Detecting minor genetic variants has become essential to cancer and infectious disease management. Many have turned to next generation sequencing to fill this need given the common perception that the limit of detection (LOD) for Sanger sequencing is somewhere between 15% to 25%1,2,3. We have discovered a software algorithmic solution to reduce this detection limit to 5% and have demonstrated detection at even lower allele frequencies. Standard Sanger sequencing protocols can be used and the method can generate the familiar electropherogram data display with noise substantially reduced. This opens up an alternative for detecting low level somatic variants. The key observation that enabled this development is that the noise underlying Sanger sequencing fluorescence data (traces) appears to be highly correlated to the primary sequence in the data. Figure 1 shows the electropherograms from two different samples: the control sample has the same primary sequence as the test sample which contains a few minor variants.

1. Harrison Leong, Edgar Schreiber, Stephan Berosik, Shiaw-Min Chen, Wallace George, Jeffrey Marks, Stephanie Schneider
ThermoFisher Scientific, Genetic Sciences Division, 200 Oyster Point Blvd., South San Francisco, CA, 94080
RESULTS
Table 1 shows the sensitivity and specificity for allele frequencies 5% and
10%. Although we have been able to detect variants at allele frequencies
0.6125%, 1%, 1.25%, 2%, and 2.5%, the algorithm did not meet the LOD
criteria of 95% sensitivity and 99% specificity for these extremely low levels.
INTRODUCTION
Detecting minor genetic variants has become essential to cancer
and infectious disease management. Many have turned to next
generation sequencing to fill this need given the common
perception that the limit of detection (LOD) for Sanger sequencing
is somewhere between 15% to 25%1,2,3. We have discovered a
software algorithmic solution to reduce this detection limit to 5%
and have demonstrated detection at even lower allele frequencies.
Standard Sanger sequencing protocols can be used and the
method can generate the familiar electropherogram data display
with noise substantially reduced. This opens up an alternative for
detecting low level somatic variants.
The key observation that enabled this development is that the noise
underlying Sanger sequencing fluorescence data (traces) appears
to be highly correlated to the primary sequence in the data. Figure
1 shows the electropherograms from two different samples: the
control sample has the same primary sequence as the test sample
which contains a few minor variants.
CONCLUSIONS
It should now be possible to achieve a reference-based limit of detection of
5% allelic proportion with standard Sanger sequencing protocols. Existing
protocols for visually reviewing the results can also be used and are
enhanced because the algorithm generates results in the form of familiar
electropherograms for which the noise has been substantially diminished.
These two features of the algorithm may give Sanger sequencing
performance and/or economic advantages in some molecular diagnostic
applications that require finding minor genetic variants.
NOTE: Results on clinical samples can be found at
www.thermofisher.com/sangeroncology. The algorithm has been
embedded within ThermoFisher Scientific’s Minor Variant Finder software
(www.thermofisher.com/mvf ).
REFERENCES
1. Lin, M.T. et al. (2014), American Journal of Clinical Pathology, June 2014; 141:856-866.
2. Jancik, S. et al. (2012), Journal of Experimental & Clinical Cancer Research 2012;
31:79:1-13.
3. Tsiatis, A.C. et al. (2010), Journal of Molecular Diagnostics, July 2010; 12:4:425-432.
4. Wang G. and Guo L. (2013) Journal of Applied Mathematics, 2013; article 696491.
High Sensitivity Sanger Sequencing for Minor Variant Detection
Thermo Fisher Scientific • 5781 Van Allen Way • Carlsbad, CA 92008 • thermofisher.com
TT27
These are the key steps in the noise minimization algorithm:
a) for each of control and test traces, find the range of base
positions where the sequence data quality is consistently high;
b) find the intersection of the high-sequence-quality ranges between
the control and test sample traces; do the following within that
intersection:
c) remove the trace components associated with the primary bases
leaving the non-primary traces;
d) locally expand or contract and/or strengthen or weaken the non-
primary traces of the control sample to maximize correspondence
between the non-primary traces of the control and test samples;
e) subtract the manipulated non-primary traces from the test sample
traces;
f) suppress non-primary peaks that are obviously not variant peaks
(set them to zero) based on several peak characteristics such as
amplitude, width, alignment with the primary trace peak, etc.
This process is applied to traces from both forward and reverse
sequencing reactions. The outcome is noise minimized traces for
forward and reverse traces of the test sample that can be displayed for
review in the familiar electropherogram format. These traces are
passed into the second stage of the algorithm to automatically detect
variants.
AUTOMATED VARIANT DETECTION
For variant detection, the forward and reverse noise-minimized traces of
the test sample are examined for any remaining peaks. These peaks
are scrutinized by a set of five interconnected multi-variate classification
functions to decide whether or not there is a bona fide variant at a given
base position and its base identity. The final thresholds of four of these
functions are optimized for classification accuracy using an algorithm
based on swarm theory 4.
DATA FOR DEVELOPING AND TESTING THE METHOD
Samples came from 22 amplicons associated with eight different
genes: TP53, KRAS, BRAF, EGFR, FLT3, RB1, CDH1, and
ERBB2. Many of these were extracted from formalin-fixed,
paraffin-embedded samples. Some were commercially available
reference standards (Acrometrix), others were quantified using the
RNase-P quantitative polymerase chain reaction assay and serially
diluted. Allelic proportions spanned 0.6125% to 50%. These
samples were amplified, sequenced, and pre-processed using
standard protocols and tools for fluorescent dye terminator Sanger
sequencing from Applied BiosystemsTM.
A third of these data were used for developing the algorithms. Two
thirds were used for evaluating the performance characteristics of
the method.
Figure 1. Noise underlying two different samples looks very
similar when their primary sequences are the same.
Control Sample
Test Sample with Variants
Figure 1: Electropherograms from two different physical samples showing the underlying
noise; note the close similarity between the two. The bottom 200 relative fluorescence units
(RFUs) is shown. The primary peaks are up at around 1000 RFUs.
A two-part algorithm has been developed to exploit this
observation. The first part minimizes the noise that underlies the
traces. The second part detects variants, if any, in the noise-
minimized traces. This communication describes the algorithmic
details and shows test results.
f) Use a global optimization algorithm (one based on swarm
theory was used) to find optimum final threshold values for the
four discriminant functions of step (c) and (e).
The classifiers of step (e) deliver the final judgment on whether a
peak is associated with a variant or non-variant. Figure 2
summarizes the complete classification engine.
MATERIALS AND METHODS
NOISE MINIMIZATION
For noise minimization, a model of the noise in the traces of the test
sample is made from traces of the control sample and this model is
subtracted from the traces of the test sample.
The key steps of the variant detection algorithm, including construction
of the classification engine, are as follows:
a) Compute metrics on trace peaks such as the location of a peak
relative to that of the nearest primary base, symmetry of the peak,
sharpness of a peak relative to that of its nearest primary base,
etc.;
b) Classify the largest non-primary peaks based on each peak metric
alone to the degree that a peak can be unambiguously classified
in this manner;
c) Of those that cannot be classified in step (b), construct two
discriminant functions, one based on peak metrics that combine
forward and reverse information (x-strand), one based on peak
metrics that do not combine the two (s-strand). Within each of
these two categories, generate discriminant functions for all
possible combinations of metrics belonging to the category and
choose the function with the highest performance;
d) Use the s-strand classifier to generate additional peak metrics;
e.g., the probability ratio between variant and non-variant peaks
based on s-strand pre-thresholded output;
e) Make two additional discriminant functions: one for peaks
categorized as variants by the x-strand classifier and the second
for peaks categorized as non-variants by the x-strand classifier.
Metrics of steps (c) and (d) are used to create these discriminant
functions using the feature selection process of (c).
INPUT DATA:
Forward control
Forward test
Reverse control
Reverse test
Fwd and Rvs
test, noise
minimized
Single-strand metrics: peak
height, width, sharpness,
symmetry, signal to noise, etc.
Cross-strand metrics
(combined fwd rvs information):
base complementarity, relative
peak amplitude, relative width,
etc.
Signal to noise outliers are variant candidates
Classifier for clear cut cases
Classifier based on
cross-strand metrics
Classifier to override
cross-strand variant calls
(all metrics)
Classifier to override cross-
strand non-variant calls (all
metrics)
OUTPUT RESULTS:
Variant locations and base identities
Meta-metrics from
classifier based on single-
strand metrics: var/non-var
probability ratio, pre-
thresholded score, etc.
Figure 2. The classification engine for variant detection.
Figure 2: Trace data enters at the upper left and detected variants, if any, are reported out at
the bottom. The figure illustrates that the decision making process is layered so that easy
decisions are made first and only the trace peaks that cannot be clearly classified are
funneled down into the deeper levels of analysis. This allows the classifier at each level to
concentrate on a smaller set of the data which may have a simpler statistical structure.
Figure 3 shows results of applying noise minimization to the
forward sequencing orientation of a sample with three variants at
an allele frequency of 1.25%. The central panel shows the traces
before minimization. The process clearly reveals the three variant
peaks. The red marks in the bottom panel indicate where the
automated variant detection algorithm called out variants.
1.25% Variant Test Sample
Control Sample
algorithm finds the variants
KB Basecaller misses the variants
1.25% Test Sample, Noise Minimized
Figure 3. Noise minimized trace example (bottom panel).
Figure 3: Noise minimization reveals 1.25% minor variants deeply embedded in the noise underlying
Sanger trace data. The high similarity in the noise between the control (top panel) and test (middle
panel) traces allows much of the noise to be removed (bottom panel).
TABLE 1: Algorithm performance for allele frequencies meeting LOD criteria
Variant
Level
Sensitivity Specificity Datasets Total True
Variants
Total True
Non-variants
5% 95.9% 99.8% 704 785 229623
10% 98.8% 99.8% 454 503 163037