Copyright Ó 2009 by the Genetics Society of America
`DOI: 10.1534/genetics.109.103218
`
`High-Throughput Multiplex Sequencing to Discover Copy Number
`Variants in Drosophila
`
`Bryce Daines,*,1 Hui Wang,*,1 Yumei Li,*,† Yi Han,† Richard Gibbs*,† and Rui Chen*,†,2
`*Molecular and Human Genetics and †Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030
`Manuscript received March 30, 2009
`Accepted for publication June 1, 2009
`
`ABSTRACT
`Copy number variation (CNV) contributes in phenotypically relevant ways to the genetic variability of
`many organisms. Cost-effective genomewide methods for identifying copy number variation are necessary
`to elucidate the contribution that these structural variants make to the genomes of model organisms. We
`have developed a novel approach for the identification of copy number variation by next generation
`sequencing. As a proof of concept our method has been applied to map the deletions of three Drosophila
`deficiency strains. We demonstrate that low sequence coverage is sufficient for identifying and mapping
`large deletions at kilobase resolution, suggesting that data generated from high-throughput sequencing
`experiments are sufficient for simultaneously analyzing many strains. Genomic DNA from two Drosophila
`deficiency stocks was barcoded and sequenced in multiplex, and the breakpoints associated with each
`deletion were successfully identified. The approach we describe is immediately applicable to the
`systematic exploration of copy number variation in model organisms and humans.
`
`STRUCTURAL variation is known to contribute
`
`extensively to the genetic variability of humans,
`mammals, and many model organisms. One class of
`structural variant, termed copy number variation (CNV),
`includes deletions, duplications, insertions, and genomic
`rearrangements which affect the number of occurrences
`of a specific DNA sequence present in the genome
`(Redon et al. 2006). CNV is known to occur extensively
`in the Drosophila genome with functionally significant
`consequences (Bridges 1936; Dopman and Hartl 2007;
`Tibshirani and Wang 2008; Zhou et al. 2008). In one
`study of 15 Drosophila strains, as many as 10% of genes
`were observed to harbor CNVs (Emerson et al. 2008).
`Cryptic CNVs that affect the phenotype observed in a
`model organism have the potential to confound research
`on multiple levels. For example, a recent report indicates
`that terminal deletions on chromosome (chr) 2L are
`frequent among deficiency kit stocks with mutations on
`the second chromosome and that the associated deletion
`of lgl has distorted the results of several previous studies
`(Roegiers et al. 2009). Despite widespread existence of
`CNV, the biological consequences of this phenomenon
`remain largely unexplored due to the lack of efficient
`tools for detection and characterization.
`Until recently, comparative genomic hybridization
`with whole-genome tiling arrays (array-CGH) was the
`
`Supporting information is available online at http://www.genetics.org/
`cgi/content/full/genetics.109.103218/DC1.
`1These authors contributed equally to this work.
`2Corresponding author: Human Genome Sequencing Center, Baylor
`College of Medicine, 1 Baylor Plaza, Houston, TX 77030.
`E-mail: ruichen@bcm.tmc.edu
`
`Genetics 182: 935–941 (August 2009)
`
`primary method for characterizing CNVs (Carter 2007);
`however, several limitations for this platform reduce its
`efficacy and efficiency. First, cross-hybridization and
`reliance on intensity scores lead to data that are difficult
`to interpret. Second, custom array design and optimi-
`zation is labor intensive and costly. Third, array-CGH
`methods can only detect CNV, not other complex
`rearrangements such as balanced translocations and
`inversions. Finally, the overall cost of array-CGH meth-
`ods is relatively high, particularly when high-resolution,
`whole-genome tiling arrays are employed.
`Direct sequencing using next-generation technology
`has several advantages that make it a potentially powerful
`alternative to array-CGH for identifying genomic struc-
`tural variations, including deletions, duplications, and
`rearrangements (Campbell et al. 2008; Chiang et al.
`2009). First, high-throughput sequencing methods over-
`come the inherent limitations of cross-hybridization and
`provide a digital count of sequence representation.
`Second, no prior knowledge or design work is necessary.
`Third, using paired-end sequencing it is possible to iden-
`tify complex structural variations. Finally, the current cost
`of CNV discovery by sequencing is comparable or lower
`than that of array-CGH and is continuing to decline.
`In this report, we describe a sequencing-based strategy
`for high-throughput, cost-effective, genomewide character-
`ization of structural variation at fine resolution by employ-
`ing the Illumina sequencing platform. Deletions in three
`deficiency fly stocks were successfully characterized and the
`associated breakpoints were accurately determined. As we
`demonstrate, high-throughput sequencing provides an
`ideal and cost-effective platform for CNV characterization.
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`MATERIALS AND METHODS
`
`Fly stocks: Fly stocks were raised on standard Drosophila
`media at room temperature (23°–25°). The dac4 deletion
`mutant was generated by X-ray mutagenesis on the b pr c px sp
`background and obtained from Graeme Mardon (Mardon
`et al. 1994). All other stocks used in this report are from the
`Bloomington Drosophila Stock Center and are described on
`FlyBase (http://flybase.org/reports/FBst0003779.html). The
`genotypes of stock no. 3779 and no. 7584 are described
`as Df(2L)Sd37/SM5 and w1118; Df(3L)Exel6105, P{XP-U}
`Exel6105/TM6B, Tb1, respectively. Df(2L)Sd37 was generated
`by X-ray mutagenesis and is cytologically described as a
`deletion between 37C6-37D1; 38A6-38B2 (Ganetzky 1977;
`Stathakis et al. 1995). Df(3L)Exel6105 was generated by
`recombination between two FRT bearing insertions resulting
`in a molecularly defined deletion 3L: 5359162, 5601375
`(Parks et al. 2004). DNA used for genomic sequencing from
`these strains was obtained from flies heterozygote for the Df
`over the respective balancer chromosome. Wild type referred
`to in this manuscript is the w1118 strain obtained from the
`Bloomington Drosophila Stock Center. DNA used for genomic
`sequencing was obtained from male adult flies.
`Sequencing: Fly genomic DNA was prepared and sequenced
`using the Illumina Genome Analyzer according to previously
`described methodologies (Srivatsan et al. 2008). Sequence
`reads obtained were mapped to the Drosophila reference
`genome release 5.1 using the vendor provided Eland pipeline.
`Barcoding for multiplex sequencing: Solexa sequencing
`primers were modified by the addition of 3 bp (2 of which are
`unique) for the sequencing in multiplex experiments de-
`scribed in this report. These modified primers were used in
`library preparations such that the 59 ends of sequencing
`products from each sample were standardized with a specific
`dinucleotide indicating their sample membership. Following
`multiplex sequencing, reads were separated in silico by a script
`that identified the leading dinucleotide tag, grouped the
`sequence products according to sample membership, and
`trimmed the barcode.
`CNV analysis simulation: Computer simulations were per-
`formed in which the dac4 sequencing reads were randomly
`sampled to generate data sets approximating various levels of
`sequencing coverage. Data sets were generated for 0.45x,
`0.35x, 0.25x, 0.15x, 0.075x, 0.0375x, and 0.01875x with seven
`replicates each. In simulations CNV was determined by DNA
`copy, an R implementation of the circular binary segmenta-
`tion algorithm, which was found to be highly specific and
`accurate on the basis of self-vs.-self tests and discovery of the
`dac4 breakpoints (Olshen et al. 2004; Venkatraman and
`Olshen 2007). To determine the effect of read coverage on
`the ability of deletion detection and breakpoint mapping,
`CNV analyses of these data sets were performed at 1-kb and
`3-kb average window sizes.
`Validation of breakpoints: PCR primers were designed
`flanking the breakpoints predicted by CNV analysis (Rozen
`and Skaletsky 2000). Amplified products were sequenced by
`traditional Sanger sequencing and subsequently mapped to the
`Drosophila reference genome to identify the molecular posi-
`tion of breakpoints. For primers used in this study, see Table S2.
`Additional methods: See File S1.
`
`RESULTS
`
`Characterization of the dac4 deletion mutant by
`direct shotgun sequencing: To test the efficiency and
`accuracy of mapping chromosomal deletions in Dro-
`sophila by high-throughput sequencing, we set out to
`
`identify the breakpoints for an existing deletion. The
`dac4 deletion was generated by X-ray mutagenesis and
`was mapped by analysis of polytene chromosomes to the
`35F–36A region that includes the dac gene (Mardon
`et al. 1994). To molecularly characterize the dac4 de-
`letion, genomic DNA from dac4/CyO flies was sequenced
`using the Solexa genome analyzer (see materials and
`methods). A total of 2.4 million 36-bp-long reads were
`obtained, which could be mapped uniquely to the
`Drosophila reference genome by the standard eland
`pipeline for 0.48x sequencing coverage on the basis of
`a genome size of 180 Mb. The average read coverage in
`nonoverlapping 10-kb windows across the entire 2L
`chromosome arm is relatively uniform while a drop in
`the coverage around the dac region is evident (Figure
`1A). Oscillation in the coverage likely results from both
`system biases and variation due to random sampling.
`Sources of system bias include variable mappability of
`genomic regions and representation biases from library
`preparation and sequencing protocols.
`To estimate the system bias and establish a reference
`coverage map, deep sequencing of wild-type Drosophila
`genomic DNA was performed generating 32 million 36-
`bp-long reads, amounting to 6.4x sequencing coverage.
`We reasoned that with this depth of sequencing, most of
`the oscillation in read coverage would be the result of
`system bias. As expected, read coverage for wild-type
`DNA is similar to that of the dac4 mutant with the
`exception of the dac gene region (Figure 1B). To reduce
`oscillation due to system bias, a set of variably sized bins
`were determined, which divide the reference genome
`into pieces of unequal length, each containing a fixed
`number of wild-type reads (Campbell et al. 2008). Reads
`from dac4 DNA were partitioned into these variably sized
`bins and the read coverage calculated. Variation in
`coverage is significantly reduced by this transformation
`with the putative deletion region becoming the only
`significant drop on the dac4 2L chromosome arm
`(Figure 1C). A significant drop in coverage is observable
`at both ends of the putative deletion and remains low
`throughout the dac4 region (Figure 1D).
`CNV analysis of dac4 deletion heterozygotes precisely
`defines deletion breakpoints: To analyze copy number
`across the 2L chromosome and identify deletion break-
`points in the dac4 deletion genome, a variety of algo-
`rithms that have been developed for CNV discovery
`with array-CGH data were tested (http://compbio.med.
`harvard.edu/CGHweb) (Hupe et al. 2004; Olshen et al.
`2004; Eilers and de Menezes 2005; Picard et al. 2005;
`Willenbrock and Fridlyand 2005; Fiegler et al. 2006;
`Marioni et al. 2006; Carter 2007; Venkatraman and
`Olshen 2007; Yu et al. 2007; Lai et al. 2008a,b;
`Tibshirani and Wang 2008). The performance of these
`algorithms was first assessed with self-vs.-self data sets
`derived from wild-type sequencing data (see materials
`and methods). Random samples of 2.4 million reads
`of wild-type sequences were partitioned as described
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`Multiplex Sequencing CNV
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`Figure 1.—Sequencing cover-
`age identifies the dac4 deletion re-
`gion. Distribution of
`sequence
`coverage for the 2L chromosome
`arm of dac4 and wild-type flies. Cov-
`erage is measured as the number
`of reads counted in each bin
`and plotted along the length of
`the
`chromosome
`by position
`in megabases
`from 11 Mb to
`18.5 Mb. (A) Sequencing coverage
`of dac4/CyO with fixed bins of
`10 kb in length. (B) Sequencing
`coverage of w1118 flies with fixed
`bins of 10 kb in length (read count
`is normalized to dac4). (C) Se-
`quencing coverage of dac4/CyO
`heterozygotes with variably sized
`bins of mean size 10 kb minimizes
`biases attributable to mappability
`and sequencing (see materials
`and methods). (D) Sequencing
`coverage of dac4/CyO heterozy-
`gotes with variably sized bins of
`mean size 10 kb, a sharp drop in
`read coverage at the boundaries
`of
`the dac4 deletion region is
`evident.
`
`above, in this way no regions of CNV are expected.
`Interestingly, some of the algorithms demonstrated
`high levels of sensitivity to oscillations in the wild-type
`data and identified extensive regions of potential copy
`change (Figure 2A and see supporting information,
`Figure S1). All algorithms were then used to analyze the
`dac4 data for copy change at a resolution of 1 kb and
`the consensus of the algorithms used to determine the
`final prediction. The most significant prediction was a
`deletion on chromosome 2L, with breakpoints occur-
`ring in the 1-kb windows whose midpoints are
`16,376,526 and 16,638,211 on chromosome 2L (Figure
`2B and see Figure S2). To validate these predictions,
`PCR primers flanking the predicted breakpoints were
`designed and a DNA fragment was amplified that spans
`the junction. The junction fragment was then se-
`quenced and the breakpoints mapped to 16,376,738
`and 16,638,995 bp position on chromosome 2L consis-
`tent with CNV prediction (Figure 2, C and D). As often
`occurs with X-ray-generated deletions, the breakpoints
`of the dac4 deletion are not ligated together, but are
`separated by a 320-bp sequence. Portions of this 320-bp
`map in small blocks to multiple chromosomes making it
`difficult to infer the origin of the inserted sequence.
`Low read coverage is sufficient for CNV detection:
`The cost for CNV detection using the high-throughput
`sequencing platform is proportional to the number of
`reads required for the analysis. Using the reads obtained
`
`from the dac4 deletion flies, a series of computer
`simulations were performed to determine the effect of
`read coverage on the ability to detect deletions and map
`associated breakpoints (see materials and methods).
`CNV analyses of these data sets were performed at 1-kb
`and 3-kb resolution. At 1-kb resolution the dac4 deletion
`was identifiable across all levels of coverage greater than
`0.04x or 200,000 reads. At 3-kb resolution the dac4
`deletion was identified in most replicates at 0.02x
`sequence coverage or 100,000 reads or greater. These
`results suggest that large CNVs can be detected even
`with extremely low depth of sequencing coverage. We
`also observed that decreasing the read coverage has a
`strong effect on the accuracy of breakpoint detection.
`For example, when coverage is below 0.1x there is a
`great deal of error in detection of the dac4 deletion
`breakpoints (Figure 3, A and B). However, when
`coverage exceeds 0.1x, the predicted breakpoints are
`highly consistent and accurate across simulations. From
`these results we concluded that the reads generated by
`one lane of Solexa sequencing are more than sufficient
`to analyze CNVs for multiple genomes at high resolu-
`tion or that 500,000 reads should be sufficient to
`identify and accurately characterize the breakpoints of
`large deletions with high resolution.
`A barcode system to enable multiplex sequencing:
`To allow the simultaneous interrogation of multiple
`genomes, we developed a barcoding system for multi-
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`B. Daines et al.
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`Figure 2.—CNV analysis of dac4 reveals copy
`loss region. Log2 ratio scores generated for the
`2L chromosome arm from dac4/CyO and w1118
`were analyzed as described in materials and
`methods. The consensus that identifies losses
`and gains on the basis of the chosen algorithms
`is depicted as ‘‘consensus.’’ (A) CNV analysis of
`wild-type coverage. No regions of copy loss are
`identified in the wild-type data for dac4 deletion
`region. (B) CNV analysis of dac4 coverage. A large
`region of low log2 score is identified on chr 2L
`from 16.376 to 16.638 Mb by the consensus
`prediction interpreted as copy loss. Junction
`PCR using primers flanking the predicted dele-
`tion breakpoints amplify a fragment and identify
`the breakpoints. (C) Trace of left breakpoint
`identifies position 16,376,738 as the left break-
`point. (D) Trace of right breakpoint identifies
`position 16,638,995 as the right breakpoint.
`
`plex Solexa sequencing. Criteria for an effective barcod-
`ing system include the ability to accurately differentiate
`reads from each sample postsequencing and relatively
`equal representation of samples among the sequenced
`reads. By differentially ligating modified primers during
`genomic library preparation, we enabled the in silico
`separation of samples postsequencing. We designed
`primers to test all 16 dinucleotide combinations for
`their efficiency in library preparation protocols. Barc-
`odes were then further tested by multiplex microbial
`sequencing. Two microbes, Escherichia coli and Rhodo-
`bacter were used to assess the accuracy of the barcoding
`and multiplex procedures. The DNA of each microbe
`was differentially labeled with barcoded oligonucleotide
`adapters and libraries were mixed in equal molar
`amounts and sequenced simultaneously on one lane
`of the Illumina genome analyzer. E. coli was labeled with
`a TT barcode and Rhodobacter with an AC barcode. Of
`4.1 million total reads produced in one experiment
`designed to test these tags, 95% of generated sequences
`were tagged, and both tags were represented in nearly
`equal proportions (see Table S1). Furthermore, reads
`exhibited an extremely low error/cross-contamination
`rate. About 0.24% of the TT-labeled reads map to
`Rhodobacter while 0.43% of AC-labeled reads map to
`E. coli. From these results we concluded that the barcode
`system is sufficiently specific and can be applied to the
`simultaneous sequencing of multiple deficiency stocks.
`
`Multiplex sequencing of deficiency stocks: To test
`the applicability of multiplex sequencing to the de-
`tection of copy number change in Drosophila, two
`deficiency fly stocks were selected from the Blooming-
`ton deficiency kit. Df(3L)Exel6105 (Parks et al. 2004)
`and Df(2L)Sd37 (Ganetzky 1977) map to different
`chromosome arms and were multiplex sequenced as a
`proof of concept. Genomic DNA from male adult flies
`heterozygote for the deficiency and stock balancer
`chromosomes was
`tagged with different barcoded
`adapters and then mixed and sequenced simulta-
`neously on one lane of the Illumina genome analyzer
`(see materials and methods). Following sequencing
`and in silico separation of the samples, 900,000 and
`600,000 uniquely mappable reads were obtained for
`Df(3L)Exel6105 and Df(2L)Sd37, respectively. CNV anal-
`ysis was then performed on both data sets as described
`above.
`CNV was successfully detected in both deficiency
`DNAs at the expected regions. Df(3L)Exel6105 was
`generated by recombination between two distinct FRT
`bearing insertion sites resulting in a 242-kb deletion
`with molecularly defined breakpoints of 5,359,162 and
`5,601,375 bp (Parks et al. 2004). Consistent with this,
`CNV analysis identified a deletion on chromosome 3L
`with breakpoints occurring in 3-kb windows whose
`midpoints were 5,360,035 and 5,600,802 (Figure 4A and
`see Figure S3). Therefore, not only was the deletion
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`Multiplex Sequencing CNV
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`19,183,957 and 19,185,709 bp, and the pr gene, whose
`location is at chromosome 2L between 20,073,714 and
`20,075,479 bp. In addition, Df(2L)Sd37 fails to comple-
`ment mutations in the RanGap gene, which is located on
`chromosome 2L between 19,442,041 and 19,447,322 bp
`(Ganetzky 1977; Pentz et al. 1990; Stathakis et al.
`1995). Recovery of the molecular breakpoints by junc-
`tion PCR with primers flanking the proposed break-
`points was unsuccessful. P-element
`stocks whose
`insertions flanked the predicted deletion junction were
`used to test for the presence or absence of genomic
`DNA on the deletion chromosome when heterozygote
`over the insertion. Because the insertion disrupts
`successful amplification of genomic DNA on the in-
`sertion chromosome, failure or success to amplify the
`PCR product can be interpreted as absence or pres-
`ence of genomic DNA on the deletion chromosome.
`Results from these analyses support the breakpoint
`windows predicted by CNV analysis and indicate that
`prediction accuracy was within 3 kb (see Figure S5 and
`Table S2).
`
`DISCUSSION
`
`We have reported a strategy for rapid, cost-effective,
`high-throughput, genomewide characterization of CNV
`using next generation sequencing technology. On the
`basis of our results, large CNVs can be identified and
`mapped with high resolution. Both computer simula-
`tion and experimental studies indicate that low levels of
`sequence coverage (,0.1x sequencing coverage or
`458,000 reads) are sufficient
`for identifying and
`mapping large CNVs at kilobase resolution. For the
`characterization of smaller CNVs, such as those in the
`kilobase range, we estimate that deeper sequencing is
`required: approximately 4–5 million reads or 1x se-
`quence coverage. Although only tested in Drosophila,
`the strategy is generally applicable to all organisms.
`The accuracy and resolution to which chromosomal
`deletions and breakpoints can be mapped by our
`platform is very high. Using the Drosophila deficiency
`dac4 as a test case, the breakpoints of the deletion were
`mapped to 1-kb resolution, which were subsequently
`confirmed by PCR and direct Sanger sequencing.
`Additionally, paired-end sequencing of size-selected
`molecules can be used to infer deletion and duplication
`events and additionally provide information regarding
`inversions and rearrangements. Due to the advantages
`of the high-throughput sequencing platform we find it
`likely this method will become the most commonly used
`platform for CNV discovery.
`The cost and throughput of CNV analysis on the
`high-throughput sequencing platform can be dramat-
`ically reduced by multiplexing, which is enabled by
`introducing barcodes during sequencing library con-
`struction. On the basis of computer simulations using
`the data obtained from the dac4 deletion, read coverage
`
`Figure 3.—Simulation studies indicate that low read cover-
`age is sufficient for CNV detection. Analyses of dac4 CNV on
`chromosome arm 2L were performed on random samples of
`the dac4 data to simulate different depths of sequencing cov-
`erage. Analyses were performed at 1-kb (A) and 3-kb (B) res-
`olution using DNA copy (see materials and methods). The
`mean observed distance between the midpoint of the pre-
`dicted window and the empirically derived breakpoint is plot-
`ted across seven replicates. Error bars represent the standard
`deviation across all seven replicates. In both simulations a
`trend of increasing error is observed as the number of reads
`sampled decreases.
`
`correctly identified but both breakpoints were also
`mapped within 3 kb. We validated the molecular break-
`points by PCR using primers specific to the insertion
`element remaining after recombination and the flank-
`ing genomic region (see Table S2).
`Similar results were obtained for Df(2L)Sd37, a de-
`ficiency generated by X-ray mutagenesis (Ganetzky
`1977). This deficiency has been mapped cytologically to
`37D2–38B2, corresponding to the 19.3–20.7 Mb region.
`The breakpoints, however, have not been molecularly
`characterized. CNV analysis identifies a deletion on
`chromosome 2L with breakpoints occurring in
`3-kb windows whose midpoints are 19,423,344 and
`19,962,150 bp (Figure 4B). This result is consistent
`with previous genetic complementation data. First,
`Df(2L)Sd37 complements mutations in the gTub37C
`gene, which is located on chromosome 2L between
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`Figure 4.—CNV analysis of deletion mutants
`Df(3L)Exel6105 and Df(2L)Sd37 identifies molecu-
`lar breakpoints. (A) Analysis of chr 3L of Df(3L)
`Exel6105 identifies a region of low log2 score
`(blue) consistent with the molecularly defined dele-
`tion Df(3L)5,359,162..5,601,375. (B) Analysis of
`Df(2L)Sd37 identifies a region of low log2 score
`(blue) consistent with a deletion on chr 2L with
`breakpoints occurring in 3-kb windows whose mid-
`points are 19,423,344 and 19,962,150 bp, respec-
`tively.
`
`as low as 0.1x or 458,000 reads is sufficient for break-
`point identification at 3-kb resolution. This result
`suggests that data generated from a single Solexa lane
`should be sufficient for simultaneously analyzing as
`many as five stocks in multiplex. Currently, this puts the
`cost of characterizing large CNVs at approximately
`$350 each. In comparison, the cost of an array-CGH
`experiment with single gene resolution is approxi-
`mately $350, while a 1-kb resolution would cost approx-
`imately $800. As the capacity of high-throughput
`sequencing continues to increase and costs decline
`rapidly, the method we describe will be more cost ef-
`fective than array-CGH. Additionally, the next-generation
`sequencing approach offers the ability to improve
`resolution by increasing the depth of sequencing
`coverage. Thus, CNV discovery by high-throughput se-
`quencing is scalable—the desired coverage-to-resolution
`balance can be determined and the cost optimized. For
`CNVs in the subkilobase range, the sequencing plat-
`form is likely to be very effective; however, methods of
`analysis in addition to those described in this report will
`be required.
`One immediate application for CNV discovery in
`Drosophila by high-throughput sequencing is mapping
`the deletions of each Bloomington core deficiency stock
`that have not been molecularly characterized. Two
`major limitations presently reduce the effectiveness of
`this important genetic tool. First, the breakpoints of
`three-quarters of the stocks are mapped cytologically,
`the size of these deletions remains uncertain, diminish-
`ing the utility of these stocks. Second, many stocks may
`harbor cryptic rearrangements that diminish the re-
`liability of results. Both problems can be largely resolved
`using the high-throughput sequencing method. The
`characterization of deficiency and duplication stocks by
`array-CGH has been described previously (Erickson
`and Spana 2006). The high-throughput sequencing
`approach provides a good alternative with greater
`resolution at a currently comparable and rapidly de-
`clining cost.
`
`In addition to identifying the expected deletions and
`accurately defining the breakpoints for all deficiencies
`described in this report, our analyses indicated addi-
`tional copy number variations in each data set (see Figure
`S2, Figure S3, and Figure S4). From the lack of false
`positives in the self-vs.-self data sets we find it likely that
`these variants are legitimate though it is unclear whether
`they occur on the same chromosome as the expected
`deletions or are harbored on the balancer chromosome,
`which was also sequenced. Further inquiry would be
`required to verify the nature of these CNVs and the
`chromosome on which they occur; because our interest
`was in defining the known deficiencies, we did not seek to
`validate these. These observations do, however, highlight
`the possibility of cryptic structural variation harbored on
`the chromosomes of deficiency stocks.
`To date CNV studies have been largely limited to
`humans primarily due to the high cost of the methods
`used for detection. As described in our report, high-
`throughput sequencing technology now offers the
`opportunity for cost-effective characterization of CNV.
`Taking advantage of this approach, the contribution of
`CNV to phenotypic variation in model organisms,
`including Drosophila, can be systematically explored.
`Such studies are likely to offer important insights
`regarding the biological consequences of CNV.
`
`We thank Graeme Mardon for providing dac4 flies, scientific discussion
`relevant to the design of experiments, and review of the manuscript. We
`also thank Hugo Bellen, Shinya Yamamoto, and Kevin Cook for scientific
`discussion and review of the manuscript. We acknowledge Stephen
`Richards of the Human Genome Sequencing Center for ongoing
`collaborations to study CNV in Drosophila. Finally, we thank the staff of
`the Human Genome Sequencing Center who performed the sequencing
`of genomic libraries. This work is partially supported by the National
`Human Genome Research Institute grant U54HG003273 to R.G.
`
`LITERATURE CITED
`Bridges, C. B.,1936 Thebar‘‘gene’’ aduplication.Science 83:210–211.
`Campbell, P. J., P. J. Stephens, E. D. Pleasance, S. O’Meara, H. Li
`et al., 2008 Identification of somatically acquired rearrangements
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`Multiplex Sequencing CNV
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`941
`
`in cancer using genome-wide massively parallel paired-end se-
`quencing. Nat. Genet. 40: 722–729.
`Carter, N. P., 2007 Methods and strategies for analyzing copy num-
`ber variation using DNA microarrays. Nat. Genet. 39: S16–S21.
`Chiang, D. Y., G. Getz, D. B. Jaffe, M. J. O’Kelly, X. Zhao et al.,
`2009 High-resolution mapping of copy-number alterations with
`massively parallel sequencing. Nat. Methods 6: 99–103.
`Dopman, E. B., and D. L. Hartl, 2007 A portrait of copy-number
`polymorphism in Drosophila melanogaster. Proc. Natl. Acad. Sci.
`USA 104: 19920–19925.
`Eilers, P. H., and R. X. de Menezes, 2005 Quantile smoothing of
`array CGH data. Bioinformatics 21: 1146–1153.
`Emerson, J. J., M. Cardoso-Moreira, J. O. Borevitz and M. Long,
`2008 Natural selection shapes genome-wide patterns of copy-
`number polymorphism in Drosophila melanogaster. Science
`320: 1629–1631.
`Erickson, J. N., and E. P. Spana, 2006 Mapping Drosophila geno-
`mic aberration breakpoints with comparative genome hybridiza-
`tion on microarrays. Methods Enzymol. 410: 377–386.
`Fiegler, H., R. Redon, D. Andrews, C. Scott, R. Andrews et al.,
`2006 Accurateandreliablehigh-throughputdetectionofcopynum-
`ber variation in the human genome. Genome Res. 16: 1566–1574.
`Ganetzky, B., 1977 On the components of segregation distortion in
`Drosophila melanogaster. Genetics 86: 321–355.
`Hupe, P., N. Stransky, J. P. Thiery, F. Radvanyi and E. Barillot,
`2004 Analysis of array CGH data: from signal ratio to gain
`and loss of DNA regions. Bioinformatics 20: 3413–3422.
`Lai, T. L., H. Xing and N. Zhang, 2008a Stochastic segmentation
`models for array-based comparative genomic hybridization data
`analysis. Biostatistics 9: 290–307.
`Lai, W., V. Choudhary and P. J. Park, 2008b CGHweb: a tool for
`comparing DNA copy number segmentations from multiple al-
`gorithms. Bioinformatics 24: 1014–1015.
`Mardon, G., N. M. Solomon and G. M. Rubin, 1994 dachshund
`encodes a nuclear protein required for normal eye and leg devel-
`opment in Drosophila. Development 120: 3473–3486.
`Marioni, J. C., N. P. Thorne and S. Tavare, 2006 BioHMM: a het-
`erogeneous hidden Markov model for segmenting array CGH
`data. Bioinformatics 22: 1144–1146.
`Olshen, A. B., E. S. Venkatraman, R. Lucito and M. Wigler,
`2004 Circular binary segmentation for the analysis of array-
`based DNA copy number data. Biostatistics 5: 557–572.
`Parks, A. L., K. R. Cook, M. Belvin, N. A. Dompe, R. Fawcett et al.,
`2004 Systematic generation of high-resolution deletion cover-
`
`age of the Drosophila melanogaster genome. Nat. Genet. 36:
`288–292.
`Pentz, E. S., B. C. Black and T. R. Wright, 1990 Mutations affect-
`ing phenol oxidase activity in Drosophila: quicksilver and tyros-
`inase-1. Biochem. Genet. 28: 151–171.
`Picard, F., S. Robin, M. Lavielle, C. Vaisse and J. J. Daudin,
`2005 A statistical approach for array CGH data analysis. BMC
`Bioinformatics 6: 27.
`Redon, R., S. Ishikawa, K. R. Fitch, L. Feuk, G. H. Perry et al.,
`2006 Global variation in copy number in the human genome.
`Nature 444: 444–454.
`Roegiers, F., J. Kavaler, N. Tolwinski, Y. T. Chou, H. Duan et al.,
`2009 Frequent unanticipated alleles of lethal giant larvae in
`Drosophila second chromosome stocks. Genetics 182: 407–410.
`Rozen, S., and H. Skaletsky, 2000 Primer3 on the WWW for gen-
`eral users and for biologist programmers. Methods Mol. Biol.
`132: 365–386.
`Srivatsan, A., Y. Han, J. Peng, A. K. Tehranchi, R. Gibbs et al.,
`2008 High-precision, whole-genome sequencing of laboratory
`strains facilitates genetic studies. PLoS Genet. 4: e1000139.
`Stathakis, D. G., E. S. Pentz, M. E. Freeman, J. Kullman, G. R.
`Hankins et al., 1995 The genetic and molecular organization
`of the Dopa decarboxylase gene cluster of Drosophila melanogaster.
`Genetics 141: 629–655.
`Tibshirani, R., and P. Wang, 2008 Spatial smoothing and hot spot
`detection for CGH data using the fused lasso. Biostatistics 9:
`18–29.
`Venkatraman, E. S., and A. B. Olshen, 2007 A faster circular bi-
`nary segmentation algorithm for the analysis of array CGH data.
`Bioinformatics 23: 657–663.
`Willenbrock, H., and J. Fridlyand, 2005 A comparison study: ap-
`plying segmentation to array CGH data for downstream analyses.
`Bioinformatics 2