Fidelity of capture-enrichment for mtDNA genome
sequencing: influence of NUMTs
Mingkun Li*, Roland Schroeder, Albert Ko and Mark Stoneking
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, D04103,
Received March 13, 2012; Revised April 18, 2012; Accepted May 7, 2012
Enriching target sequences in sequencing libraries
via capture hybridization to bait/probes is an effi-
cient means of leveraging the capabilities of next-
generation sequencing for obtaining sequence data
from target regions of interest. However, homolo-
gous sequences from non-target regions may also
be enriched by such methods. Here we investigate
the fidelity of capture enrichment for complete
mitochondrial DNA (mtDNA) genome sequencing
by analyzing sequence data for nuclear copies of
mtDNA (NUMTs). Using capture-enriched sequenc-
ing data from a mitochondria-free cell line and the
parental cell line, and from samples previously
sequenced from long-range PCR products, we
demonstrate that NUMT alleles are indeed present
in capture-enriched sequence data, but at low
enough levels to not influence calling the authentic
mtDNA genome sequence. However, distinguishing
develop here a computational method to distinguish
NUMT alleles from heteroplasmies, using sequence
data from artificial mixtures to optimize the method.
Next-generation sequencing (NGS) platforms can process
hundreds of thousands to millions of DNA templates in
parallel, thereby providing dramatically faster and cost-
effective sequence throughput compared with traditional
capillary sequencing (1). With this fast-evolving technol-
ogy, whole-genome sequencing is enabled for most organ-
isms and will likely be routine in the future. However,
currently, it is not yet feasible to sequence large numbers
of complex genomes, as the cost and time required are still
prohibitive. Moreover, whole-genome sequencing is not
necessary for many studies that focus on some specific
target region(s) of interest (e.g. specific genes, exons, regu-
latory elements, etc.). In such cases, targeted sequencing is
preferable as most of the sequencing capacity is then
devoted to the genomic region(s) of interest. Although
various target-enrichment methods have been developed
(2,3), it is unclear how homologous sequences from non-
target regions might influence the sequencing results. This
is particularly the case for the capture-enrichment method
(4,5), which makes use of the similarity between a bait/
probe and the target. Investigating this issue is particularly
important for clinical and forensic applications, because
contamination from homologous sequences could be
misidentified as lower level (heterogeneous) mutations.
Mitochondrial DNA (mtDNA) genome sequencing
offers an excellent opportunity to evaluate the contamin-
ation from homologous sequences in capture-enriched se-
quence data. First, sequences homologous to the mtDNA
genome are well-characterized in the nuclear genome
(nuclear mitochondrial DNA inserts or NUMTs) (6–8).
NUMTs vary in length and similarity to the mtDNA
genome; thus, NUMTs may co-enrich with the mtDNA.
Second, a rapid and cost-effective method for capture
enrichment of mtDNA is available (9), which has been
carried out on hundreds of samples in our laboratory
(10; unpublished), providing substantial comparative
data to work with. Third, mtDNA sequencing in general
has been widely carried out and has important biomedical
and forensic applications (11), so it is important to under-
stand the impact of NUMTs on capture-enrichment
mitochondria-free cell lines (which have nuclei but lack
mitochondria) provides a perfect negative control for as-
sessing the impact of NUMTs on capture-enriched
In this study, we first collected all known NUMTs
through a computational search of the nuclear genome
and found many mtDNA positions that could be poten-
tially affected by NUMTs. We evaluated the impact of
NUMTs in capture-enriched sequence data by analyzing
*To whom correspondence should be addressed. Tel: +49 341 3550530; Fax: +49 341 3550555; Email: email@example.com
Published online 30 May 2012Nucleic Acids Research, 2012, Vol. 40, No. 18e137
? The Author(s) 2012. 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/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
such data from a mitochondria-free cell line and the
parental cell line, and by comparing long-range PCR
enriched sequence data (expected to be largely free of
NUMTs) to capture-enriched sequence data for the
same samples. Our results indicate that NUMTs are indeed
captured along with the mtDNA genome. Furthermore,
we evaluated methods for distinguishing true low-level
mutations (i.e. heteroplasmy) from NUMTs by analyzing
artificially mixed samples.
MATERIALS AND METHODS
NUMTs in silico
To predict all NUMTs that exist in the human nuclear
genome, previously published criteria were applied (6).
BLASTn was used to compare the revised Cambridge
reference sequence for the human mtDNA genome with
the human nuclear genome (HG19 excluding mtDNA)
(12,13). All hits with e-score ?0.0001 were kept as
potential NUMTs (see a full list in Supplementary Table
S1). A Perl script was written to retrieve all positions
showing a difference (substitution or indel) between
each NUMT and the mtDNA genome (we call these
mtDNA genome, counts of corresponding NUMTs, and
nucleotides in the mtDNA genome and NUMTs were
All reported human mtDNA polymorphism data were
.mitomap.org) (14). For each identified NUMTs-affected
position, we examined whether the same mutational dif-
ference (occurring between the mtDNA and nuclear DNA
copies) was also observed as an mtDNA polymorphism in
human populations. All of these data can be downloaded
Theposition on the
Whole-mtDNA genome sequencing
DNA from 14 samples was extracted from cheek cell
MtDNA was enriched by an in-solution capture method
and sequenced on the Illumina GAIIx platform (GAIIx;
San Diego, CA, USA) via a multiplex sequencing protocol
(9); the average sequencing coverage (in-target) was 930?
with read length of 76bp (single end). The same DNA
samples were sequenced previously on the same platform
using long-rangePCR products
sequencing libraries (15). Briefly, the mtDNA genome
was amplified in two overlapping products of ?9.7 and
7.3kb and the average sequencing coverage was 1328?
with read lengths of 36 and 76bp (single-end) (15).
Furthermore, whole-genome shotgun sequencing data
from 13 samples(NA18501,
NA18505, NA18507, NA18508, NA18510, NA18511,
NA18517, NA18519, NA18520, NA18522, NA18523)
was retrieved from the 1000 Genomes project (17); the
average sequencing coverage for mtDNA was 1919?
with read lengths of 36bp (single end).
Quality control and genome assembly
First, the base quality score was recalibrated with the IBIS
software using PhiX 174 sequencing data as the training
dataset (18). Reads with more than five bases having a
quality score <15 were removed from further analysis.
The adapter sequences were trimmed, and reads were
(NC012920) and the entire genome (HG19) using the
BWA assembler (19). Single-end reads with identical
alignment positions were retained as most of them are
derived from unique molecules (20). The resulting SAM
output was further filtered by requiring not more than two
mismatches and a minimum mapping quality score of 20.
All bases with a quality score <20 were removed from the
Mitochondria-free (rho zero) cell line
DNA from a mitochondria-free (rho zero) cell line and the
parental cell line from which it was created were kindly
provided by EMD-Millipore Inc. (San Diego, CA, USA).
Genomic libraries were prepared and pooled with other
libraries before hybridization capture (9). To avoid any
possible contamination caused by jumping PCR or
cluster misidentification, indexes were added at both
ends of the template DNA (21), and all reads lacking
the expected double indexes were discarded. These
libraries were prepared in duplicate to investigate the
reproducibility of the results.
Artificially mixed samples
Two mixtures were prepared from genomic DNA, one
from two individuals differing at 34 positions in their
mtDNA genomes, and the other from two individuals
differing at 27 positions. Mixtures were prepared in the
following proportions: 1:1, 1:3, 1:9, 1:19 and 1:39. DNA
concentrations were assessed via qPCR (Mx3005PTM,
Stratagene) using mtDNA-specific primers (22), and the
DNA samples were diluted and mixed in the above pro-
portions in triplicate, and then used for capture enrich-
ment and Illumina GAIIx sequencing as described above.
Distinguishing NUMT alleles from heteroplasmy
To distinguish NUMT alleles from true heteroplasmic
positions, we made use of the DREEP (Detecting
low-level mutations by utilizing the re-sequencing error
profile of the data) software that we developed previously
to distinguish low-level mutations (i.e. heteroplasmy) from
sequencing errors (20). This method assigns a quality score
to the minor allele at each position, which is a Phred-like
value that measures the deviation of the observed minor
allele count from the expect error count derived from a
reference panel. Here, NUMTs were regarded as a special
type of sequencing error, and the quality score assigned by
DREEP then reflects the deviation of the minor allele
count from the expected minor allele count (caused by
sequencing error and NUMTs), derived from a reference
panel. The DREEP software is available at http://dmcrop
e137Nucleic Acids Research, 2012,Vol.40, No. 18PAGE 2 OF 8
NUMTs in silico
We identified 1077 NUMTs in silico from analysis of the
HG19 human reference genome. HG19 is a composite of
sequences from multiple individuals, and 28 of the
identified NUMTs are located on unplaced sequences or
alternative loci. The NUMTs range in length from 34 to
8798bp, with an average of 240bp. The percent sequence
identity to human mtDNA ranged from 78% to 100%. By
comparing the mtDNA reference sequence with the
NUMTs, we found 8239 positions (49.7% of the
mtDNA genome) that could be potentially affected by
NUMT alleles in that the NUMTs possess one or more
alternative nucleotides relative to the mtDNA reference
sequence. The count of alternative NUMTs alleles
mapped to the same mtDNA position ranges from 1 to
46, and >90% of them were identical (Supplementary
Table S2). Moreover, the majority (74%) of alternative
NUMT alleles were not found among modern human
mtDNAs, suggesting that they are either mutations that
arose in the NUMT after the insertion event or insertions
of mtDNA sequences that are no longer present in modern
NUMTs in the mt-free (rho zero) cell line
The amount of endogenous mtDNA in the mt-free (rho
zero) cell line was examined, by comparing the enrichment
for reads mapping to the mtDNA genome after capture
hybridization of sequencing libraries prepared from both
the mt-free cell line and its parental cell line. A large
fraction of the reads from both the mt-free cell line and
its parental cell line mapped to the HG19 human reference
genome (Table 1), while only 11.2% of the reads were
inferred to be duplicate reads, indicating that there was
sufficient endogenous DNA in the libraries for sequenc-
ing. However, ?40% of the reads from the parental cell
line mapped to the mtDNA genome, whereas <0.1% of
the reads from the mt-free cell line mapped to mtDNA
(Table 1). Thus, the mt-free cell line is indeed essentially
devoid of mtDNA. The few reads from the mt-free cell line
that do map to mtDNA could reflect a small amount of
surviving mtDNA, contamination, artifacts such as
jumping PCR, or NUMTs. If the mtDNA genome is
used as the only mapping reference, more reads from the
mt-free cell line would be mapped to the mtDNA
(Table 1), 94.6% of which could also be mapped to
Comparison of the mapping results for the mt-free cell
line versus the parental cell line clearly shows evidence of
NUMTs in the sequence data. When using mtDNA alone
as the reference for mapping, we found hundreds of pos-
itions that differed between the reads mapping to the
mtDNA genome from the mt-free cell line and the
parental cell line (Table 2). Reads mapped to these pos-
itions in the mt-free cell line were thus unlikely to be
derived from the surviving mitochondria as 87% of the
mt-free cell line-specific alleles in such cases were included
in the aforementioned NUMTs database. The count of
these putative observed NUMT alleles in the database is
higher than that of NUMT alleles that were not observed
in the sequence data from the mt-free cell line (7.73 versus
4.62, P<0.001 in Mann–Whitney U-test). Moreover, 88%
of these putative NUMT alleles were also observed in the
parental cell line as minor alleles, with a frequency
between 0.02% and 0.93%. All the above evidence
supports the interpretation that these are indeed reads
coming from NUMTs.
When comparing the discrepant positions between the
mt-free cell line and the parental cell line given by two
independent mt-free cell line sequencing libraries, ?70%
of them overlapped, of which 95% were included in the
NUMTs database. The non-overlapping alleles could be
either rare NUMT alleles (76% were included in the
NUMTs database) or sequencing errors. We then
created another database, called RHO94, that consists of
94 positions that: (i) differed between the true mtDNA
genome sequence of the cell line and the reads obtained
from the mt-free cell line; (ii) were observed in reads from
both libraries from the mt-free cell line; and (iii) were also
observed as minor alleles in the parental cell line
sequencing data. This database is thus expected to
contain NUMT alleles that are especially likely to occur
in capture-enriched sequence data.
The choice of mapping (assembly) reference seems to be
a crucial factor in influencing the amount of discrepant
positions.When using the
sequence (HG19) as the mapping reference rather than
just mtDNA, 90% of the discrepant positions disappeared
(Table 2). However, mapping to the entire genome
sequence causes other problems, as discussed below.
Comparison between long-rang PCR and capture-enriched
Fourteen samples, which were sequenced from long-range
PCR products in our previous study (16), were sequenced
again via the capture-enrichment method here. The ex-
pectation is that the sequence data from long-range PCR
products should be largely free of NUMTs, as only single
Table 1. Mapping results for the mt-free cell line and the parental
aBoth cell lines were sequenced twice (76-bp paired-end reads with
double indexes), from independent sequencing libraries. RHO, mt-free
cell line, WT, parental cell line.
cPercentage of reads mapped to mtDNA when using the entire genome
(nuclear DNA+mtDNA) as the mapping reference (number of reads in
dPercentage of reads mapped to mtDNA when using only the mtDNA
genome as the mapping reference (number of reads in parentheses).
PAGE 3 OF 8Nucleic Acids Research, 2012,Vol.40, No. 18 e137
bands of the expected size were observed after gel electro-
phoresis of the PCR products. With mtDNA as the
mapping reference, the two methods gave the same
major allele at all positions, suggesting there were indeed
more reads derived from mtDNA than that from the
NUMTs with alternative alleles in the capture-enriched
sequence data. Before comparing the minor alleles
detected by the two methods, a quality filter was applied
to remove sequencing errors, in which all minor alleles
with frequency <1% on any strand were discarded.
After quality filtering, the capture-enrichment method
gave twice as many minor alleles as the long-range PCR
method (406 versus 250, see details in Supplementary
Table S3), with only 12 minor alleles detected by both
methods. There were significantly more minor alleles in
the NUMT databasefrom
method than from the long-range PCR method (50%
versus 23%, P=1.08?10?11, Fisher’s exact test), as
well as in the RHO94 database (16% versus 3%,
P=7.44?10?8, Fisher’s exact test). This suggests that
indeed NUMTs are enriched by the capture-enrichment
method. If we exclude all minor alleles found in the
NUMTs database, then the two methods give similar
numbers of minor alleles (192 versus 204). Presumably
these minor alleles reflect sequencing errors not removed
by the quality filter, contamination, true NUMTs missing
from the database and/or heteroplasmies. The average fre-
quency of minor alleles from the capture-enrichment data
that were found in the RHO94 database was 2.6±3.3%
(Supplementary Table S4).
The number of minor alleles detected in the reads from
the shotgun library was much less than the number of
minor alleles detected in reads from the long-range PCR
or capture-enriched libraries (Table 3). This could reflect
cross-contamination and/or index misidentification that
occurred during handling/sequencing multiple samples in
one library, as observed previously (21). Conversely, the
shotgun library had the highest proportion of NUMT
alleles among all minor alleles (85%, compared to
23.6–49.9% for the other methods), indicating the enrich-
ment efficiency is indeed much higher for mtDNA than for
When using the entire genome as the mapping reference,
the proportion of NUMT alleles in the capture-enriched
data was significantly reduced from 50% to 19% (P=
1.74?10?14, Fisher’s exact test) and became equivalent
to that of the long-range PCR method (19% versus
22%, P=0.510, Fisher’s exact test). However, at the
same time, there were 39 positions whose major alleles
differed from the consensus sequence called when the
mtDNA genome alone was used as the mapping reference.
For 31 of these 39 positions, the consensus allele differed
from the reference mtDNA but was identical to a NUMT.
These positions have extremely low coverage (<10?), with
>90% of the reads that mapped to mtDNA filtered out
because they could be better mapped to the nuclear DNA
genome. This problem was also observed with the
long-range PCR method and the shotgun method
(Figure 1). The positions with major alleles that changed
depending on the reference genome tend to occur in
several regions along the mtDNA, such as positions
4769, 8860 and 7256, all of which have very similar
The reduction in reads not only can lead to the wrong
consensus allele, but can also cause gaps and reduce the
power to detect low-level mutations (heteroplasmy). With
36-bp single-end data, 36% of the reads would be removed
in total, while 16% of the positions in the mtDNA genome
would not have any mapped reads and 35% of the pos-
itions would lose more than half of the mapped reads.
Longer reads and paired-end information does help
to reduce the loss of reads (Supplementary Figure S1);
for instance, no gap was observed when using 76-bp
paired-end reads, but 7% of the reads were still discarded
and 5% of the positions lost at least half of the mapped
Table 2. Reads mapping to potential NUMTs in sequence data from the mt-free cell line and the parental cell line
Major mt-free allele=Major parental alleleMajor mt-free allele6¼Major parental allele
Minor mt-free allele=
Minor parental allele
Minor mt-free allele6¼
Minor parental allele
Major mt-free allele=
Minor parental allele
Major mt-free allele6¼
Minor parental allele
Comparison Ref POSa
AbsentPresent AbsentPOS PresentAbsent PresentAbsent
RHO1 versus WT1 MT 9061
RHO2 versus WT2 MT15
aNumber of positions in the mtDNA genome included in reads from the mt-free cell line.
bPresent means the mt-free allele is present in the NUMTs database and absent means the mt-free allele is not in the NUMTs database.
Table 3. Minor allele profile in different sequencing libraries (after
quality filter) mapped with either the mtDNA genome (MT) or entire
genome (HG19) as the mapping reference
e137 Nucleic Acids Research, 2012,Vol.40, No. 18PAGE 4 OF 8
Inferring low-level mutations (heteroplasmy)
from capture-enriched sequence data
Although most of the reads from capture-enriched libraries
that mapped to mtDNA were authentic mtDNA reads, the
existence of reads coming from NUMTs must be con-
sidered when attempting to infer heteroplasmy. To inves-
tigate difficulties that might arise in distinguishing
heteroplasmy from NUMTs, we created a series of artifi-
cially mixedsamplesto mimic differentlevels of
Figure 1. Correlation between read loss and major allele frequency change. Read loss was calculated as the percentage of reads that could be
mapped when using the mtDNA genome as the reference (mapping quality score ?20) but discarded when mapped to the entire genome (mapping
quality score <20). Major allele frequency change was calculated as the frequency change of the correct allele (defined as the allele obtained when
using mtDNA as the reference). Each dot represents one position in the mtDNA sequence of one of 14 samples (13 samples for the shotgun data).
Blue dots indicate positions with consensus alleles that are not included in the NUMTs database; color intensity is proportional to the number of
dots. Red dots represent positions with consensus alleles that differ from the reference mtDNA and are the same as a NUMT allele. The circled
dots indicate positions whose major alleles changed when mapping to different reference genomes (mtDNA alone versus the entire genome).
(A) long-range PCR data; (B) capture-enriched data; (C) shotgun data.
PAGE 5 OF 8Nucleic Acids Research, 2012,Vol.40, No. 18 e137
heteroplasmy (see details in ‘Materials and Methods’
section), which were then sequenced to an average
coverage of 2824?.
First, different minor allele frequency (MAF) thresh-
olds were applied to remove the NUMT alleles and
sequencing errors. With a requirement of a minimum
MAF of 0.01 on both strands, the false-negative error
rate (FN) was 0.7% (6 out of 888 mixed positions were
missed), while the false-positive rate (FP) was 11.1% (98
out of 882 detected mixed positions were false positives)
when reads were mapped to the mtDNA genome. In
contrast, mapping the reads to the entire genome
increased the FN to 9.0%, while no change was
observed in the FP (11.0%). However, the percentage of
NUMT alleles in the FP was reduced from 57.1% with the
mtDNA genome as the only mapping reference to 25.8%
with the entire genome as the mapping reference
(P=0.006, Fisher’s exact test). Using the entire genome
as the mapping reference thus helps eliminate reads
mapping to NUMTs. In order to filter out all false posi-
tives, a minimum MAF of 0.055 on both strands is
needed, which then results in FN=38.9%, and 93.5%
of the heteroplasmies with MAF of 2.5–5% would be
lost. In contrast, when using the entire genome as the
mapping reference, a minimum MAF of 0.02 would
remove all false positives with FN=15.9% and only
29.2% of the heteroplasmies with MAF of 2.5%–5%
would be lost. However, as before, using the entire
genome as the mapping reference results in gaps (4.7%
of the mtDNA) due to loss of reads assigned to the
mtDNA genome; 6.6% of the heteroplasmies (in each
level) were lost as they were located in such gaps.
To filter out NUMT alleles for the analysis of
heteroplasmy, a possible solution is to make use of the
population sequencing data. The underlying principle is
that data generated by the same capture-enrichment
protocol from different individuals should be similarly
influenced by NUMTs. Thus, NUMTs alleles should be
observed more often and with higher frequencies than true
heteroplasmies, and hence data from other samples may
help to identify these NUMT alleles. Here, 2272 samples
sequenced with the same protocol and on the same
platform were used as the reference, and quality scores
assigned by DREEP (DQS) were used to reflect the
relative magnitude of the MAF compared with the refer-
ence panel. The FN and FP were calculated under differ-
ent thresholds of DQS and MAF (Figure 2). When using
mtDNA as the mapping reference, DQS performed better
than MAF, as the DQS threshold that resulted in no false
positives had lower FN values compared with that of the
MAF threshold, whereas the opposite trend was observed
when using the entire genome as the mapping reference
(Figure 2). The best threshold combination (giving no
false positives and the lowest FN) when using mtDNA
alone as the reference (MAF?0.015, DQS?4) gave a
much lower FN than obtained when using the entire
genome as the reference (MAF?0.02) (6.8% versus
15.9%). A lower FN was still obtained when a more strin-
gent threshold was applied under the first mapping
Moreover, this strategy performed better than others for
most mixture levels (Table 4).
Although genome sequencing is now feasible for most or-
ganisms, targeted sequencing of specific genomic regions is
preferred by many studies (2,3), either because it is more
cost-effective to sequence more samples, or because
specific targets are of interest. Many methods have been
developed to enrich desired target sequences from
sequencing libraries (2,3,9); in general, these methods
either make use of specific primers to amplify the
targeted region, or the segments of interest are enriched
by hybridization to complementary probes/baits.
Both enrichment strategies have been applied in
mtDNA genome sequencing. Compared to shotgun
libraries, mtDNA sequences were enriched 391-fold by
long-range PCR and 155-fold by the in-solution capture
method (Supplementary Table S5). Although PCR has
long been regarded as the gold standard due to its
higherspecificity and reproducibility,
quality is an issue (e.g. with degraded DNA templates),
the capture-hybridization method is much faster, cheaper,
easier to use and more efficient. However, a concern of the
capture-hybridization method is that NUMTs could be
whereas NUMTs are not expected to amplify in the
long-range PCR protocol (23). Indeed, the results of this
study demonstrated the existence of NUMTs in the
capture-enriched sequence data, as identified by compari-
son to the NUMTs database that we constructed. It is
quite probable that additional NUMTs are actually rep-
resented in the sequence data, as there could exist poly-
morphic NUMTs absent in the reference sequence, or
NUMTs present in the human reference genome that
were not detected by our in silico analysis. Despite the
existence of NUMT-derived sequences in the capture
sequence data, by examining the alternative NUMTs
allele frequency in the mt-free and parental cell lines and
in sequence data for 14 samples enriched by both
long-range PCR and by capture hybridization, we found
that NUMT alleles usually had a frequency <5%. Thus all
capture-enriched sequence data gave the same mtDNA
genome consensus sequences as that given by the
long-range PCR products, and in general, we do not
expect NUMTs to interfere with determining the authentic
mtDNA genome sequence from capture-enriched data.
However, as shown by the mt-free cell line data
(Table 2), NUMTs could have a significant influence on
the consensus sequence calling when the mitochondria
become extremely rare. It would be interesting to test
various types of samples (with different DNA concentra-
tions, degradation status and ratio of mtDNA/nuclear
DNA), to obtain an overall view of the impact of
NUMTs on calling consensus mtDNA sequences.
Moreover, a bigger challenge is distinguishing NUMT
alleles from true low-level mutations (heteroplasmy), as
NUMT alleles and true heteroplasmies have the same
mapping profile. To distinguish them, the most straight-
forward way would be to discard all minor alleles included
e137Nucleic Acids Research, 2012,Vol.40, No. 18PAGE 6 OF 8
in the NUMTs database. However, we showed in this
study that not all putative NUMT alleles are included in
the database; moreover, some putative NUMT alleles
could nonethelessbetrue heteroplasmies.Another
approach would be to use the entire genome as the refer-
ence for mapping, as reads from NUMTs would be
mapped to both mtDNA and the nuclear genome and
could thus be filtered out by requiring a minimum
mapping quality score. However, we showed that authen-
tic mtDNA reads would be discarded by this approach
and result in sequencing gaps and even inaccurate
calling of the consensus mtDNA genome sequence. A
third approach would be to apply a higher MAF threshold
to remove the NUMT alleles, but this results in a high
false-negative rate: in our artificially mixed samples
(with coverage of 2824?), a MAF of 5.5% is needed to
filter out all the false positives, but 38.9% of the mixed
positions would be missed by this criterion.
We therefore developed and investigated a method that
makes use of a statistic that reflects the magnitude of the
observed MAF relative to the expected frequency distri-
bution (obtained from a reference panel). The DQS
quality score given by DREEP was chosen for this
Figure 2. False-negative rates and false discovery rates under different thresholds of MAF, DQS. (A and C) False-negative rate; (B and D)
false-positive rate. A and B are results when using mtDNA as the mapping reference; C and D are results when using the entire genome (HG19)
as the mapping reference. Empty bins in B and D represent no false positives. Basic thresholds used here are as follows: coverage ?100; minor allele
count ?3 on each strand; minor allele count (number of distinct reads) ?3 on each strand; and position is not located in C-stretch or STR regions
(303–315, 512–525, 16 181–16 195).
Table 4. False-negative rates under different mapping strategies and
different mixture levels
Ref. Mixture level
0.5 0.250.1 0.05 0.025All levels
MT000 0.033 0.639 0.138
HG19 0.066 0.0660.066 0.0820.508 0.159
aAll thresholds result in no false positives.
DQS: DREEP quality score.
PAGE 7 OF 8 Nucleic Acids Research, 2012,Vol.40, No. 18e137
purpose, as it represents the likelihood of the observation Download full-text
given the reference minor allele distribution. By applying
thresholds to both MAF and DQS to the sequencing data
from the artificial mixtures, we could achieve a much
lower false-negative rate (6.8%) with no false positives.
This method performed better than any others tested
(Table 4). With this method, we can detect almost all
mixtures with MAF ?5% and half of the mixtures with
MAF=2.5%, with no false positives; the sensitivity could
be further improved with higher sequencing depth.
In conclusion, we have demonstrated that NUMTs are
present in capture-enriched sequence data, but not at a
high enough level to interfere with calling accurate con-
sensus mtDNA genome sequences. However, NUMTs
could interfere with accurate detection of heteroplasmic
mutations, and we have developed and tested a method
for distinguishing NUMTs from true heteroplasmic pos-
itions in mtDNA sequence data. Given that accurate de-
tection of heteroplasmies or other low-level mutations
(e.g. arising from mixed samples) is important for some
clinical or forensic applications of mtDNA analysis, we
advocate that NUMT detection should be part of the
data processing in such studies.
Supplementary Data are available at NAR Online:
Supplementary Tables 1–5 and Supplementary Figure 1.
The sequencing data are publicly available from the
European Nucleotide Archive Sequence Read Archive
(http://www.ebi.ac.uk/ena/) through accession number
We thank EMD-Millipore Inc. (San Diego, CA, USA) for
kindly providing the rho zero and parental cell lines,
T. Maricic for help discussion and comments on the
manuscript, and the MPI-EVA sequencing group and
M. Kircher for technical support.
Funding for open access charge: The Max Planck Society.
Conflict of interest statement. None declared.
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