1. Bioinformatics and PCR
Jo Vandesompele
professor, Ghent University
co-founder and CEO, Biogazelle
Bioinformatics for the Medical Molecular Laboratory
HOWEST Brugge, 18 oktober, 2011

2. outlines introduction on (q)PCR
primer design and assay validation (17)
reference gene selection and validation (7)
cross laboratory data comparison using external standards (7)
data-analysis (5)
RDML data exchange and reporting (4)
statistics (1)

3. polymerase chain reaction (PCR)
30-45 cycles of a 3-step process
denaturation (95° C 15 s)
annealing (50-70° C 15 s)
20bp ~1/
extension (72° C 1 min)

4. polymerase chain reaction (PCR)
exponential
amplification
35 cycli
235 = 68 x 10E9 1
2 (21)
4 (22)
8 (23)
16 (24)

5. polymerase chain reaction (PCR)
endpoint detection (agarose gel electrophoresis)
not really quantitative
carry-over contamination
laborious

6. real-time PCR principle

7. real-time PCR principle
continuous monitoring of PCR product accumulation
i.e. measure every cycle the amount of fluorescence
relationship between the time fluorescence increases above background and the initial amount of template
i.e. the sooner fluorescence is visible, the more template was present, and vice versa

8. amplification curve exponential function (y=a^x + b)
sigmoid amplification curve

9. amplification curve

10. quantification cycle value: threshold methodCq

11. quantification cycle value: 2nd derivative maximum methodCq

12. comparative Cq quantification method
delta-Cq = 19 – 17 = 2
2^2 = 4

13. comparative Cq quantification method

14. comparative Cq quantification method
Relative Quantity (RQ) = E^delta-Cq (calibrator – unknown sample)
amplification efficiency E
normalization to correct for experimental differences
NRQ = RQgoi/RQref

15. standard curve quantification method
y=a*x+b Cq 1 35
10 1
10 2
unknown sample
10 3 25
350 copies
10 4
10 5
10 6 15 1 2 3 4 5 6
log10 quantity

16. standard curve quantification method
slope (a) of the standard curve ~ efficiency of the PCR
efficiency = 10^(-1/a) - 1
ideal efficiency
100 % 1
slope of
2 (base exponential amplification) !

17. real-time PCR advantages large dynamic range of linear quantification
sensitivity
accuracy and reproducibility
high-throughput
absence of post-PCR manipulations

18. real-time PCR detection chemistries
double-stranded DNA specific binding dyes
SYBR Green I, EvaGreen
very well suited for quantification of a large number of different sequences
detection of primer-dimer artifacts and non-specific amplification
product differentiation (DNA melting curve analysis) (Ririe et al., 1997)

19. real-time PCR detection chemistries
double-stranded DNA specific binding dyes
SYBR Green I, EvaGreen
sequence specific probes
hydrolysis probe (TaqMan), Molecular beacon, dual hybridization probes
combined primer-probes
Scorpion
fluorescent primers
Sunrise, LightUp, Lux
probes are useful for multiplexing, genotyping and in diagnostics; for all other applications, ds DNA binding dyes are method of choice

20. SYBR Green I

21. SYBR Green I

22. SYBR Green I 65 70 75 80 85 90
melt curve analysis Tm
82.6
88.1
-dF/dT
melting peak

23. uMelt - http://dna.utah.edu/umelt/umelt.html
prediction of melt peaks

24. hydrolysis probe

25. hydrolysis probe

26. hydrolysis probe
FRET

27. hydrolysis probe

28. hydrolysis probe

29. hydrolysis probe

30. hydrolysis probe (TaqMan probe)
Pacman

31. booming technology
Higuchi et al., 1993

32. real-time PCR applications
gene expression analysis (gold standard)
fusion gene detection (MRD)
pathogen detection (e.g. viral load)
genotyping (SNP analysis, mutation analysis)
gene copy number quantification (e.g. oncogenes, exon deletions)

33. critical factors contributing to reliable results
Derveaux et al., Methods, 2010

34. assay design – probes vs SYBR
choose probes for
multiplexing
genotyping
absolute sensitivity (detection past cycle 40) (e.g. clinical-diagnostic setting, GMO detection)
choose SYBR Green I for
all other applications
low cost
seeing what you do 34

35. assay design – design guidelines
location
sequence repeats, protein domains (pfam database)
splice variants
intron spanning vs. exonic
size
short amplicons: bp
primers
dTm < 2°C
identical Tm for all assays
maximum 2 GC in last 5 nucleotides
use software to design assays
Primer3(Plus), BeaconDesigner, RTPrimerDB / primerXL 35

36. PCR assay design and validation
do thorough in silico assay evaluation
BLAST/BiSearch specificity analysis
mfold secondary structure
SNP analysis of primer annealing regions
splice variant specificity
do experimental validation
standard curve (range, # dilution points are important)
formula 4 Hellemans et al., 2007, Genome Biology
electrophoresis (agarose, polyacrylamide, microfluidic) (only once)
melting curves
(sequence)
submit validated assay to public database, such as RTPrimerDB

37. Primer3Plus - http://www.primer3plus.com

38. BiSearch - http://bisearch.enzim.hu
developed for PCR specificity assessment
faster due to indexing the genome

39. dbSNP - http://www.ncbi.nlm.nih.gov/projects/SNP/

40. mfold - http://mfold.rna.albany.edu/?q=mfold

41. assay QC – in silico validation
101%
920% 41

42. RTPrimerDB – http://www.rtprimerdb.org
database of experimentally verified qPCR assays
in silico evaluation of custom assays
Nucleic Acids Research, 2003, 2006, 2009

43. assay QC – in silico validation
step 1

44. assay QC – in silico validation
step 2

45. assay QC – in silico validation
step 3

46. assay QC – in silico validation
mfold
Zuker et al., Nucleic Acids Research, 2003

47. assay QC – in silico validation

48. assay QC – in silico validation

49. assay QC – wet lab validation49

50. primerXL – http://www.primerxl.org (not online yet)
Lefever et al., in preparation
primer design for qPCR, resequencing, genotyping

51. reference gene variability
quantitative RT-PCR analysis of 10 reference genes (belonging to different functional and abundance classes) on 85 samples from 13 different human tissues 1 2 3 4
ACTB
HMBS
HPRT1
TBP
UBC A B C D E F G
15 fold difference between A and B if normalized
by only one gene (ACTB or HMBS)

52. our geNorm solution
framework for qPCR gene expression normalisation using the reference gene concept:
quantified errors related to the use of a single reference gene
(> 3 fold in 25% of the cases; > 6 fold in 10% of the cases)
developed a robust algorithm for assessment of expression stability of candidate reference genes
proposed the geometric mean of at least 3 reference genes for accurate and reliable normalisation
Vandesompele et al., Genome Biology, 2002

53. Data QC – reference gene stability
pairwise variation V (between 2 genes)
gene stability measure M
average pairwise variation V of a gene with all other genes
gene A
gene B
sample 1 a1 b1
log2(a1/b1)
sample 2 a2 b2
log2(a2/b2)
sample 3 a3 b3
log2(a3/b3) … … … …
sample n an bn
log2(an/bn)
standard deviation = V

54. geNorm software
ranking of candidate reference genes according to their stability
determination of how many genes are required for reliable normalization

55. Data QC – reference gene stability
geometric mean of 3 reference gene expression levels
controls for outliers
compensates for differences in expression level between the reference genes
geometric mean = (a x b x c) 1/3
arithmetic mean =
a + b + c 3

56. geNorm validation cancer patients survival curve
statistically more significant results
0.003
0.006
0.021
0.023
0.056
NF4
NF1
log rank statistics
Hoebeeck et al., Int J Cancer, 2006

57. normalization using multiple stable reference genes
geNorm is the de facto standard for reference gene validation and normalization
> 3500 citations of our geNorm technology
> 15,000 geNorm software downloads in 100 countries

58. improved geNorm > genormPLUS
classic
geNorm
improved geNorm
platform
Excel
Windows
qbasePLUS
Win, Mac, Linux
speed 1x
20x
interpretation - +
ranking best 2 genes
handling missing data
raw data (Cq) as input

59. external oligonucleotide standards
synthetic control
55-60 nucleotides
standard desalted – unblocked (<> Vermeulen et al., 2009)
5 points dilution series: molecules > 15 molecules FP
stuffer
RCRP

60. external oligonucleotide standards cross lab comparison
5 standards (triplicates)
3 reference genes + 5 genes of interest
366 samples

61. external oligonucleotide standards cross lab comparison
5 standards (triplicates)
average ΔCq standards
correction Cq samples
Cq qPCR instrument 1, mastermix 1
Cq qPCR instrument 2, mastermix 2

62. external oligonucleotide standards cross lab comparison
5 standards (triplicates)
Cq qPCR instrument 1, mastermix 1
Cq qPCR instrument 2, mastermix 2

63. external oligonucleotide standards cross lab comparison
ARHGEF7 gene
366 samples
use of 5 standards (triplicates) for correction
Cq 7900HT
Cq LC480
abs (dCq)

64. external oligonucleotide standards cross lab comparison
5-15% better patient classification accuracy after calibration

65. external oligonucleotide standards cross lab comparison
Vermeulen et al., Nucleic Acids Research, 2009

66. problem of data-analysis
extraction of meaningful biological information from qPCR data

67. problem of data-analysis
extraction of meaningful biological information from qPCR data

68. universal quantification model with proper error propagation

69. qBase paper
Hellemans et al., Genome Biology, 2007

70. qbasePLUS
most powerful, flexible and user-friendly real-time PCR data-analysis software
based on Ghent University’s geNorm and qBase technology
up to fifty 384-well plates
multiple reference genes for accurate normalization
detection and correction of inter-run variation
dedicated error propagation
automated analysis; no manual interaction required

71. MIQE guidelines in Clinical Chemistry

72. MIQE checklist for authors, reviewers and editors
experimental design
sample
nucleic acid extraction
reverse transcription
target information
oligonucleotides
qPCR protocol
qPCR validation
data analysis

73. RDML data exchange format73

74. RDML data exchange format
Lefever et al., Nucleic Acids Research, 2009

75. Statistical analysis & interpretations
sample size (~ power analysis)
log transform gene expression data
consider pairing
parametric vs. non-parametric
central limit theorem: parametric test
better safe than sorry: non-parametic
t-test Mann-Whitney
Paired t-test Wilcoxon signed rank test
ANOVA Kruskal-Wallis 75