CHILD: a new tool for detecting low-abundance insertions and deletions in standard sequence traces.
ABSTRACT Several methods have been proposed for detecting insertion/deletions (indels) from chromatograms generated by Sanger sequencing. However, most such methods are unsuitable when the mutated and normal variants occur at unequal ratios, such as is expected to be the case in cancer, with organellar DNA or with alternatively spliced RNAs. In addition, the current methods do not provide robust estimates of the statistical confidence of their results, and the sensitivity of this approach has not been rigorously evaluated. Here, we present CHILD, a tool specifically designed for indel detection in mixtures where one variant is rare. CHILD makes use of standard sequence alignment statistics to evaluate the significance of the results. The sensitivity of CHILD was tested by sequencing controlled mixtures of deleted and undeleted plasmids at various ratios. Our results indicate that CHILD can identify deleted molecules present as just 5% of the mixture. Notably, the results were plasmid/primer-specific; for some primers and/or plasmids, the deleted molecule was only detected when it comprised 10% or more of the mixture. The false positive rate was estimated to be lower than 0.4%. CHILD was implemented as a user-oriented web site, providing a sensitive and experimentally validated method for the detection of rare indel-carrying molecules in common Sanger sequence reads.
- SourceAvailable from: Chyong-Huey Lai[Show abstract] [Hide abstract]
ABSTRACT: The direct sequencing of PCR products generates heterozygous base-calling fluorescence chromatograms that are useful for identifying single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), short tandem repeats (STRs), and paralogous genes. Indels and STRs can be easily detected using the currently available Indelligent or ShiftDetector programs, which do not search reference sequences. However, the detection of other genomic variants remains a challenge due to the lack of appropriate tools for heterozygous base-calling fluorescence chromatogram data analysis. In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. The heterozygous sequences are identified as two distinct sequences and aligned with reference sequences. Our results showed that MSR may be used to (i) physically locate indel and STR sequences and determine STR copy number by searching NCBI reference sequences; (ii) predict combinations of microsatellite patterns using the Federal Bureau of Investigation Combined DNA Index System (CODIS); (iii) determine human papilloma virus (HPV) genotypes by searching current viral databases in cases of double infections; (iv) estimate the copy number of paralogous genes, such as β-defensin 4 (DEFB4) and its paralog HSPDP3.The Scientific World Journal 01/2012; 2012:365104. · 1.22 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Designer nucleases such as TALENS and Cas9 have opened new opportunities to scarlessly edit the mammalian genome. Here we explored several parameters that influence Cas9-mediated scarless genome editing efficiency in murine embryonic stem cells. Optimization of transfection conditions and enriching for transfected cells are critical for efficiently recovering modified clones. Paired gRNAs and wild-type Cas9 efficiently create programmed deletions, which facilitate identification of targeted clones, while paired gRNAs and the Cas9D10A nickase generated smaller targeted indels with lower chance of off-target mutagenesis. Genome editing is also useful for programmed introduction of exogenous DNA sequences at a target locus. Increasing the length of the homology arms of the homology-directed repair template strongly enhanced targeting efficiency, while increasing the length of the DNA insert reduced it. Together our data provide guidance on optimal design of scarless gene knockout, modification, or knock-in experiments using Cas9 nuclease.PLoS ONE 08/2014; 9(8):e105779. · 3.53 Impact Factor
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ABSTRACT: Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3-14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant Mycobacterium tuberculosis strains carrying frameshift mutations mixed with wild-type bacteria in the pncA gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing.PLoS ONE 01/2013; 8(1):e54835. · 3.53 Impact Factor
CHILD: a new tool for detecting low-abundance
insertions and deletions in standard sequence
Ilia Zhidkov1,2, Raphael Cohen3, Nophar Geifman1,4, Dan Mishmar1,2and Eitan Rubin1,4,*
1National Institute for Biotechnology in the Negev,2Dept. of Life Sciences,3Dept. of Computer Sciences
and4Shraga Segal Dept. of Microbiology and Immunology, Ben Gurion University of the Negev,
Beer Sheva 84105, Israel
Received August 25, 2010; Revised December 21, 2010; Accepted December 22, 2010
Several methods have been proposed for detecting
insertion/deletions (indels) from chromatograms
generated by Sanger sequencing. However, most
such methods are unsuitable when the mutated
and normal variants occur at unequal ratios, such
as is expected to be the case in cancer, with
organellar DNA or with alternatively spliced RNAs.
In addition, the current methods do not provide
robust estimates of the statistical confidence of
their results, and the sensitivity of this approach
present CHILD, a tool specifically designed for
indel detection in mixtures where one variant is
rare. CHILD makes use of standard sequence align-
ment statistics to evaluate the significance of the
results. The sensitivity of CHILD was tested by
sequencing controlled mixtures of deleted and
undeleted plasmids at various ratios. Our results
indicate that CHILD can identify deleted molecules
present as just 5% of the mixture. Notably, the
results were plasmid/primer-specific;
primers and/or plasmids, the deleted molecule was
only detected when it comprised 10% or more of
the mixture. The false positive rate was estimated
to be lower than 0.4%. CHILD was implemented as
a user-oriented web site, providing a sensitive and
experimentally validated method for the detection of
rare indel-carrying molecules in common Sanger
Heritable short insertion and deletions (indels) account for
as many as 2.5M polymorphic sites in the human genome,
representing more than 25% of the entire human variation
repertoire (1). Short indels have also been associated with
diseases (2), underlining their importance to human
expected to encode either 50% or 100% of the molecules
in a given genome. However, in many cases, indels are
expected to encode different fractions of molecules as a
result of splice variants (3–5), de novo mutations in cancer
(6–8) and mutations found in organelles that carry
(9–12), for example. An attractive approach for identify-
ing novel indels in specific genomic regions is to amplify
these regions via the polymerase chain reaction (PCR) and
directly sequence products with the standard Sanger chain
termination sequencing method.
sequencing of mixed molecular species will give rise to a
‘double trace’, in which chromatograms that would have
resulted from the sequencing of each molecular species
separately are superimposed. In the case of an indel, the
two traces following the inserted/deletion region should be
identical but shifted. In other words, the traces comprising
the double trace should self-align (Figure 1). Alternative
approaches for de novo indel detection are more laborious
or costly, such as cloning and sequencing of multiple mol-
ecules. Massive parallel sequencing for this purpose is cur-
rently only cost-efficient if used on large genomic regions
Resolving chromatograms for rare indel detection is
complicated by the minor contribution of the rare
variant to the superimposed chromatogram, a contribu-
tion that may be close to noise levels. A number of tools
have been developed that can be used for the purpose of
detecting relatively rare indels. Such tools are all based on
calling not only the best base for every position in the
trace but also the base defined by the second highest in-
tensity peak in the chromatogram. Two sequences are thus
generated from each chromatogram (or, in some cases, a
single degenerate sequence is formed), which can then be
In this method,
*To whom correspondence should be addressed. Tel: +972 8 6477180; Fax: +972 8 6479197; Email: email@example.com
Published online 28 January 2011Nucleic Acids Research, 2011, Vol. 39, No. 7 e47
? The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
aligned to each other (Figure 1), with or without compari-
son to a reference sequence (16–21). Interestingly, all of
the published methods presented thus far rely on
specialized sequence alignment algorithms specifically
designed to search for near-perfect shifted alignment,
rather than standard alignment algorithms, such as the
Consequently, none of these tools provides a robust stat-
istical significance estimate of the results. ShiftDetector,
for example, compares two sequences derived from
primary and secondary peak calls using a window-based
comparison and provides an estimate of the likelihood to
observe by chance a given number of matches in a
window, based on the binomial distribution. However,
ShiftDetector does not account for the multiple hypothesis
testing involved in considering many windows and many
possible gap sizes, and does not account for sequence com-
position biases (20). Other methods, such as Indelligent,
offer no statistical test.
Here, we describe CHILD, a new short indel detection
algorithm based on double-trace resolution which utilizes
standard alignment algorithms and the robust statistics
implemented inthe SSEARCH
CHILD was specifically designed to consider rare indels
and offers a user-friendly web interface. We experimen-
tally evaluated CHILD performance in terms of both
sensitivity and specificity using a set of controlled mixes
of cloned molecules containing indels. We show that
indels could be detected with a sensitivity as low as 5%
and a specificity as high as 98%, although these values
may vary, depending on the different primers and tem-
MATERIALS AND METHODS
The CHILD algorithm
The algorithm involves three consecutive steps: (i) infer-
ence of primary and a secondary DNA sequences from
trace files (Sanger sequencing) by parsing the output of
the PHRED algorithm, (ii) local alignment of the
primary and secondary sequences using the Smith–
Waterman algorithm and (iii) indel detection within the
(i) Inference of primary and a secondary DNA se-
quences from trace files. Trace files are processed
with PHRED (24). Using the detailed output
option (i.e. ‘–d’), the amplitudes of all four traces
at every sequence position are written to a ‘*.poly’
file. This file is parsed to extract a primary sequence,
representing those bases with the highest intensity at
each position, and a secondary sequence, containing
those bases with the second-best amplitude at every
position. In positions where the secondary peak in-
tensity was lower than 2.3% of the primary peak
intensity, the secondary sequence is assigned the
same base as the primary base. This threshold was
chosen based on the distribution of the intensity
ratio between primary and secondary peaks in 47
positions), which was found to reach its maximal
second-best amplitude values are found to have
passed the ratio threshold, one base is randomly
chosen. To reduce noise, only positions 20–700 of
the chromatogram are considered, thus trimming
the beginning and ends of the sequenced fragments.
(ii) Local alignment of the primary and secondary se-
quences. The primary and secondary sequences are
aligned using the SSEARCH program (22,23) from
the FASTA package (version 3.5), which imple-
ments the Smith–Waterman local alignment algo-
rithm with shuffling-based significance estimation.
Default parameters are used with the following
modifications: gap penalty score (the ‘-f’ flag) is
set to ?80, maximal expectation value (the ‘-E’
flag) is set to 10?4and the number of shuffling
tests is set to 1000.
(iii) Indel detection in the resulting sequence alignment.
The results are parsed to extract the best alignment
significance level and coordinates (i.e. the begin-
nings and ends of the primary and secondary se-
quences). An indel is reported only for statistically
gapped alignment is detected, only the 50terminal
ungapped alignment is considered; if both primary
and secondary gaps are detected, the longest of
the 50terminal ungapped alignments is considered.
A special warningis
indel length is estimated from the difference in
Figure 1. Representative fragment of an ABI trace file with mixed intact and 9-bp-deleted templates. The framed bases on the top panel represent
the strongest calls, while bottom sequences represent the second-best calls harboring the 9-bp deletion and hence, the sequence frame-shift.
e47Nucleic Acids Research, 2011,Vol. 39,No. 7PAGE 2 OF 8
Implementation and availability. CHILD is implemented
in Perl (version 5). The source code and sample files are
available as Supplementary Data; chromatograms can be
submitted for CHILD analysis at http://bioinfo.bgu.ac.il/
Plasmid constructs. The program sensitivity and accuracy
were validated by analyzing plasmid constructs harboring
human mitochondrial DNA (mtDNA) fragments encom-
passing naturally occurring deletions. Specifically, two sets
of constructs were generated carrying deletions of differ-
ent sizes (pMitA and pMitB). For pMitA, PCR products
of human mtDNA fragments corresponding to positions
1–722 (Revised Cambridge Reference Sequence (rCRS)
NC_012920), with or without a 51-bp deletion corres-
ponding to positions 297–348, (pMitA and pMitA?51,
respectively) were amplified and cloned into the pUC18
vector, as described previously (25). Similarly, pMitB
and pMitB?9 were created by cloning the PCR fragments
corresponding to rCRS positions 8123–8388, with or
without a 9-bp deletion corresponding to positions
8271–8281, using the pGEM-T vector, as described previ-
ously (26). A series of dilutions varying the ratios of the
deleted and intact constructs were prepared in triplicate
for all of the above-described constructs.
Sequencing. The construct mixtures were sequenced using
an Applied Biosystems 3130 Genetic Analyzer DNA se-
quencer. For the pUC18 vector, the M13 reverse standard
primer was used. For the pGEM-T vector, the standard
T7 and SP6 primers were used.
Twotypes of calibration
generated for examining the shadow effect (see text)
relying on: (i) the primers and template provided with
the BigDye Terminator v1.1 sequencing kit (Applied
Biotechnology in the Negev, Ben Gurion University
(BGU)], and (ii) the pGEM ezf+ plasmid (Applied
Biosystems) with an in-house synthesized, column-purified
T7 primer (Biological Services, Weizmann Institute of
Science) and the ABI 3730 DNA analyzer.
We have developed CHILD, a computer program for the
detection of small sub-populations of molecules carrying
indels using ABI trace files. The program compares the
sequence of the strongest base calls at each position with
the sequence of the second-best calls (Figure 1). Alignment
of the two sequences and shuffling tests are then used to
test whether the sequences generated from the secondary
peaks (namely the secondary sequence) are not random,
i.e. represent a shifted version of the primary sequence.
Evaluation of CHILD performance
The data were experimentally generated to test the per-
formance of CHILD, evaluating (i) sensitivity, i.e. the
minimal fraction of the molecules carrying an indel that
can be detected; (ii) specificity, i.e. the fraction of falsely
reported indels in pure samples; and (iii) size accuracy, i.e.
the fraction of correctly determined indel sizes. Two sets
of plasmid constructs were used to evaluate CHILD per-
formance, with the first carrying a 51-bp deletion and the
second a 9-bp deletion in the human mitochondrial
genome. The tests were conducted separately for each
mutation, including both intact and deleted constructs of
the relevant mutation (see ‘Materials and Methods’
section). Mixtures of each construct, with and without
the deletions, were prepared in triplicate, sequenced at
the BGU sequencing facility and analyzed with CHILD
(Table 1). The sensitivity of CHILD varied, depending on
the construct and sequencing primers used. CHILD suc-
cessfully detected the deleted molecule while analyzing
chromatograms that originated from mixtures containing
5% or more of the plasmid pMitA?51 construct. For the
plasmid pMitB?9 construct, on the other hand, only
mixtures containing 10% or more of the deleted
molecule were reproducibly detected, especially when the
sequencing reactions were conducted using the T7 primer.
Furthermore, when the SP6 primer was used to sequence
mixtures containing this construct, 20% or more of the
deleted molecules were required for detection.
CHILD was found to be highly specific. When
shadow-induced short (1–2bp) indels were ignored, false
positive were not found in nine pure samples (three of
plasmid pMitA and six of plasmid pMITB, with either
the T7 or the SP6 primers). To further investigate the spe-
cificity of the program, we analyzed 3024 traces (available
at the NCBI Trace Archive: http://nsdl.org/resource/2200/
test.20061004111541306T, from chromosome 20 of the
ENCODE project). As these traces originated from
cloned molecules, they were not expected to include
indel mixtures. Less than 1% of these samples (N=28)
were reported by CHILD to carry indels of a length >2.
CHILD also identified the indel size with high accuracy.
For 85% of the mixtures involving plasmid pMitA?51,
the deletion size was accurately determined, while for the
remaining four samples, it was overestimated by 1bp. For
plasmid pMitB?9, all of the reported indel sizes were
Unlike the size determination, position determination
with CHILD was inaccurate, with indel start position
varying by as much as 200bp (Table 1). Since we think
that these errors may very likely be the result of an intrin-
sic property of the sequencing reaction, we conclude that
CHILD can offer only a rough estimate of indel position.
The impact of double peaks on indel detection from
Manual inspection of the alignment between the primary
and secondary sequences of the 2.5% plasmid pMitA?51
mixture revealed a statistically significant alignment
involving a 1-bp indel (Figure 2A). In fact, a high-quality
‘shadow’ alignment was frequently found 50to the deletion,
with a shift of 1 or 2bp. This phenomenon is documented
in the ABI troubleshooting guide (Applied Biosystems,
Carlsbad CA, USA), where it is suggested to be influenced
by the primer used for the PCR or by the sequencing
steps. Suggested causes of such shadow alignment include
PAGE 3 OF 8Nucleic AcidsResearch, 2011, Vol.39,No. 7e47
length variation introduced during primer synthesis,
homopolymer runs embedded within the beginning of the
template. Surprisingly, the primer–plasmid combination
used for standard calibration of the sequencing kits
(Figure 2B) also produced shadow alignments, suggesting
that a low level of shifted molecules is commonly found in
sequencing reactions, regardless of the presence of
homopolymers in the template or the primer sequences.
A comparison of CHILD performance versus that of
Indellignet and ShiftDetector
The chromatograms generated from controlled indel/
wild-type mixtures were analyzed with two other algo-
rithms, namely Indelligent and ShiftDetector (Table 2).
VarDetect was not tested since it repeatedly failed to
analyze these chromatograms, either due to technical
problems or due to the increase of the maximal indel
size parameter to 51bp. The results suggest that CHILD
is both more accurate and more sensitive in detecting rare
variants. CHILD was the only algorithm that repeatedly
detected indels involving 5% of plasmid pMit?A.
ShiftDetector did report a 51-bp indel for mixtures
involving higher concentrations of the deletion-carrying
molecule but often reported other deletions as well. For
example, given a 30% mixture sequenced with the M13
primer, ShiftDetector reported two to three indels for
each replica, correctly reporting a 51-bp indel for two
out of the three repeats (compared to three out of three
for CHILD, which reports only the correct indel).
Indelligent performed very poorly in this analysis, never
detecting the correct indel for plasmid pMit?A, and
detecting plamsid pMit?B with a sensitivity of ?20%
(as compared to 10% with CHILD). We note that using
Table 1. Experimental evaluation of CHILD sensitivity and reproducibility
% ? constructPredicted% ? constructPredicted
P-value LocSizeP-valueLoc Size
% ? construct primer used: T7
% ? constructprimer used: SP6
P-valueLoc SizeP-value LocSize
The table shows an analysis of the 51- and 9-bp deletion constructs with CHILD.
(Upper part) Results for pMitA and pMitA?51.
Analysis was conducted for chromatograms generated with increasing concentration of the deletion-containing construct (%?construct) and with
two different sequencing primers (T7 and SP6).
The confidence that the indel is not the result of noise (P-value), the indel positions in the corresponding ABI trace file (LOC) and the inferred length
of the indel (size) are given as provided by CHILD, analyzing each biological replication separately.
(lower part) The same analysis as in upper part, using pMitB and pMitB?9.
NA: no indel was found in the corresponding ABI trace file (the alignment was statistically insignificant or otherwise incompatible with an indel).
aIndels of length 1–2bp are likely to be artificial (see text) and are ignored in subsequent analysis.
e47 Nucleic Acids Research, 2011,Vol. 39,No. 7PAGE 4 OF 8
Indelligent requires an extra step of secondary peaks
calling with Sequencher, and that we did not optimize
this step. Nevertheless, our results indicate that a naı¨ve
user can submit chromatograms to CHILD without any
optimization or manual editing, and that rare indel
variants are best detected with CHILD.
We present here CHILD, a dynamic programming-based
software for the identification of indels from ABI
sequencing traces. CHILD infers primary and secondary
DNA sequences from sequence traces using PHRED and
aligns the two using SSEARCH. A statistically significant
similarity between the two reads is used to rule out the
possibility that the secondary sequence is a random
sequence. The beginning of the alignment indicates the
position of the indel and the shift between the reads
reveals the size of the indel.
In addition to the development of this new tool, we
present here, for the first time, a controlled experimental
evaluation of the sensitivity and specificity of this
approach. In previous reports, the ability to detect indels
was evaluated with DNA extracted from individuals
shown to carry an indel with other methods, either in a
heterozygous situation (see for example ref. 20) or at
unknown ratios (1). Our results suggest that the sensitivity
of this approach depends on template and primer choice.
While variation in DNA purity could contribute to this
difference, a 2-fold decrease in sensitivity was observed
when the same template was sequenced using different
primers (Table 1). The sensitivity of chromatogram-based
indel detection most likely depends on signal and noise
levels. One source of noise known to be template- and
primer-dependent is the occurrence of n+1/n–1 ‘shadow’
peaks in the chromatogram. Sufficiently high levels of
shadow peaks will confound shift detection algorithms.
Additionally, random noise may also be template- and
primer-dependent, and could overshadow the secondary
‘shadow’ sequences are expected to be detrimental to
indel localization by CHILD. The observed position at
which the indel-related alignment begins is determined
by the relative strength of the secondary sequence and
the shadow sequence. As a result, CHILD provides only
a rough estimate of the indel position, as indicated in the
output of the program.
Our controlled analysis suggests that CHILD frequently
fails to resolve traces resulting from equal amounts of the
two molecular species (Table 1). When considering the
algorithm, the occasional successes are more surprising
than the failures. In the case of equimolar species, the
peaks originating from the two molecules should be
equal in intensity. As a result, their assignment as
‘primary’ and ‘secondary’ should be random. The result-
ing sequences are thus expected to align very poorly. We
note that this should hold true for other algorithms that
are based on primary/secondary sequence calling. This
stands in contrast with the reported successes of other al-
gorithms in resolving traces originated from heterozygous
Figure 2. Examples of n+1-shifted chromatograms (‘shadow sequences’). Alignment of secondary (upper) and primary (lower) sequences generated
using (A) a highly diluted mixture of plasmids pMitA and pMitA?51 (2.5%) or (B) a pure sample of the pGEM ezf+plasmid and a column-purified
T7 primer. The primary and secondary sequences from each chromatogram were aligned using SSEARCH (see ‘Materials and Methods’ section).
PAGE 5 OF 8Nucleic AcidsResearch, 2011, Vol.39,No. 7e47
indels, as well as the success of ShiftDetector reported
here (Table 2) and the occasional success of CHILD
in resolving 50% mixtures. This contradiction can be
explained by (i) minute differences in the original concen-
tration of the two molecules; (ii) differences in the ampli-
fication efficiency of the two species in PCR; or (iii)
differences in the efficiency of the sequencing reactions
in reading the two molecules. Consistent differences
between the intensities of the peaks derived from the
two molecules through either of these mechanisms would
result in proper assignment of peaks to primary and sec-
ondary sequences, and successful peak detection. Further
research is required to understand why and when CHILD
and other secondary-sequence-based algorithms succeed
in detecting heterozygous mutations. We note that
Indelligent is not expected, even in theory, to have
difficulties resolving traces from equimolar mixtures as it
calls a degenerate sequence without trying to assign the
secondary peaks into a separate sequence (see below).
Due to design considerations, CHILD will always
report a single indel and may successfully report only
one indel in a molecule carrying multiple indels. In fact,
we present results in which a very similar scenario is suc-
cessfully resolved: shadow sequences imitate the presence
of two indels co-occurring in the same chromatogram,
involving a short (1–2bp) indel and a longer (9- or
51-bp long) indel. In most of the above-reported cases,
one indel was successfully identified in each chromato-
gram and the other was ignored. Nevertheless, it is diffi-
cult to predict the behavior of CHILD with real multiple
indels: it is expected to depend on the length of the indels,
their position, the quality of the alignment before and
after each gap and the presence of a shadow sequence.
The windows-based approach of ShiftDetector may be
mixtures involving rare multi-indel variants, if adjust-
ments are made to make it more suitable for the detection
of rare variants.
Some modifications to the algorithm could be con-
sidered that would further improve its ability to detect
very rare variants. Chromatogram positions in which the
primary and secondary bases are accidentally identical are
currently identified based on the ratio of intensity between
the secondary and primary bases passing some threshold.
However, as sequence quality increases at the very begin-
ning of the sequence and drops toward the ends, a more
dynamic threshold could be developed that better resolves
such identities. A simpler improvement is adding some
warning when simple repeats flank the indel, such as im-
plemented in ShiftDetector, as such repeats are expected
to lead to inaccuracy in indel positioning (20). However,
such an improvement will have impact on CHILD per-
formance only after the impact of shadow sequences is
eliminated, as CHILD currently reports only rough esti-
mates of indel location (as discussed earlier).
Our analysis of controlled indel mixtures revealed that
in some cases, CHILD can resolve indel mixtures even
when one of the deleted species accounts for only 5% of
the mixture. Such sensitivity is sufficient to detect rare
tumor-related indels, splice variants and heteroplasmic
mitochondrial deletions. This level of sensitivity or better
Table 2. The performance of indel detection algorithms with
controlled mixtures of indel constructs
% ? construct primerCHILDShiftDetector Indelligent
Plasmid pMitA ("=51bp)
Plasmid pMitB ("=9bp)
The performance of CHILD, ShiftDetector and Indelligent in resolving
The plasmids pMitA and pMitB (see text) were used to generate chro-
matograms with increasing concentration of the deletion-containing
construct (%? construct), using the M13, T7 or SP6 primers, and re-
peating the entire process three times.
Each chromatogram was analyzed with all three algorithms and the
results are summarized for each replicate (‘-’ indicates no result was
Correct indel size assignments are indicated in bold.
For Indelligent, chromatograms were converted using the Sequencher
package (GeneCodes, Ann Arbor MI), using default parameters.
For ShiftDetector, the significance cutoff was set to 0.0001 to match the
default stringency of CHILD.
Where applicable, multiple indel size predictions are provided as a
of indel-carryingand wild-type
e47Nucleic Acids Research, 2011,Vol. 39,No. 7PAGE 6 OF 8
can be achieved, in principle, in next-generation massive
parallel sequencing efforts (6,10,15). However, standard
Sanger sequencing will most likely continue to be more
cost effective than massive parallel sequencing for
targeted analysis of specific genomic regions/transcripts.
Thus, increasing indel detection sensitivity and accuracy
provides benefits to those researchers not conducting
CHILD offers several advantages over existing tools.
To the best of our knowledge, only three tools can
utilize a single trace to detect rare variants. Indelligent
(18) transforms the chromatogram into a degenerate
(IUPAC) representation and applies a dynamic pro-
gramming algorithm specifically designed to self-align
the resulting degenerate sequences allowing for indels.
This tool provides several descriptive statistics that allow
users to evaluate the proposed indel but does not perform
formal hypotheses testing (e.g. calculating the likelihood
of a given alignment to occur by chance). VarDetect (27)
uses a specialized base-calling algorithm and a matrix rep-
resentation that describes both primary and secondary
base calls. It then applies a search algorithm that utilizes
matrix representation to detect similarity between the two.
Again, no test is conducted to evaluate the statistical sig-
nificance of this similarity. In addition, prior knowledge of
the rare variant concentration is required for a secondary
base call, and the program needs to be locally installed.
For technical reasons, CHILD was not compared to
VarDetect, as we were unable to use it for indel detection
with out data (data not shown). ShiftDetector (20) is the
algorithm most similar to CHILD. It calls primary and
secondary sequences (using PHRED) from the chromato-
gram and uses a special running-window matching algo-
rithm, considering a predefined range of indel sizes,
reporting separately on possible alignments for each
indel size. ShiftDetector does offer a statistical test,
which is based on the binomial distribution, to evaluate
the likelihood of fortuitous indel-like patterns. However,
ShiftDetector does not account for multiple testing or for
sequence composition biases. CHILD was specifically
designed to address the shortcoming of these existing
tools. It uses the well-tested statistical method to reject
program, in which the distribution of fortuitous alignment
scores is estimated by fitting the alignment scores of
shuffled sequences to the extreme value distribution. It is
probably thanks to the strength of the alignment algo-
rithm and the accuracy of the statistical test that
CHILD is more accurate and sensitive in detecting rare
variants than are Indelligent and ShiftDetector (Table 2).
CHILD thus combines an algorithm specifically designed
to handle rare variants with a user-friendly interface
designed for simplicity. Only a single trace file is
required. No additional tools need to be installed and
no parameters need to be adjusted.
To conclude, we present a new experimentally tested
bioinformatics tool for rare indel detection from single
chromatograms, and offer, for the first time, an evaluation
of the sensitivity of this approach. We show that even rare
indels can be detected (as low as 5% of the molecules) by a
friendly tool that is accessible to experimental biologists,
as well as to bioinformaticians.
Supplementary Data are available at NAR Online.
The authors wish to thank the Israel Ministry of
Absorption for a scholarship granted to Ilia Zhidkov.
We thank the anonymous reviewers of the manuscript
for their useful comments.
The National Institute for Biotechnology in the Negev
and by the Israeli Cancer Association. Funding for open
access charge: BGU Research budget 33591766.
Conflict of interest statement. None declared.
1. Bhangale,T.R., Stephens,M. and Nickerson,D.A. (2006)
Automating resequencing-based detection of insertion–deletion
polymorphisms. Nat. Genet., 38, 1457–1462.
2. Ball,E.V., Stenson,P.D., Abeysinghe,S.S., Krawczak,M.,
Cooper,D.N. and Chuzhanova,N.A. (2005) Microdeletions and
microinsertions causing human genetic disease: common
mechanisms of mutagenesis and the role of local DNA sequence
complexity. Hum. Mutat., 26, 205–213.
3. Breitbart,R.E., Andreadis,A. and Nadal-Ginard,B. (1987)
Alternative splicing: a ubiquitous mechanism for the generation of
multiple protein isoforms from single genes. Annu. Rev. Biochem.,
4. Ner-Gaon,H., Halachmi,R., Savaldi-Goldstein,S., Rubin,E.,
Ophir,R. and Fluhr,R. (2004) Intron retention is a major
phenomenon in alternative splicing in Arabidopsis. Plant J., 39,
5. Sammeth,M., Foissac,S. and Guigo,R. (2008) A general definition
and nomenclature for alternative splicing events. PLoS Comput.
Biol., 4, e1000147.
6. Campbell,P.J., Pleasance,E.D., Stephens,P.J., Dicks,E., Rance,R.,
Goodhead,I., Follows,G.A., Green,A.R., Futreal,P.A. and
Stratton,M.R. (2008) Subclonal phylogenetic structures in cancer
revealed by ultra-deep sequencing. Proc. Natl Acad. Sci. USA,
7. Ding,L., Getz,G., Wheeler,D.A., Mardis,E.R., McLellan,M.D.,
Cibulskis,K., Sougnez,C., Greulich,H., Muzny,D.M.,
Morgan,M.B. et al. (2008) Somatic mutations affect key pathways
in lung adenocarcinoma. Nature, 455, 1069–1075.
8. Stratton,M.R., Campbell,P.J. and Futreal,P.A. (2009) The cancer
genome. Nature, 458, 719–724.
9. Chomyn,A. and Attardi,G. (2003) MtDNA mutations in aging
and apoptosis. Biochem. Biophys. Res. Commun., 304, 519–529.
10. He,Y., Wu,J., Dressman,D.C., Iacobuzio-Donahue,C.,
Markowitz,S.D., Velculescu,V.E., Diaz,L.A. Jr, Kinzler,K.W.,
Vogelstein,B. and Papadopoulos,N. (2010) Heteroplasmic
mitochondrial DNA mutations in normal and tumour cells.
Nature, 464, 610–614.
11. Kmiec,B., Woloszynska,M. and Janska,H. (2006) Heteroplasmy
as a common state of mitochondrial genetic information in
plants and animals. Curr. Genet., 50, 149–159.
12. Woodson,J.D. and Chory,J. (2008) Coordination of gene
expression between organellar and nuclear genomes. Nat. Rev.
Genet., 9, 383–395.
PAGE 7 OF 8Nucleic AcidsResearch, 2011, Vol.39,No. 7 e47
13. Lutz-Bonengel,S., Sanger,T., Pollak,S. and Szibor,R. (2004)
Different methods to determine length heteroplasmy within the
mitochondrial control region. Int. J. Legal Med., 118, 274–281.
14. Shendure,J. and Ji,H. (2008) Next-generation DNA sequencing.
Nat. Biotechnol., 26, 1135–1145.
15. Massouras,A., Hens,K., Carine,G., Uplekar,S., Decouttere,F.,
Rougemont,J., Cole,S.T. and Deplancke,B. (2010) Primer-initiated
sequence synthesis to detect and assemble structural variants.
Nat. Methods, 7, 485–486.
16. Chen,K., McLellan,M.D., Ding,L., Wendl,M.C., Kasai,Y.,
Wilson,R.K. and Mardis,E.R. (2007) PolyScan: an automatic
indel and SNP detection approach to the analysis of human
resequencing data. Genome Res., 17, 659–666.
17. Dicks,E., Teague,J.W., Stephens,P., Raine,K., Yates,A.,
Mattocks,C., Tarpey,P., Butler,A., Menzies,A., Richardson,D.
et al. (2007) AutoCSA, an algorithm for high throughput DNA
sequence variant detection in cancer genomes. Bioinformatics, 23,
18. Dmitriev,D.A. and Rakitov,R.A. (2008) Decoding of
superimposed traces produced by direct sequencing of
heterozygous indels. PLoS Comput. Biol., 4, e1000113.
19. Montgomery,K.T., Iartchouck,O., Li,L., Loomis,S., Obourn,V.
and Kucherlapati,R. (2008) PolyPhred analysis software for
mutation detection from fluorescence-based sequence data.
Curr. Protoc. Hum. Genet., Chapter 7, Unit 7 16.
20. Seroussi,E., Ron,M. and Kedra,D. (2002) ShiftDetector: detection
of shift mutations. Bioinformatics, 18, 1137–1138.
21. Tenney,A.E., Wu,J.Q., Langton,L., Klueh,P., Quatrano,R. and
Brent,M.R. (2007) A tale of two templates: automatically
resolving double traces has many applications, including efficient
PCR-based elucidation of alternative splices. Genome Res., 17,
22. Smith,T.F. and Waterman,M.S. (1981) Identification of common
molecular subsequences. J. Mol. Biol., 147, 195–197.
23. Pearson,W.R. (1991) Searching protein sequence libraries:
comparison of the sensitivity and selectivity of the
Smith–Waterman and FASTA algorithms. Genomics, 11, 635–650.
24. Ewing,B., Hillier,L., Wendl,M.C. and Green,P. (1998) Base-calling
of automated sequencer traces using phred. I. Accuracy
assessment. Genome Res., 8, 175–185.
25. Falkenberg,M., Gaspari,M., Rantanen,A., Trifunovic,A.,
Larsson,N.G. and Gustafsson,C.M. (2002) Mitochondrial
transcription factors B1 and B2 activate transcription of human
mtDNA. Nat. Genet., 31, 289–294.
26. Suissa,S., Wang,Z., Poole,J., Wittkopp,S., Feder,J., Shutt,T.E.,
Wallace,D.C., Shadel,G.S. and Mishmar,D. (2009) Ancient
mtDNA genetic variants modulate mtDNA transcription and
replication. PLoS Genet., 5, e1000474.
27. Ngamphiw,C., Kulawonganunchai,S., Assawamakin,A.,
Jenwitheesuk,E. and Tongsima,S. (2008) VarDetect: a nucleotide
sequence variation exploratory tool. BMC Bioinformatics,
9(Suppl. 12), S9.
e47Nucleic Acids Research, 2011,Vol. 39,No. 7PAGE 8 OF 8