_test/test3

  18. test.all_epitopes.tsv Mutation Position MT Epitope Seq WT Epitope Seq Best MT Score Method HLA-A*01:01 11 3 9 KSLPGGLDTVV KSLPGGLDAVV NetMHC 31067.37 31004.92 0.998 Best MT Score Corresponding WT Score Corresponding Fold Change

  19. pVac-seq • Binding filter • Coverage filter • Transcript support level filter • Top score filter

  20. Transcript support level filter The GENCODE TSL provides a consistent method of evaluating the level of support that a GENCODE transcript annotation is actually expressed in humans.

  21. Question Expression data Coverage data

  22. Creating a phased VCF of proximal variants By default, pVACseq will evaluate all somatic variants in the input VCF in isolation. As a result, if a somatic variant of interest has other somatic or germline variants in proximity, the calculated wildtype and mutant protein sequencesmight be incorrect because the amino acid changes of those proximal variants were not taken into account.

  23. Future work • pVac-seq • hail

  24. Progress report 2018.12.10 PJ Lab

  25. pVac-seq(1.0.7) test.combined.parsed.tsv Mutation Position MT Epitope Seq WT Epitope Seq Best MT Score Method HLA-A*01:01 11 3 9 KSLPGGLDTVV KSLPGGLDAVV NetMHC 31067.37 31004.92 0.998 Best MT Score Corresponding WT Score Corresponding Fold Change

  26. NetMHC 78 HLA allele sequences

  27. The tables below show the allele-specific thresholds for the 38 most common HLA-A and HLA-B alleles, representative of the nine major supertypes.  The tables can also be downloaded as an RTF file (see attached file)

  28. Different threshold pvacseq 1.1.5

  29. pvacseq run --iedb-install-directory /home/jacky831006/ -e 11 ~/vep_data/VCF/ACC-US.vcf test HLA-A*01:01,HLA-A*02:01,HLA-A*02:02,HLA-A*02:03,HLA-A*02:06 NetMHC ~/pvac_test/ACC-US 5 alleles 1000 mutations 21mins ACC 19000 -> 37000 mutations *10 * 73 Total 37*21*10*73*78/5 mins=147474.6 hrs = 6144.775 days

  30. Other Algorithm pvacseq 1.1.5

  31. NetMHC • Total 37*21*10*73*78/5 mins = 147474.6 hrs = 6144.775 days MHCflurry • Total 1.5hrs *10*73*80/5 mins = 730 days

  32. Question nohup pvacseq run --iedb-install-directory /home/jacky831006/ -e 11 ~/vep_data/VCF/ACC-US.vcf test HLA-A*01:01,HLA-A*02:01,HLA-A*02:02,HLA-A*02:03,HLA-A*02:06 NetMHC ~/pvac_test/ACC-US &

  33. Progress report 2018.12.24 PJ Lab

  34. Test tensorflow Tensorflow -> Tensorflow(GPU)

  35. Test pvac-seq (MHCflurry)

  36. Question Time 1.5hr2hr up

  37. MHCflurry

  38. Question

  39. Website

  40. Allele WT binding affinity MT binding affinity Fold Change Protein structure

  41. Future work • Run MHCflurry by gpu • Remove the duplicate mutation in all projects

  42. Progress report 2019.1.07 PJ Lab

  43. pVac-seq (GPU)

  44. Speedup suggestion • Number of variants in your VCF Split the VCF into smaller subsets and process each one individually, in parallel. • Number of transcripts for each variant Use the --pickoption when running VEP to annotate each variant with the top transcript only. • The --fasta-sizeparameter value When using a local IEDB install, increase the size of this parameter. • Number of prediction algorithms, epitope lengths, and HLA-alleles • --downstream-sequence-lengthparameter value Reduce the value of this parameter.

  45. VCF processing • bcftools • vcftools

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