Presentation for IMPRS-CBSC 18 May 2015

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)
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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
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© EMBO Conference, 2014 [1]
© illumina Incorporation, 2011.[2]
Intensity
Image
Processing
Base
Calling
ReadAssemblingGenome

4. Challenge: Base Calling Errors Are Always
Compared
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© 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]

5. Fundamental Question:
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6. Our Approach: Analytical Assumptions and
Method
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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
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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
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• 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
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• 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
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• 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
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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
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14. Acknowledgement
Thank You for Your Patience, Time and Attention.
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