1. Tutorial 6 High Throughput Sequencing

2. HTS tools and analysis Review of resequencing pipeline Visualization - IGV Analysis platform – Galaxy Tuning up the pipelines

3. Review of resequencing pipeline

4. Demultiplexing Lane Unknown inserts

5. Reference Genome Sample Mapping Demultiplexing Example of mapping parameters: Number of mismatches per read Scores for mismatch or gaps Mapping parameters affect the rest of the analysis

6. Removing duplicates and non-unique mappings Mapping Demultiplexing Reference Genome ?

7. Resequencing/ Exome Pipeline

8. Coverage profile and variant calling Removing duplicates and non-unique mappings Mapping Demultiplexing Reference Genome …ACTTCGTCGAAAGG… G

9. Coverage profile and variant calling Removing duplicates and non-unique mappings Mapping Demultiplexing Variant filtering Reference Genome …ACTTCGTCGAAAGG… Reference Genome …ACTTCGTCGAAAGG… Frequency >= 20% Coverage >= 5

10. Variant calling Removing duplicates and non-unique mappings Mapping Demultiplexing Variant filtering Genes and known variants Reference Genome …ACTTCGTCGAAATG… …GTCCCGTGATACTCCGT… G A rs230985 Gene X

11. Resequencing results

12. Working with IGV http://www.broadinstitute.org/igv/

13. Integrative Genome Viewer IGV is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.

14. Genome used for mapping Name of sample (BAM file) Lowest resolution of the genome (zoomed out)

15. Zooming in

16. Coverage track Alignment track

17. Zoomed in until we get to the base pair value SNP

18. Hover over the coverage track in order to see details regarding all bases in a specific position Can we trust this SNP?

19. Hover over the alignment track in order to see details regarding a specific read What is the quality of this read and its mapping?

20. Right-click on alignment track to change view of this track

21. Color reads by strand to verify there is no strand bias

22. Why and how to work with IGV

23. Base qualities, comparison between samples

24. False positive indels

25. Same mapping statistics – different meaning What might cause this low percentage of mapping?

26. The sample contains a high percentage of contamination The sample is very different from the reference genome

27. One image is worth a thousand words…

28. Structural Variations Large deletion in the sample compared to the reference genome

29. Galaxy

30. https://main.g2.bx.psu.edu/

31. Use your account name and password to login to Galaxy:

32. Uploading data to Galaxy

37. Use the “eye” icon to view the contents of a file

38. Mapping, filtering and conversion to BAM

39. Mapping

40. Filter SAM file

41. Convert SAM to BAM

42. Variant calling

43. Create pileup

44. Find variants

45. Tuning up the pipelines

46. 1 mismatch per read 5 mismatches per read How can mapping parameters affect the results

47. False positives vs. true negatives 3-bases insertion One pipeline for all projects?

48. How can you tune your analysis? Try different programs. Mapping: – Change mapping parameters – Use non-unique mappings – Don’t filter duplicates Variants: – Change variant filtration – Change variant merging – penetrance, different heredity, low coverage in one individual… – Look for bigger variants: big insertions/ deletions, inversions, copy number variations etc.