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Virus Hunting in French Guiana
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Virus Hunting in French Guiana

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Lab meeting presentation about my work doing viral metagenomics in French Guiana …

Lab meeting presentation about my work doing viral metagenomics in French Guiana

Rat by Francisca Arévalo from The Noun Project
Bat by Adam Heller from The Noun Project

Published in: Science, Business
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  • 1. French Guiana Virus Hunting in Nacho Caballero
  • 2. French Guiana
  • 3. Rodents Bats
  • 4. Rodents Bats Leishmania
  • 5. Capture
  • 6. Capture Isolate viral particles
  • 7. Capture Isolate viral particles Extract RNA
  • 8. Capture Isolate viral particles Extract RNA Sequence
  • 9. Estimated read coverage % reads with coverage smaller than x Rodents
  • 10. Estimated read coverage % reads with coverage smaller than x Rodents
  • 11. Estimated read coverage % reads with coverage smaller than x Rodents Bats
  • 12. Read How can we estimate the coverage without a reference genome?
  • 13. Read How can we estimate the coverage without a reference genome?
  • 14. K-mers Read How can we estimate the coverage without a reference genome?
  • 15. How can we estimate the coverage without a reference genome?
  • 16. 1 1 1 1 1 1 1 How can we estimate the coverage without a reference genome?
  • 17. 7 8 10 8 11 3 6
  • 18. 7 8 10 8 11 3 6 Median k-mer count ≈ Read coverage
  • 19. k-mers make it possible to align without a reference
  • 20. Problem: each sequencing error introduces k erroneous k-mers
  • 21. Problem: each sequencing error introduces k erroneous k-mers
  • 22. 7 8 10 8 11 3 6 Over a threshold, additional reads are redundant
  • 23. 5 5 5 5 5 3 5 Solution: digital normalization reduces redundancy and errors
  • 24. Assembly
  • 25. Assembly SPADes
  • 26. Assembly Alignment
  • 27. Assembly Alignment BLAST
  • 28. Assembly TaxonomyAlignment
  • 29. Assembly TaxonomyAlignment NCBI
  • 30. Problem: 67% of contigs in rodent dataset (serum) align to human sequences
  • 31. Problem: 67% of contigs in rodent dataset (serum) align to human sequences Night-heron coronavirus HKU19 (1 Kb) Simian hemorrhagic fever virus (300 bp) Equine arteritis virus (3.7 Kb) Possum nidovirus Rodent hepacivirus Chipmunk parvovirus Theiler's disease-associated virus Reticuloendotheliosis virus Mosquito VEM Anellovirus SDBVL A Porcine reproductive and respiratory syndrome virus Dragonfly-associated circular virus 1 Gemycircularvirus 3 Rodent pegivirus Cyclovirus PK5510 Hypericum japonicum associated circular DNA virus
  • 32. Pig stool associated circular ssDNA virus (1Kb) Avian gyrovirus 2 Torque teno sus virus 1a Mosquito VEM virus SDBVL G Turdivirus 3 Problem: 92% of contigs in bat dataset (droppings) don’t align to anything in NCBI
  • 33. Lymphocytic choriomeningitis virus (7kb) Hepatitis C virus Amphotropic murine leukemia virus Murid herpesvirus 1 Mosquito VEM Anellovirus SDBVL A Rat retrovirus SC1 Mason-Pfizer monkey virus (retrovirus) Eidolon helvum parvovirus 2 Periplaneta fuliginosa densovirus (also a parvovirus) Moloney murine sarcoma virus Sclerotinia sclerotiorum hypovirulence associated DNA virus 1 Problem: 95% of contigs in rodent dataset 2 (serum,spleen) align to mouse sequences (2)
  • 34. 7 out of 10 samples contained more than 1Kb of Leishmania RNA virus (94% ident) 5 Kb genome
  • 35. Lessons
  • 36. Assume that 50% of your samples are going to fail Lessons
  • 37. Assume that 50% of your samples are going to fail Lessons Design a small experiment, then iterate
  • 38. Assume that 50% of your samples are going to fail Lessons Design a small experiment, then iterate Come up with excuses to learn

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