What is the impact of assay failure in your laboratory and how do you monitor for it? The most heavily degraded samples are not suitable for standard exome coverage: sometimes it’s not even a matter of getting bad sequencing, you might get nothing at all! FFPE artifacts increase with storage time Artifacts go against the statistical power of your variant calling analysis Molecular reference standards help filter out bad mappings and spurious variants Bioinformatics pipelines allow adding Molecular Reference Standards in your joint variant calling pipeline Genome In A Bottle Reference Standards are invaluable for validating variant calling analysis NIST and its collaborators shared datasets created with most NGS technologies Horizon Diagnostics shared annotated, merged variant calls from NIST for the Ashkenazim Trio ~35K variants are predicted having high or moderate impact within the Trio GM24385 (Ashkenazim Son) includes 352 small variants with high/moderate impact which are absent in Father and Mother Routinely monitor the performance of your workflows and assays with independent external controls

1. HORIZON DIAGNOSTICS
Molecular QC: Interpreting your
Bioinformatics Pipeline
25th June 2015
Dr. Danielle Folkard and Dr. Alessandro Riccombeni

2. 2
What is the impact of assay failure in
your laboratory and how do you
monitor for it?
Research Use Only

3. 3
External Quality Assessment
0
5
10
15
20
25
30
35
40
PercentageofIncorrectResults
EGFR Sample Tested
EGFR Genotyping Errors
External Quality Assessment 2014
European Molecular Quality Network (EMQN)
Research Use Only

4. 4
Clinical Application of Next Generation Sequencing
Using just one sample, one workflow can test for mutation status across multiple genes
Research Use Only

5. 5
The Sources of Variability in the Next Generation
Sequencing Workflow
Research Use Only

6. 6
The Sources of Variability in the Next Generation
Sequencing Workflow
Research Use Only

7. 7
Influence of Analytical Pipelines
Reference: Genome in a Bottle Consortium
Research Use Only

8. 8
Introduction
2016
Four decades, three generations
1976
Maxam-Gilbert
1977 Sanger
φX174 genome
1983 PCR 1990 HGP starts (3B $)
Pyrosequencing
2003 First human
genome sequence
1987 First automated
sequencer
ABI 370
2000 454 LS Corp.
2016 454 ends1998 Solexa
2006 GA
SOLiD
2005 Roche 454
1976
2008 RNA-Seq
Helicos
2011 Ion Torrent
MiSeq
2012 Helicos ends
2009 PacBio
ON
CG
LEGEND
First generation
Second generation (NGS)
Third generation
2014
HiSeqX
Research Use Only

9. 9
Next-Generation Sequencing for Clinical Bioinformatics
• NGS revolutionised our access to genomic information
• 2nd generation technology allows WGS for less than 1000 GBP
• However, a number of challenges exist
• Data creation
• Data analysis/processing
• Data (clinical) interpretation
Research Use Only

10. 10
FFPE and NGS
• Why FFPE?
• FFPE has been used for a number of NGS applications
• Tuononen, Spencer (targeted resequencing)
• Fanelli, Gu (ChIP-seq, RRBS)
• Weng, Meng (RNA-Seq, miRNA)
• What happens with fixatives?
• Need to counteract protein-DNA interactions
• Additional effects from tissue preparation,
paraffin embedding, x-linking, chemical
modification of tissues
• Lower DNA yield, DNA degradation, smaller
fragments
• How does FFPE affect NGS pipelines?
Spencer et al. 2013
Research Use Only

11. 11
FFPE and NGS
• Schweiger 2009, comparison of FFPE and Fresh Frozen (FF) tissues for Illumina
sequencing:
• Fixation time does not significantly affect the quality of sequencing data from FFPE
• Lower mappability
• Higher mutation rate
• Lower fraction of known SNVs
• Van Allen 2014: good correlation between FFPE and FF samples
Van Allen 2014
Research Use Only

12. 12
FFPE and NGS
• Hedegaard 2014: 3 months storage
resulted in less efficient DNA extraction
• High fragmentation: loss of material
• Decrease in library complexity
• High increase in PCR duplicates, 60-
85% for FFPE vs. 30% for FF
• C > U deamination is a common cause of
artifacts
• U-tolerant polymerase didn’t help
• Pattern, T <> C, A <> G transition
• The fraction of mapped reads decreases
with storage time
• Increase in partial mappings
• Increase in gapped mappings
Hedegaard et al. 2014
Wong et al. 2014
Research Use Only

13. 13
FFPE and NGS
C
T
T
T
T
T
C
T
T
T
T
T
New mismatches: artifact variants
C
T
T
T
Lost mappings
Partial mappings
T
T
T
T
T
Artifacts include:
• SNVs
• Larger indels
• CNV
Research Use Only

14. 14
FFPE: Conclusions
• FFPE artifacts increase with storage time
• Artifacts go against the statistical power of your variant calling analysis
• Molecular reference standards help filter out bad mappings and spurious variants
• Bioinformatics pipelines allow adding Molecular Reference Standards in your joint
variant calling pipeline
Research Use Only

15. 15
Upcoming Webinar
Title:
Understanding and Controlling for Sample and Platform Biases in
NGS Assays
Date:
Wednesday 22nd July 2015
Time:
4:00pm BST, 11:00am EST
Register now: www.horizondx.com/upcomingwebinar
Research Use Only

16. 16
Genome in a Bottle
Infrastructure for performance
assessment of NGS
No widely accepted set of
metrics to characterize the
fidelity of variant calls from NGS
GIAB is developing standards to provide well
characterized human genomes as Reference Materials
Tools and standardized methods to use these RMs
Research Use Only

17. 17
Horizon Diagnostics: Ashkenazim Trio FFPE Reference Standards
• GM24385 – Ashkenazim PGP Son
• Coriell: NA24385
• NIST: HG002
• PGP: huAA53E0
• GM24149 – Ashkenazim PGP Father
• Coriell: NA24149
• NIST: HG003
• PGP: hu6E4515
• GM24143 – Ashkenazim PGP Mother
• Coriell: NA24143
• NIST: HG004
• PGP: hu8E87A9
Research Use Only

18. 18
Horizon Diagnostics: Ashkenazim Trio FFPE Reference Standards
• GM24385 – Ashkenazim PGP Son
• GM24149 – Ashkenazim PGP Father
• GM24143 – Ashkenazim PGP Mother
• Complete Genomics:
• Small variants (SNPs & Indels)
• Copy Number Variants
• Structural Variants
• Mobile Element Insertions
• National Institute of Standards and
Technology (NIST):
• Illumina HiSeq, BWA + GATK 1.6
 SNPs, Indels, large SVs, CNVs
• Illumina Mate Pair 6kb Insert: mappings
• PacBio: raw data
• Ion Torrent Exome: variants + mappings
• BioNano: raw data + assemblies
• Moleculo: mappings for Son and Father
• SNPs and Indels shared by 2+
technologies:
• Complete Genomics, proprietary pipeline
• Illumina HiSeq, BWA + GATK 1.6
• TMLT Ion Proton, TAMP + TVC
Research Use Only

19. 19
NIST preliminary analysis of Ashkenazim Trio
Run SNP &
Indels
SV CNV Genomic
VCF
Son 1 x x x x
2 x x x
Father 1 x x x x
3 x x x x
Mother 1 x x x x
2 x x x x
• GM24385 – Ashkenazim PGP Son
• GM24149 – Ashkenazim PGP Father
• GM24143 – Ashkenazim PGP Mother
• National Institute of Standards and
Technology (NIST):
• Illumina HiSeq, BWA + GATK 1.6
 SNPs, Indels, large SVs, CNVs
Research Use Only

20. 20
NIST preliminary analysis of Ashkenazim Trio
• GM24385 – Ashkenazim PGP Son
• GM24149 – Ashkenazim PGP Father
• GM24143 – Ashkenazim PGP Mother
• National Institute of Standards and
Technology (NIST):
• Illumina HiSeq, BWA + GATK 1.6
 SNPs, Indels, large SVs, CNVs
00 Run SNP &
Indels
SV CNV Genomic
VCF
UCSC BED
tracks
Son 1 x x x x x
2 x x x x
Father 1 x x x x x
3 x x x x x
Mother 1 x x x x x
2 x x x x x
Trio Merged x x x x x
Research Use Only

21. 21
Merged Ashkenazim Variants
• GM24385 – Ashkenazim PGP Son
• GM24149 – Ashkenazim PGP Father
• GM24143 – Ashkenazim PGP Mother
• National Institute of Standards and
Technology (NIST):
• Illumina HiSeq, BWA + GATK 1.6
 SNPs, Indels, large SVs, CNVs
Run SNP &
Indels
SV CNV
Son 1 5637374 14785 381
2 5618495 0 358
Father 1 5575725 17091 377
3 5598533 17569 348
Mother 1 5709480 16851 385
2 5690410 17488 356
Trio Total 33830017 83784 2205
Trio Merged 8423146 53151 1100
Research Use Only

22. 22
Filtered, Merged Ashkenazim Variants
• Horizon Diagnostics:
• Annotation: snpEff + SnpSift (dbNSFP)
 COSMIC ID
 dbSNP
 HGVS AA change
 HGVS codon change
 NCBI ClinVar (clinical significance)
 SIFT score (prob. damaging variant)
 phastCons 1000 score (site conservation)
 1000 genomes p1 Allele Freq. (non-syn.)
SNP &
Indels
SV CNV
Merged Trio 8423146 53151 1100
Filtered
variants
32532 53151 1100
Mixed variants 1162 1352 0
HIGH impact 5169 1607 41
MOD. impact 68028 265 0
Ann. Effects 73236 175708 3220
• GM24385 – Ashkenazim PGP Son
• GM24149 – Ashkenazim PGP Father
• GM24143 – Ashkenazim PGP Mother
• National Institute of Standards and
Technology (NIST):
• Illumina HiSeq, BWA + GATK 1.6
 SNPs, Indels, large SVs, CNVs
Research Use Only

23. 23
Comparison of the Ashkenazim Trio Data
Research Use Only

24. 24
GIAB: Conclusions
• Genome In A Bottle Reference Standards are invaluable for validating variant calling
analysis
• NIST and its collaborators shared datasets created with most NGS technologies
• Horizon Diagnostics shared annotated, merged variant calls from NIST for the
Ashkenazim Trio
• ~35K variants are predicted having high or moderate impact within the Trio
• GM24385 (Ashkenazim Son) includes 352 small variants with high/moderate impact
which are absent in Father and Mother
• Filtered, annotated variants are available for download on horizondx.com
Research Use Only

25. 25
Top NGS-Related Technical Enquiries
Research Use Only

26. 26
“I would like to validate my NGS workflow. What is the
application of your different Q-Seq products?”
Research Use Only

27. 27
How to Test the Robustness and Sensitivity of your
Workflow and Assay
Structural
Multiplex
DNA
Sample Complexity
Sample
Features
Quantitative
Multiplex
FFPE, DNA and
Formalin-
Compromised
DNA
Genome In A Bottle
FFPE
Gene-Specific
Multiplex
DNA and FFPE
Tru-Q
DNA
Research Use Only

28. 28
“I would like to assess the effect of Formalin on my assay”
Research Use Only

29. 29
Impact of Formalin Treatment on DNA
Research Use Only

30. 30
Quantitative Multiplex Reference Standard as Formalin-
Compromised DNA
Characterized fragmentation levels, DNA quantification, and defined allelic frequency
*These products are part of our early access program. It is the responsibility of the individual laboratory to determine
expected results specific to its assay.
Genomic DNA Tapescreen
assay
1 Ladder
2, 4 HD-C749 Reference Standard
3, 5 HD-C751 Reference Standard
[bp] 1 2 3 4 5
Research Use Only

31. 31
Upcoming Webinar
Title:
Understanding and Controlling for Sample and Platform Biases in
NGS Assays
Date:
Wednesday 22nd July 2015
Time:
4:00pm BST, 11:00am EST
Register now: www.horizondx.com/upcomingwebinar
Research Use Only

32. 32
“I would like to assess my bioinformatics pipeline for
detection of SNVs, Structural Variants and CNVs”
Research Use Only

33. 33
Variant Type Mutation
Expected Fractional
Abundance (%) or CNV:
SNV High GC GNA11 Q209L 5.6
SNV High GC AKT1 E17K 5.6
SNV Low GC KRAS G13D 5.6
SNV Low GC Pi3Ka E545K 5.6
Long Insertion EGFR V769 ins 5.6
Long Deletion
EGFR (delE746-A750)
5.3
Fusion ROS1 translocation 5.6
Fusion RET translocation 5.6
CNV MET amplification 4.5 x amplification
CNV MYC amplification 9.5 x amplification
SNP EGFR_G719S 5.3
Short Deletion MET_p.V237fs 4.8*
SNV High GC NOTCH1_p.P668S 5.0
Short Deletion FLT3_p.S985fs 5.6
Short Deletion BRCA2_p.A1689fs 5.6
Short Deletion FBXW7_p.G667fs 5.6
* %AF lower due to
MET amplification
Structural Multiplex Reference Standard
*This product is part of our early access program. It is the responsibility of the individual laboratory to determine
expected results specific to its assay.
Research Use Only

34. 34
Routinely monitor the performance of your workflows and
assays with independent external controls
What extraction
and quantification
methods are you
using?
What is the limit of
detection of your
workflow?
Is the impact of
formalin treatment
interesting to you?
What is the impact of assay failure in
your laboratory and how do you
monitor for it?
Research Use Only

35. 35
References
Slide 13 http://www.genome.gov/sequencingcosts/
Tuononen 2013, http://www.ncbi.nlm.nih.gov/pubmed/23362162
Spencer 2013, http://www.ncbi.nlm.nih.gov/pubmed/23810758
Fanelli 2010, http://www.ncbi.nlm.nih.gov/pubmed/21106756
Fanelli 2011, http://www.ncbi.nlm.nih.gov/pubmed/22082985
Gu 2011, http://www.ncbi.nlm.nih.gov/pubmed/?term=Preparation+of+reduced+representation+bisulfite+sequencing+libraries+for+genome-scale+DNA+methylation+profiling.
Gu 2010, http://www.ncbi.nlm.nih.gov/pubmed/20062050
Weng 2010, http://www.ncbi.nlm.nih.gov/pubmed/20593407
Meng, 2013, http://www.ncbi.nlm.nih.gov/pubmed/?term=meng+2013+comparison+of+microrna+deep+sequencing
Slide 15 Schweiger 2009, http://www.ncbi.nlm.nih.gov/pubmed/?term=schweiger%5BAuthor%5D+AND+2009%5BDate+-+Publication%5D+ffpe
Van Allen 2014, http://www.ncbi.nlm.nih.gov/pubmed/24836576
Slide 16 Hedegaard 2014, http://www.ncbi.nlm.nih.gov/pubmed/24878701
Wong 2014, http://www.ncbi.nlm.nih.gov/pubmed/24885028
Slide 27 BWA: Li 2010, http://www.ncbi.nlm.nih.gov/pubmed/20080505
GATK: McKenna 2010, http://www.ncbi.nlm.nih.gov/pubmed/20644199
snpEff: Cingolani 2012, http://www.ncbi.nlm.nih.gov/pubmed/?term=22728672
SnpSift: Cingolani 2012, http://www.ncbi.nlm.nih.gov/pubmed/22435069
dbNSFP: Liu 2013, http://www.ncbi.nlm.nih.gov/pubmed/23843252
Research Use Only