Streamlining NGS data processing with GENALICE MAP - Remco Ursem (Rijk Zwaan)
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Rijk Zwaan develops vegetable varieties and sells the seeds they produce. Rijk Zwaan has over 1.000 varieties in 25 different vegetable crops. The company shares challenges of sequencing data and compares deployment of GENALICE MAP with BWA/GATK on storage footprint, analysis speed and shares first in house experiences. For more information on GENALICE MAP, please visit http://www.genalice.com/product/genalice-map/
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Streamlining NGS data processing with GENALICE MAP - Remco Ursem (Rijk Zwaan)
- 1. NGS data processing with GENALICE MAP Remco Ursem
- 2. Rijk Zwaan Biotechnology Research Facilities Fijnaart, The Netherlands
- 3. Outline • Rijk Zwaan • Sequencing data challenge • Comparing GENALICE MAP with BWA/GATK – Storage footprint – Analysis speed – First in house experiences • Wrap-up
- 4. • Since 1924 • Independent • 2,500 colleagues Family company
- 6. > 25 crops
- 7. Data deluge? • Re-sequencing larger number of accessions/lines • Reference available for growing amount of crops • Multiple references available per (sub)species • Fast evolving versioning of references
- 8. Yes! • Storage footprint grows exponential • Computational demand as well • Looking for a solution we came in contact with Genalice
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- 11. Cucumber alignment file size 27,9 GB 0,99 GB 28 X BAM GAR
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- 15. GAR file
- 16. Lossless compression? • No, but does it matter? • Probably not when we have clean high quality data.
- 17. Analysis
- 18. Variant calling pipeline • Based on BWA / GATK (Broad)
- 19. Conclusions • This variant calling pipeline does need serious hardware! • Not all steps can be easily parallelized. • Does not scale in our setup.
- 20. GENALICE Server • 24 cores • 128G RAM
- 21. Two test cases • Cucumber sample – 300 MB genome – No problems with old pipeline • Brassica sample – 600 MB genome – Unreliable calls detected
- 22. Elapsed time to Map/Call cucumber BWA/GATK GENALICE mapping 21:02 16 calling 2:24 1 total 23:26 17 83X
- 23. Comparing Cucumber SNP calling 0 100000 200000 300000 400000 500000 600000 700000 Total SNP's Overlapping Genotypes BWA/GATK GENALICE Higher quality SNPs overlap. GENALICE settings probably to stringent? BWA/GATK to loose? Both?
- 24. Comparing SNP zygosity 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Homozygous SNP's Heterezygous SNP's BWA/GATK GENALICE
- 25. Elapsed time to Map/Call Brassica BWA/GATK Genalice mapping 20:03 13 calling 1:18 1 total 21:21 14 92X
- 26. Comparing Brassica SNP calling 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000 4500000 Total SNP's Overlapping genotypes To stringent, but something else going on? BWA/GATK GENALICE
- 28. Comparing SNP zygosity 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Homozygous SNP's Heterozygous SNP's BWA/GATK GENALICE
- 29. Comparing with marker assay results 0 100 200 300 400 500 600 700 800 Failed Markers Passed markers BWA/GATK GENALICE
- 30. Conclusions • GENALICE makes it possible to do wide parameters sweeps. • This enables crop/assembly specific pipeline fine tuning .
- 31. Integration with other tools • BAM and VCF export functionality and GAR-API suite available. • IGV plugin works with GAR files.
- 33. More than SNPs • GENALICE pipeline calls INDELs up to 126 base pairs. • SV calling under development.
- 34. Wrap-up • GENALICE pipeline is very fast! • Feasible to rerun entire collection of resequenced lines (e.g. new assembly) • Higher quality variants found are overlapping with BWA/GATK results. • Fast mapping and calling makes it possible to do parameter sweeps. • This enables assembly or project specific pipeline fine tuning .
- 35. Wrap-up (continued) • GAR file is a very efficient format. • GAR plugin for IGV and GAR-API available. • INDELs called up to 126 bases. • Structural Variation detection is under development. • RNA-Seq mapping functionality available, not yet tested at Rijk Zwaan.