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Comprehensive Guide to High-Throughput Sequencing Analysis Using IGV and Galaxy

This tutorial focuses on high-throughput sequencing (HTS) tools and analysis methodologies, emphasizing visualization with IGV and data management via the Galaxy platform. Learn how to effectively tune your pipelines by exploring base qualities, sample comparisons, and understanding mapping statistics. It covers crucial aspects such as false positives in indel detection, structural variations, and variant calling. Practical guidance on optimizing mapping parameters and filtration processes is provided, ensuring accurate analysis of genomic data, including gene expression studies.

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Comprehensive Guide to High-Throughput Sequencing Analysis Using IGV and Galaxy

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Presentation Transcript

  1. Tutorial 6 High Throughput Sequencing

  2. HTS tools and analysis • Visualization - IGV • Analysis platform – Galaxy • Tuning up the pipelines

  3. Working with IGV

  4. http://www.broadinstitute.org/igv/

  5. Why and how to work with IGV

  6. Base qualities, comparison between samples

  7. False positive indels

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

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

  10. One image is worth a thousand words…

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

  12. Galaxy

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

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

  15. Uploading data to Galaxy

  16. Mapping, filtering and conversion to BAM

  17. Mapping

  18. Filter SAM file

  19. Convert SAM to BAM

  20. Variant calling

  21. Create pileup

  22. Find variants

  23. Tuning up the pipelines

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

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

  26. 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. Gene expression: • Change the test threshold

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