1. Statistical methods for improved variant calling of massively parallel sequencing data.
VirVarSeq vs ViVaMBC
Pictured above: The structure of HIV.
Bie Verbist | NCS Brugge |

2. OUTLINE
Viral dynamics
Massive parallel sequencing
Variant calling
VirVarSeq
ViVaMBC
Results
HCV plasmids
HCV clinical sample

3. Viral dynamics
A virus is a small infectious agent that replicates only inside the living cells of other organisms.
High replication rate
(1011 replications a day for HIV)
High mutation rate
Viral population consist of closely related subgroups, viral quasispecies, which we want to identify and quantify.

4. Viral dynamics Number of virusus in population Time
Drug-sensitive variants
Drug-resistant variant
Number of virusus in population
Heterogeneous viral population
Undetectable
Before treatment
On treatment
Time

5. Sequencing Sanger sequencing detection limit: 20-30%
no accurate estimate of frequency
Massively parallel
sequencing
ACGGTTTCCGTCTGGG
ACGGTTTCTGTCTGGG
ACGGTTTCCGTCTGGG
ACGGTTTCTGTCTGGG
ACGGTTTCTGTCTGGG
ACGGTTTCTGTCTGGG
ACGGTTTCTGTCTGGG
ACGATTTCTGTCTGGG
detection limit << 20%
more accurate estimate of frequency

6. Massively parallel sequencing
Fragmentation
Amplification
Viral population
DNA Fragments
Sequencing
by synthesis
Example, one fragment: T G C C A A A G A C G G T T T C T

7. Massively parallel sequencing
Viral population
@HWUSI-EAS1524:17:FC:1:120:19254: :N:0:GATCAG
GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTGAAAAAAA +
@HWUSI-EAS1524:17:FC:1:120:9430: :N:0:GATCAG
ATCGGAAGAGCACACGNCTGAACTCCAGTCACGATCAGATCCCGTATGCCGTCTTCTGCTTGAAAAAAAA
@HWUSI-EAS1524:17:FC:1:120:12760: :N:0:GATCAG
ATCATACTGTCTTACTNTGATAAAACCTCCAATTCCCCCTANCATTNTTGGTTNCCATCTTCCTTGCAAA

8. Distinguish low-frequency variants
Variant calling
Distinguish low-frequency variants
from sequencing error.
VirVarSeq
ViVaMBC
Adaptive filtering approach based on quality scores.
Verbist et al. 2014, Bioinformatics. doi: / bioinformatics/btu587.
Model based clustering approach which models the
error probabilities based on quality scores.
Verbist et al. 2014, BMC bioinformatics. under revision.

9. VirVarSeq Extract reads that cover codon of interest
Filter based on the quality scores.
Build a codon table
Reference
...
...
...
...
Reads
...
CGA
CCA
CGT
GGA
CGA
CCA
CGT
GGA
...
Pos x
Codon
Freq
CGA
0.62
CCA
0.25
GGA
0.13
...
...
...
...
...
Filtering
Codon Table
...
...
...
...
...
...
...
...
...
* codon = nucleotide triplets which specifies a single amino acid

10. Image or graphic goes here
VirVarSeq
Definition of the Q-threshold (QIT) :
Fit mixture distribution on Q-scores with 3 components:
Point prob around Q 2
Error distribution
Reliable call distribution
Intersection point is threshold.
QIT
Image or graphic goes here

11. ViVaMBC Extract reads that cover codon of interest
Perform Model Based Clustering
Model the error probability
Clusters unknown, EM algorithm
Reference
...
...
Reads
...
CGA
CCA
CGT
GGA
...
Pos x
Codon
Freq
CGA
0.62
CCA
0.25
GGA
0.13
...
...
CCA
...
...
CCA
GGA
...
Clustering
Codon Table
CCA
...
CGT
...
...
CGA
...
...
CGA
CGA
...
CGA
CGA
...
CGA
...
CGA
...
Cluster medoids = variant
Size of Cluster = Frequency
N° Clusters = N° variants

12. Results – HCV plasmids Two plasmids Amino acids 1 to 181 of NS3 region
differ at two codon positions (36 and 155)
mixed 4 different proportions

13. Results – HCV plasmids Other variants (11481 max) are false positives.
VirVarSeq reports:
more false positives
with frequencies going up to 0,5%

14. Results - HCV clinical sample
VirVarSeq reports more variants. Above 1% methods in agreement, even above 0.5%. Few false pos in GC region for ViVaMBC ?
Image or graphic goes here
VirVarSeq
ViVaMBC

15. VirVarSeq vs ViVaMBC
When applying reporting limits of 1% or 0.5%, methods are in agreement.
Below this limit, trade-off between sensitivity and specificity, with VirVarSeq less specific.
VirVarSeq
Adaptive approach
Easy development
Runs fast
ViVaMBC
More elegant
Longer development time
Longer run time

16. Acknowledgements
Promoters: Prof.Dr.O.Thas1, Prof.Dr.L.Clement1 and Prof.Dr.L.Bijnens2
Yves Wetzels, Tobias Verbeke, Joris Meys1 for IT support
Scientists within discovery sciences2
Non-clinical statistics team2 2 2 1

17. Thank you

18. Back-up

19. ViVaMBC Notation: Complete Data Likelihood:
ri: best base calls of read i (i=1 ... n)
si: second best base calls of read i (i=1 ... n)
zij: zij=1 when read i belongs to haplotype j (j=1...k)
τj: probability to belong to haplotype j
Complete Data Likelihood:

20. ViVaMBC Complete Data Likelihood:
Likelihood depends on cluster membership zij
 EM algorithm

21. Library preparation
Sequencing by synthesis