VIRANA: A Standardized Analysis of Viral Next Generation Sequencing - - PowerPoint PPT Presentation

VIRANA: A Standardized Analysis of Viral Next Generation Sequencing Data

Bastian Beggel Max-Planck-Institute for Informatics Saarbrücken

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Improvements in the rate of DNA sequencing

Source: Stratton et al., Nature 2009

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Reduction in the cost of DNA sequencing

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Result set of viral NGS data analysis

NGS datasets

position-wise

Pileup

haplotype level

60% ATATC…GATCG 20% ATATC…TATCG 10% ATATC…TATCG

read level

• HIV tropism
• Hypermutation
• Dual infections

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Standardized processing of next-generation sequencing data

Quality Control

• Withdraw/ clip bad

quality reads Map to reference

• Select reference
• Solve alignment

problem

Pre-processing Standardized Analysis

Coverage

• Number of mapped

reads per position Pileups/ Dynamics

• Summarize data

position-wise

• Analysis of changes

Haplotypes

• Raw output
• Visualization
• Complexity

Custom Analysis

HIV Tropism

• g2p[454]

Hypermutation

• Classify reads as

hypermutated Statistics

• Correlate NGS data

with clinical parameters

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VIRANA as a Web-Service Upload sequence data Download summary statistics and plots

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Pileups summarize NGS data position-wise ID Pos

• Ref. NT

G A T C Cov. A_BL 1 T 0.1% 32.9% 66.8% 0.1% 8300 A_BL 2 G 99.6% 0.2% 0.0% 0.0% 8305 A_BL 3 T 0.1% 0.0% 99.4% 0.4% 8331 A_BL 4 T 0.0% 0.0% 99.7% 0.1% 8334 A_BL 5 G 98.4% 0.6% 0.1% 0.0% 8338

LoFreq (Wilm et al., 2012)

• Modeling biases in sequencing error rates
• Uses base-specific quality scores (phred scores)
• Poisson–binomial distribution

Error model

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Dynamic pileups to analyze changes over time ID1 ID1 Pos Ref. NT dG dA dT dC mCov. A B 1 T 0.0% 0.0% 0.1% -0.1% 8300 A B 2 G

• 0.1% 0.0% -0.1% 0.2% 8305

A B 3 T 0.0% 0.0% -4.5% 4.5% 8331 A B 4 T 0.0% 0.0% 0.0% 0.0% 8334 A B 5 G

• 0.1% 0.0% -0.1% 0.2% 8338

deepSVN (Gerstung et al., 2012)

• Compares two similar samples
• Adapts error rates to genomic context
• Hierarchical binomial model (overdispersed)

Error model

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Median and sample coverage

Coverage Coverage NT position NT position

Median Coverage Single Sample Coverage

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Monitoring of resistance mutations

Sample time NGS frequency 181T Prevalence of resistance mutations at baseline Dynamics of resistance mutations NGS frequency

BL 12M Patient

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Monitoring genetic change

AA

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Quasispecies estimation using ShoRAH

Source: Zargordi et al. 2011

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Visualization of the viral quasispecies

Principal Component 2 Principal Component 1

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Conclusions

• State-of-the-art processing of viral NGS data
• HBV, HCV, HIV
• Summary plots and statistics
• Coverage
• Pileups, Dynamics
• Resistance mutation
• Quasispecies
• Fully automated
• Web-based version in planning
• Looking forward to more collaborations

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Thank you for your attention

Acknowledgements

• Thomas Lengauer
• Alex Thielen
• Rolf Kaiser
• Martin Däumer
• Sven-Eric Schelhorn

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End