Base Calling Error Toleration in Reference Base Assembly
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This document discusses base calling error toleration in reference based genome assembly. It hypothesizes that base calling errors are compensated for in downstream sequencing steps like mapping and variant calling. Through analytical assumptions and simulations using E. coli genome data, it finds that variant calling error increases with base calling error but the relationship depends on factors like mismatches, read length and genome size. The simulations verify the analytical predictions and show that repeat regions in genomes can impair accuracy, but using a random genome obviates this effect. Next steps include exploring different genome structures, variant types and sequencing methods.
![Importance: Base Calling Translates Noisy
Intensity Data Into Reads
3
© EMBO Conference, 2014 [1]
© illumina Incorporation, 2011.[2]
Intensity
Image
Processing
Base
Calling
ReadAssemblingGenome](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-3-320.jpg)
![Challenge: Base Calling Errors Are Always
Compared
4
© C. Ye, 2014 [3]
Figure: Error rate for base callers
per sequencing cycle on the
PhiX174 test data is plotted.
Accurate callers are slower than the
others. [3]](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-4-320.jpg)
![Simulation Method and Setup
8
• Generate Target Genome
• Simulate Reads [4]
• Add Base Calling Error
• Call Variants
• Calculate Variant Calling Error
Method Setup
© Gemsim, 2013[4]](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-8-320.jpg)
![Simulation Results: Simulation Verifies Analysis
Predictions
9
• E-Coli Genome [5]
• Mismatches= {3, 4, 5}
• Genome Size ~ 4Mbp
• Read Length= 30bp
• Variation Rate~ 0.01
• Single-end Shotgun Run
• Map with SOAP[6]
Figure: Variant Calling Error Vs.
Base Calling Error
© NCBI, 2014[5]
© G. BGI, 2008[6]](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-9-320.jpg)
![Simulation Results: Random Genome Obviates
Repeat Region Effect
10
• Genome Sizes ~ 4Mbp
• Mismatches= 3
• Read Length= 30bp
• Variation Rate~ 0.01
• Single-end Shotgun Run
• Map with SOAP[6]
Figure: Random Genome Vs.
E-Coli Genome
© G. BGI, 2008[6]](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-10-320.jpg)
![References
[1] EMBO Conference, “Human Evolution in the Genomic Era: Origins, Populations, and
Phenotypes,” 2014, [Online]. Available: events.embo.org/14-human-evo
[2] Illumina Inc., “Theory of Operation, HCS 1.4/RTA 1.12”,2011.
[3] C. Ye, C. Hsiao, and H. Corrada Bravo, “BlindCall: ultra-fast base-calling of high-
throughput sequencing data by blind deconvolution,” Bioinformatics, 30(9), 1214–1219,
2014.
[4] C. Ledergerber and C. Dessimoz, “Base-calling for next-generation sequencing
platforms”, Briefings in Bioinformatics, 2011.
[5] GemSIM, “Gemsim,” 2013. [Online]. Available:
http://sourceforge.net/projects/gemsim
[6] NCBI, “Escherichia coli o157:h7 str. sakai dna, complete genome - nucleotide - ncbi,”
2014. [Online]. Available:
http://www.ncbi.nlm.nih.gov/nuccore/47118301?report=fasta
[7] G. BGI, “Soap: Short oligonucleotide analysis package,” 2008. [Online]. Available:
http://soap.genomics.org.cn
13](https://image.slidesharecdn.com/hadigharibiiran-150520083111-lva1-app6892/85/Base-Calling-Error-Toleration-in-Reference-Base-Assembly-13-320.jpg)
Base Calling Error Toleration in Reference Base Assembly
- 1. Base Calling Error Toleration in Reference Based Assembly Hadi Gharibi Email: h_gharibi@ee.sharif.edu Sharif University of Technology Max Planck Institute for Molecular Genetics May 2015
- 2. How Base Calling Error Can Be Tolerated in Next Generation Sequencing (NGS) 2 Importance Challenges Our Hypothesis Our Approach • Deal with Large Amount of Data • Impact on Sequencing Data Analysis Time and Accuracy Researchers have developed many base calling algorithms, however, they have not resolved the tradeoff between accuracy and time complexity. • Required Accuracy • Sequencing Data Analysis Execution Time Base Calling Error Is Compensated in Down-stream Sequencing Steps • Massive Data • Diverse Algorithms
- 3. Importance: Base Calling Translates Noisy Intensity Data Into Reads 3 © EMBO Conference, 2014 [1] © illumina Incorporation, 2011.[2] Intensity Image Processing Base Calling ReadAssemblingGenome
- 4. Challenge: Base Calling Errors Are Always Compared 4 © C. Ye, 2014 [3] Figure: Error rate for base callers per sequencing cycle on the PhiX174 test data is plotted. Accurate callers are slower than the others. [3]
- 6. Our Approach: Analytical Assumptions and Method 6 Assumptions • Random Genome • Single Variations • Mismatches << Read Length • Uniform Substitution Error • Equally Likely Base Errors Method • Variant Calling for Re-sequencing • Derive Variant Calling Errors
- 7. Analytical Results: Base Calling Error Is Tolerated by Mapping Mismatch 7 Figure: Variant Calling Error Vs. Base Calling Error Random Genome Mismatches={2, 5, 7, 9} Genome Size ~ 4Mbp Read Length= 30bp Variation Rate= 0.01
- 8. Simulation Method and Setup 8 • Generate Target Genome • Simulate Reads [4] • Add Base Calling Error • Call Variants • Calculate Variant Calling Error Method Setup © Gemsim, 2013[4]
- 9. Simulation Results: Simulation Verifies Analysis Predictions 9 • E-Coli Genome [5] • Mismatches= {3, 4, 5} • Genome Size ~ 4Mbp • Read Length= 30bp • Variation Rate~ 0.01 • Single-end Shotgun Run • Map with SOAP[6] Figure: Variant Calling Error Vs. Base Calling Error © NCBI, 2014[5] © G. BGI, 2008[6]
- 10. Simulation Results: Random Genome Obviates Repeat Region Effect 10 • Genome Sizes ~ 4Mbp • Mismatches= 3 • Read Length= 30bp • Variation Rate~ 0.01 • Single-end Shotgun Run • Map with SOAP[6] Figure: Random Genome Vs. E-Coli Genome © G. BGI, 2008[6]
- 11. 11 Conclusion Simulation Results • Confirm the Hypothesis • Genome Repeat Regions Impair Accuracy • Confirm the Hypothesis • Higher Mismatches May Not Obey Analytical Results
- 12. Next Steps 12 Simulation Steps • Genome Having More Repeat Regions • Develop Mapper with Higher Mismatches • Genome Structure • Paired-end Shotgun Sequencing • Erasure Base Calling Error • Other Variant Types Analytical Steps
- 13. References [1] EMBO Conference, “Human Evolution in the Genomic Era: Origins, Populations, and Phenotypes,” 2014, [Online]. Available: events.embo.org/14-human-evo [2] Illumina Inc., “Theory of Operation, HCS 1.4/RTA 1.12”,2011. [3] C. Ye, C. Hsiao, and H. Corrada Bravo, “BlindCall: ultra-fast base-calling of high- throughput sequencing data by blind deconvolution,” Bioinformatics, 30(9), 1214–1219, 2014. [4] C. Ledergerber and C. Dessimoz, “Base-calling for next-generation sequencing platforms”, Briefings in Bioinformatics, 2011. [5] GemSIM, “Gemsim,” 2013. [Online]. Available: http://sourceforge.net/projects/gemsim [6] NCBI, “Escherichia coli o157:h7 str. sakai dna, complete genome - nucleotide - ncbi,” 2014. [Online]. Available: http://www.ncbi.nlm.nih.gov/nuccore/47118301?report=fasta [7] G. BGI, “Soap: Short oligonucleotide analysis package,” 2008. [Online]. Available: http://soap.genomics.org.cn 13
- 14. Acknowledgement Thank You for Your Patience, Time and Attention. 14