Library Preparation and Multiplex Capture for Massive
`Parallel Sequencing Applications Made Efficient and Easy
`
`Marten Neiman1, Simon Sundling1, Henrik Gro¨ nberg2, Per Hall2, Kamila Czene2, Johan Lindberg1*
`˚
`.
`Daniel Klevebring1*
`
`.
`,
`
`1 Department of Medical Epidemiology and Biostatistics, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden, 2 Department of Medical Epidemiology,
`Karolinska Institutet, Stockholm, Sweden
`
`Abstract
`
`During the recent years, rapid development of sequencing technologies and a competitive market has enabled researchers
`to perform massive sequencing projects at a reasonable cost. As the price for the actual sequencing reactions drops,
`enabling more samples to be sequenced, the relative price for preparing libraries gets larger and the practical laboratory
`work becomes complex and tedious. We present a cost-effective strategy for simplified library preparation compatible with
`both whole genome- and targeted sequencing experiments. An optimized enzyme composition and reaction buffer
`reduces the number of required clean-up steps and allows for usage of bulk enzymes which makes the whole process
`cheap, efficient and simple. We also present a two-tagging strategy, which allows for multiplex sequencing of targeted
`regions. To prove our concept, we have prepared libraries for low-pass sequencing from 100 ng DNA, performed 2-, 4- and
`8-plex exome capture and a 96-plex capture of a 500 kb region. In all samples we see a high concordance (.99.4%) of SNP
`calls when comparing to commercially available SNP-chip platforms.
`
`Citation: Neiman M, Sundling S, Gro¨ nberg H, Hall P, Czene K, et al. (2012) Library Preparation and Multiplex Capture for Massive Parallel Sequencing Applications
`Made Efficient and Easy. PLoS ONE 7(11): e48616. doi:10.1371/journal.pone.0048616
`
`Editor: Michael Watson, The Roslin Institute, University of Edinburgh, United Kingdom
`
`Received June 1, 2012; Accepted September 27, 2012; Published November 5, 2012
`Copyright: ß 2012 Neiman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
`unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
`
`Funding: This study was supported by the Swedish Research Council. The funders had no role in study design, data collection and analysis, decision to publish,
`or preparation of the manuscript.
`
`Competing Interests: The authors have declared that no competing interests exist.
`
`* E-mail:
`; daniel.klevebring@ki.se
`johan.lindberg@ki.se (JL)
`. These authors contributed equally to this work.
`
` (DK)
`
`Introduction
`
`Since the introduction of massively parallel DNA sequencing,
`there has been a rapid adoption of the different technologies in the
`sequencing field. Resequencing of
`full human genomes and
`targeted sequencing of exomes have enabled discoveries of genes
`and altered pathways in both mono- and polygenic inherited
`diseases [1,2,3,4,5]. Even though amplification-free library prep-
`aration protocols are available [6,7], the vast majority of sample
`preparation strategies for massively parallel sequencing rely on
`amplification by PCR.
`In order
`to prepare a sample for
`sequencing, genomic DNA is sheared and end-repaired after
`which common adapter sequences, often containing barcodes, are
`ligated onto each fragment. This step is critical as a low efficiency
`in the ligation step yields a low number of amplifiable DNA
`templates for the downstream PCR step. Inefficient ligation thus
`leads
`to a low number of unique molecules available for
`sequencing (i.e. a library with low complexity) relative to the
`amount of starting material. Obviously, the performance of the
`library preparation process determines the amount of input DNA
`required in order to produce a sufficiently complex end product
`for sequencing. In order to improve the yield, one needs to
`increase the efficacy within each step and/or reduce the total
`number of clean-up steps during the library preparation. Several
`slight increases in the yield of each enzymatic step have the
`potential to positively affect overall yield significantly. Clean-up
`steps are common sources of loss of material and reduction of
`
`overall library yield. A typical yield in a spin-column purification is
`60–80% [8,9], thus for library preparation protocols with three
`purification steps prior to PCR, these steps alone decreases the
`yield by 50–80%. Automated protocols circumventing spin
`columns have been devised [10], capable of handling large
`numbers of samples. An issue with these protocols is that robotics
`are necessary to reach a large throughput.
`The traditional Illumina TruSeq library preparation requires
`1 mg DNA [11] and several approaches have been devised to lower
`in vitro
`the necessary input amount. Currently,
`the use of
`transposition is the most effective way of building sequencing
`libraries, where whole-genome sequencing of human samples can
`be achieved with 50 ng of DNA. Furthermore, conventional T7-
`based linear amplification, commonly used for microarrays, has
`been adopted to obtain a more even amplification of
`ligated
`products [12]. However, it requires several clean-up steps prior to
`amplification, which reduce the complexity of the library. Due to
`the inherent nature of
`ligation of
`full-length complementary
`adapters, only 25% of ligated molecules will be available for linear
`amplification. In addition to this, the Klenow DNA polymerase
`exo(2) enzyme, which is used for adenylation after end-repair, does
`not distinguish between different nucleotides. Therefore, only 1/
`16 of the starting molecules will carry the correct 39 overhang (A in
`both ends) for ligation, if nucleotides from the end-repair are not
`removed prior to adenylation. Zheng and colleagues refined the
`library preparation for the 454 sequencer [13] and reduced the
`number
`in cleanup steps, using a Y-shaped adapter with
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`complementarity only in the ligating end. In this approach each
`double-stranded DNA molecule can give rise to two template
`molecules in the PCR step [14].
`(GWAS) has led to the
`Genome wide association studies
`identification of hundreds of gene loci associated with different
`phenotypic traits [15]. Recent pioneering work demonstrated the
`feasibility of targeted resequencing to identify causal variants in
`regions identified through GWAS [5]. As the cost of sequencing
`decreases the relative cost of performing targeted enrichment
`increases. Multiplexed capture, where samples are barcoded and
`then mixed and used in a single capture reaction reduces the
`relative cost of enrichment. It is also an attractive means for
`increased throughput, especially in laboratories without access to
`infrastructure allowing automation. When sequencing a large
`number of samples the use of DNA barcodes is the most common
`method to determine the origin of
`the reads
`[16,17]. To
`circumvent the need of equal amounts of unique barcodes as
`samples in a mixture, the combination of two different barcodes
`can be used to decipher the origin of the reads [18,19]. Rohland
`and Reich have developed a dual barcode based method for cost-
`effective and automatable library preparation for multiplexed
`capture [20] but it is dependent of relatively large amounts of
`starting material [21]. The use of two different barcodes at each
`end of a molecule is appealing, but has the drawback that
`misidentified molecules cannot be identified as any two combina-
`tions of the barcodes are valid combinations.
`In order to perform parallel
`library preparation, we have
`devised a methodology, which only requires a single cleanup from
`fragmentation to PCR and where the entire enzymatic chain is
`functional in one single buffer (figure 1). By adjusting enzyme
`concentrations and changing the enzyme used in the adenylation
`step, a single combined size-selection and clean-up step using
`superparamagnetic beads is used in the procedure. This allows for
`cheap and easy automatable multiplex capture and sequencing,
`starting from small amounts of DNA.
`
`Library Preparation and Multiplex Capture
`
`Materials and Methods
`
`DNA extraction
`DNA was extracted from whole blood using Qiagen’s QIAmp
`spin miniprep kit according to the manufacturers recommenda-
`tions. The DNA concentration was measured using a Qubit
`fluorometer (Invitrogen, CA, USA) and the dsDNA HS kit.
`
`DNA fragmentation
`Human genomic DNA was suspended in 120 ml nuclease free
`water and sheared using the Covaris (Covaris Inc, MA, USA)
`sonication system according to the manufacturers instructions. 1 ml
`of the sample were analyzed using an Agilent 2100 Bionalyzer
`(Agilent Technologies, Santa Clara, CA, USA) and the DNA
`7500 kit.
`
`End-polishing, phosphorylation, adenylation and adaptor
`ligation
`The fragmented DNA was transferred to a fresh 1.5-ml tube
`after which the volume was reduced to 30 ml using vacuum
`centrifugation. These 30 ml were mixed with 10 ml end-polishing/
`phosphorylation/adenylation mix to a final concentration of
`16T4 DNA ligase buffer, 460.5 mM dNTP, 0.25 mM ATP,
`2.5% PEG 4000, 0.0025 U/ ml T4 DNA polymerase, 0.125 U/ ml
`T4 Polynucleotide kinase and 0.0025 U/ ml Taq DNA polymerase
`(recombinant)
`(all enzymes and buffers
`from Fermentas
`life
`sciences, Burlington, Canada). The DNA-samples were end-
`polished, adenylated and phosphorylated by incubating the
`reaction mixes for 15 min at 12uC, 15 min at 37uC, 20 min at
`72uC and final 4uC forever in a pre-cooled thermal cycler
`(GeneAmp 9700 PCR system, Applied Biosystems). Ten micro-
`litres of a ligation mix was added to the samples to a final
`concentration of 0.3 U/ ml T4 DNA Ligase and a 1:10 molar ratio
`of DNA fragments to adaptor constructs (table S1). Adaptors were
`ligated to the template DNA by incubating the reaction mix at
`
`Figure 1. A schematic overview: genomic DNA is fragmentized, end-repaired, phosphorylated and adenylated in the same reaction.
`Adaptor ligation is followed by size-selection and PCR.
`doi:10.1371/journal.pone.0048616.g001
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`16uC over night (16 h) in a pre-cooled thermal cycler (GeneAmp
`9700 PCR system, Applied Biosystems).
`
`Short fragments removal
`Short DNA fragments and unligated adaptor constructs were
`washed away by polyethylene glycol (PEG) mediated precipitation
`on carboxylic acid coated magnetic beads (MyOne, Invitrogen)
`using 6.3% PEG solution in a MagnatrixTM1200 (NorDiag ASA,
`Oslo, Norway) liquid handling robot [22]. The mg-samples were
`split in 5 reactions prior to clean-up and the volumes were adjusted
`to 50 ml using 0.16EB (Qiagen Elution Buffer). The DNA was
`eluted in 23 ml EB.
`
`Enrichment of ligated fragments
`Barcoding and enrichment of ligated fragments was carried out
`by PCR. The eluted DNA was mixed together with PCR reagents
`and primers for a final concentration of 1xPhusion HF master mix
`(Finnzymes, Espoo, Finland) and 0.2 mM of each PCR primer
`(table S1). The reaction volume was 5650 ml for the mg-samples
`and 50 ml for the ng-samples. The reactions were incubated in a
`thermal cycler (GeneAmp 9700 PCR system, Applied Biosystems)
`for 2 min at 98uC, 12 cycles of 10 s at 98uC, 30 s at 65uC, 20 s at
`72uC and a final extension of 5 min at 72uC ending with an
`infinite hold at 4uC. Final
`library cleanup was done by PEG-
`mediated precipitation on carboxylic acid coated magnetic beads
`as described above. The final libraries were evaluated using an
`Agilent 2100 Bionalyzer (Agilent Technologies) and the DNA
`7500 kit or the DNA High Sensitivity kit.
`
`Quanitative PCR
`Quantitative PCR was carried out using the BioRad CFX96
`instrument as instructed by the manufacturer. The function
`ratiocalc from the R-package qpcR [23,24] was used to estimate
`the relative amounts of
`library molecules obtained from the
`different amounts of starting material. The function Cy0 was used
`to calculate Cy0-values, which correspond to the more traditional
`Ct-value but are more accurate [25].
`
`Enrichment of genomic regions
`Samples prepared as described above from 100 ng or 1 mg
`DNA, were pooled for 2-, 4- and 8-plex exome capture. Exome
`capture was carried out using the SeqCap EZ Exome Library
`Version 1(Nimblegen) according to the manufacturers instructions
`with modified blocker oligonucleotides covering the entire Y-
`adapter. Equal amounts of each index-blocker were used, with a
`total of 1000 pmol per reaction (i.e. for the 2-plex 50 pmol of each
`of the two indices were used, for the 8-plex 125 pmol of each index
`was used). Post-capture PCR was run for 18 cycles.
`
`Adjustment for 96-plex library preparation and targeted
`resequencing
`For the 96-plex capture reaction, 500 ng of DNA was mixed
`with 1.5 ml Fragmentase (NEB), 1.5 m106Fragmentase buffer and
`nuclease-free water to 15 ml. The reaction was incubated in 37uC
`for 20 minutes, followed by heat inactivation in 65uC for 15
`minutes. Fragmented DNA was end-repaired, phosphorylated and
`adenylated by adding 5 ml master mix as described above. A
`double-stranded 8-bp barcode with an 39 A overhang in one end
`and a 39 3-bp overhang in the other end was ligated the fragments
`in the plate as described above (59 ends were
`in each well
`phosphorylated). Equal volumes of ligation mixture DNA from
`each well was pooled and cleaned up using PEG-mediated
`precipitation (see above). A modified Y-shaped adapter with a 3-
`
`Library Preparation and Multiplex Capture
`
`bp overhang matching the one on the barcodes was ligated onto
`the pooled DNA after which unligated adapters were removed by
`PEG-mediated precipitation (see above). Pre-capture PCR was
`carried out as described above after which enrichment of a
`genomic region encompassing 500 kb was performed using a
`custom SureSelect XT kit (Agilent) according to the manufacturers
`instructions with the modification that the bait library was diluted
`a factor 10 prior to use. Post-capture PCR was performed as
`described above.
`
`Sequencing
`Sequencing was carried out on the Illumina HiSeq 2000 system
`according to the manufacturers recommendations. All lanes were
`spiked with 1% phiX as a quality control.
`
`Low-level processing of sequence data and SNP calling
`Raw data was aligned to the GRCh37 (hg19) genome using
`BWA (Burrows-Wheeler Aligner, version 0.5.9) [26]. Standard
`arguments were used except for –q 10, which soft-clips low-quality
`bases at the ends of reads. Tools available in the software suite
`Picard (http://picard.sourceforge.net) were used for quality
`control and removal of technical duplicates. Subsequently, the
`sequence data was realigned and base qualities recalibrated using
`the genome analysis
`toolkit
`(GATK) [27]. Single nucleotide
`polymorphisms (SNPs) were called with the MAQ SNP calling
`model, available in Samtools (version 0.1.16) [28]. To validate the
`SNP calls, the same DNA used for library preparation was assayed
`using the Affymetrix 6.0 SNP array. The Affymetrix data was
`processed as described previously [29]. For the 96-plex capture,
`the validation was carried out on the Illumina HumanHap300,
`240 and 550 platforms as described previously [30].
`
`Ethics statement
`This project was carried out according to the declaration of
`Helsinki. The Regional Ethics Committee in Stockholm specifi-
`cally approved this study. Written consent was received from all
`participants of the study
`
`Results
`
`library preparation, we replaced
`To enable single-buffer
`Klenow fragment exo(2) with Taq DNA polymerase as the
`adenylating enzyme. Taq has the propensity of remaining bound
`to the DNA if used in too high concentrations. As a consequence
`due to steric hindrance, the ligation will suffer from reduced
`efficiency. Therefore, we reduced the Taq DNA polymerase
`concentration in the adenylation step by a factor of 50 compared
`to recommended amounts, which improved the overall yield
`significantly (figure S1). To further increase the efficiency, we
`investigated the effect of prolonging the ligation time to two hours
`and over-night (16 h). We also investigated the effect of modifying
`the incubation temperature scheme during the end-polishing
`reaction for each enzyme by changing the traditional 30 min at
`30uC into 15 min at 12uC (optimal for T4 DNA polymerase) plus
`15 min at 37uC (optimal
`for T4 PNK). To investigate the
`importance of the three variables we prepared libraries from
`100 ng DNA using all combinations of
`the variables and
`performed quantitative PCR (qPCR) on the ligation products
`(figure S2). An analysis of variance (ANOVA)
`table was
`constructed using the Cy0-values from the qPCR as outcome
`(table S2). The table shows that both the over-night ligation and
`the lowered DNA polymerase concentration have significant
`effects on the threshold cycle of the amplification, whereas the
`modified end-polishing incubation scheme shows no improvement
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`Library Preparation and Multiplex Capture
`
`Figure 2. Concordance of heterozygous SNPs (lines with dots) for 100 ng and 1 mg exome libraries of different multiplexity and a
`500 ng 96-plex target capture library. The average concordance for exome libraries was 99.4% with no significant difference between libraries.
`For the 96-plex experiment, the average concordance was 99.8%. Solid lines indicate the average allelic balance. Even in the 96-plex experiment, no
`bias in allelic balance is observed.
`doi:10.1371/journal.pone.0048616.g002
`
`in yield. We also investigated the fraction of duplicate molecules
`after sequencing for selected libraries, which shows a 10-fold
`decrease after improving the protocol (table S3).
`
`Multiplex targeted capture
`As the number of multiplexed samples increases, the concen-
`tration of the bait molecules has the potential to limit efficient
`capture of non-reference alleles due to competitive hybridization.
`To monitor such effects we prepared libraries from 1 mg of DNA
`and performed 2-, 4- and 8-plex captures using the SeqCap EZ
`Exome Library targeting 180 000 coding exons. Since sample
`availability is commonly limiting, we repeated the experiment
`using only 100 ng of DNA for library preparation. The 8-plex
`captures were run on a single lane on the Hiseq 2000. The 4-plex
`and 2-plex reactions were pooled together in 2:1 ratios in two lanes
`to yield ,1/6 lane per library. Each sample was sequenced to a
`mean coverage of around 426 in the target regions (figure S1). To
`evaluate the performance of the multiplexed capture, SNP calls
`were compared to variants
`identified using a commercially
`available SNP-array [29,30]. From the sequencing data, SNPs
`were only called at positions with .15 in read depth that
`overlapped with SNPs available on the array. On average, 13328
`positions were examined for each sequenced exome library. The
`average concordance between heterozygote (hz) variants called by
`the SNP-chip and the sequenced libraries was 99.4% with no
`significant difference between DNA input amounts or degree of
`multiplexing (Kruskal-Wallis, p = 0.93)(figure 2). Furthermore, we
`investigated the allelic bias - i.e. if the variant allele was lost in the
`capture step due to competitive hybridization. We could not detect
`any such effect (figure 2). To investigate potential biases in the
`modified protocol, we compared the sequences results with the
`standard protocol in terms of insert size, GC content and variation
`across targets (figure S3). We did not se any trends indicating that
`
`the modified protocol has effect on either of these parameters. For
`the 96-plex capture, we investigated the concordance of 94 SNPs
`that overlapped with our 500 kb target region and the SNP-chip.
`The average concordance of 2724 heterozygous SNPs across all 96
`samples was 99.8% when requiring sequence coverage over 156.
`As for the exome libraries, we were not able to see any evidence of
`a shifted allelic balance due to competitive hybridization.
`
`Discussion
`
`We demonstrate that library preparation for massive parallel
`sequencing can be made cheap, simple and efficient. Our method
`is applicable on all sequencing platforms requiring addition of
`universal adapter handles prior to sequencing, such as Illumina,
`SOLiD, 454 and Ion Torrent. The absence of spin column
`purification makes the protocol easy to automate and reduces the
`loss of material. This is achieved by utilizing Taq DNA polymerase
`for adenylation instead of Klenow fragment exo(2), which is used
`in the Illumina TruSeq protocol (figure 1). Klenow exo(2) adds any
`of the four bases to 39-ends of the DNA fragments. Therefore,
`nucleotides remaining from the end-repair reaction have to be
`removed by a clean-up step prior to adenylation. In contrast, Taq
`adds only dATP’s even in the presence of all nucleotides, which
`makes a nucleotide removal step prior to adenylation superfluous.
`Since Taq is a thermophilic enzyme, which is inactive at low
`temperatures, end-polishing by T4 DNA polymerase and phos-
`phorylation by T4 polynucleotide kinase takes place at a low
`temperature. Subsequently, the temperature is increased to 72uC,
`which allows for the adenylation reaction to start, while the
`mesophilic enzymes are heat-inactivated.
`Targeted capture of specific genomic regions is a powerful
`technology for cost-efficient
`interrogation of
`limited parts of
`genomes. It is commonly associated with an increased manual
`labor to prepare the libraries required. Furthermore, in settings
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`such as analysis of solid tumors, it is common to have a limited
`amount of material available for library preparation. In this study,
`we present a simplified laboratory procedure for preparing
`libraries for massively parallel sequencing. To maintain high yield
`while starting with a lower amount of input DNA, we changed
`several key aspects in the protocol. First, we changed the reaction
`buffer of the enzymatic steps to a single one-for-all buffer. This
`enabled us to remove all column-based cleanup steps in the
`protocol and replace them with a single cleanup step based on
`PEG-mediated precipitation on superparamagnetic beads. Our
`protocol is thus well suited for automation in any robot that is
`equipped with a magnet to handle superparamagnetic beads.
`For studies where large numbers of samples are analyzed, the
`cost of preparing the libraries can be a significant proportion of the
`total cost. Since our protocol is based on readily available bulk
`enzymes,
`the cost
`is
`significantly reduced. To test
`this, we
`investigated the performance of
`three different degrees of
`multiplexing and evaluated the end data quality in several aspects.
`Firstly, the samples remain balanced after capture; i.e. a similar
`number of
`reads are sequenced from each sample in a
`multiplexing pool
`(figure S1). When increasing the number of
`samples in a multiplexed capture reaction, there is a risk that
`variant alleles are captured to a lower extent than the reference
`allele for which the bait was designed. However, we do not observe
`such effect. In our data, the allele frequency is very close to 50% in
`heterozygous
`tag-SNP positions
`independently of
`coverage
`(figure 2). There was no difference based on the number of
`samples in the multiplexing pool. Secondly, to push the number of
`samples
`in a multiplexing pool, we modified the library
`preparation protocol to add a specific 8-bp barcode to each well
`in a 96-well plate in order to perform 96-plexed capture of a
`genomic region of 500 kb. Even in this data, we do not see any
`tendency that the variant allele is captured to a lower extent
`(figure 2). The ability to perform multiplexing with 96 samples in
`parallel can cut costs for projects where large numbers of samples
`are analyzed significantly while maintaining individual level data.
`The modifications we introduced in the protocol improved the
`yield of the library thus allowing us to reduce the starting amount
`of DNA.
`
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`Library Preparation and Multiplex Capture
`
`Supporting Information
`
`Figure S1 Average coverage in targeted regions for
`exome libraries. The data is even across samples even
`when 8 samples are pooled in the capture step.
`(TIF)
`
`Figure S2 qPCR plot on which the ANOVA was based.
`An overnight
`ligation and adjusted enzyme mix significantly
`improve the Cy0 value in the qPCR. Each curve represents the
`mean of two technical replicates.
`(PDF)
`
`Figure S3 Fold 80 base penalty (A), insert size (B) and
`GC-content (C) for libraries prepared with the standard
`and improved protocols.
`(PDF)
`
`Table S1 Sequences for the oligonucleotides used.
`(PDF)
`
`Table S2 The implications of protocol adjustments
`calculated using an analysis of variance table.
`(PDF)
`
`Table S3 Summary of sequencing data for selected
`libraries. Modifying the ligation time and enzyme mix reduces
`the fraction of PCR duplicates approximately a factor 10-fold.
`(XLSX)
`
`Acknowledgments
`
`The authors would like to thank Anna Westring for excellent laboratory
`support. We thank Julia Sandberg for proof-reading the manuscript and
`thank Afshin Ahmadian for valuable discussions. Furthermore, we
`acknowledge support
`from Science for Life Laboratory,
`the Swedish
`national infrastructure SNISS, and Uppmax for providing assistance in
`massively parallel sequencing and computational infrastructure.
`
`Author Contributions
`
`Conceived and designed the experiments: MN JL DK SS. Performed the
`experiments: MN JL DK SS. Analyzed the data: MN JL DK. Contributed
`reagents/materials/analysis tools: PH KC HG. Wrote the paper: MN JL
`DK PH KC HG. PH KC HG. Obtained ethical approval.
`
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`multiplex sequencing method for rapid and reliable genotyping of highly
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`19. Neiman M, Lundin S, Savolainen P, Ahmadian A (2011) Decoding a substantial
`set of samples in parallel by massive sequencing. PLoS One 6: e17785.
`20. Rohland N, Reich D (2012) Cost-effective, high-throughput DNA sequencing
`libraries for multiplexed target capture. Genome Res.
`21. Kircher M, Sawyer S, Meyer M (2012) Double indexing overcomes inaccuracies
`in multiplex sequencing on the Illumina platform. Nucleic Acids Res 40: e3.
`22. Lundin S, Stranneheim H, Pettersson E, Klevebring D, Lundeberg J (2010)
`Increased throughput by parallelization of
`library preparation for massive
`sequencing. PLoS One 5: e10029.
`23. Ritz C, Spiess AN (2008) qpcR: an R package for sigmoidal model selection in
`quantitative real-time polymerase chain reaction analysis. Bioinformatics 24:
`1549–1551.
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`Issue 11 | e48616
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`00005
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`

`Library Preparation and Multiplex Capture
`
`24. Team RDC (2010) R: A language and environment for statistical computing.
`Vienna: R Foundation for Statistical Computing.
`25. Guescini M, Sisti D, Rocchi MB, Stocchi L, Stocchi V (2008) A new real-time
`PCR method to overcome significant quantitative inaccuracy due to slight
`amplification inhibition. BMC Bioinformatics 9: 326.
`26. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-
`Wheeler transform. Bioinformatics 25: 1754–1760.
`27. Depristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, et al. (2011) A
`framework for variation discovery and genotyping using next-generation DNA
`sequencing data. Nature genetics.
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`28. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, et al. (2009) The Sequence
`Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079.
`29. Liu W, Laitinen S, Khan S, Vihinen M, Kowalski J, et al. (2009) Copy number
`analysis indicates monoclonal origin of lethal metastatic prostate cancer. Nat
`Med 15: 559–565.
`30. Li J, Humphreys K, Heikkinen T, Aittomaki K, Blomqvist C, et al. (2011) A
`combined analysis of genome-wide association studies in breast cancer. Breast
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`PLOS ONE | www.plosone.org
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`6
`
`November 2012 | Volume 7 |
`
`Issue 11 | e48616
`
`00006
`
`

`

`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`€xejdg-6ny,
`
`pyxe\dg-6ny,
`
`gxa\jdg-6ny,
`
`9gxajdg-6ny,
`
`LZx9\dg-6n,
`
`gxodg-6ny,
`
`50 -
`
`T
`
`T
`
`T
`
`t+
`
`oO
`
`oO
`
`So
`
`OoN
`
`abesan05
`
`10 +
`
`
`
`
`
`
`
`|xeidz-Bugor
`
`zxe|dz-Bug0L
`
`Lxejdp-BugoL
`
`Zxaldp-6ug0}
`
`€xaldp-6ug0}
`
`vxeldp-BugoL
`
`|xedg-6ugo1
`
`Zzxedg-BugoL
`
`€xe|dg-Bugo1
`
`vxe|dg-BugoL
`
`gxe|dg-BugoL
`
`g"xeid9-6u00L
`
`°oZxeidg-6io1
`
`gxe|dg-ByQoL
`
`|xejdz-6n,
`
`zZxajdz-Bny,
`
`|xejdp-6ny,
`
`Zxaldp-6ny,
`
`€xaldp-Bny,
`
`pyxaldp-Bn1
`
`|xejdg-6ny,
`
`
`
`Zzxajdg-Bny,
`
`00007
`
`

`

`Amplification efficiency
`
`2h|N|N
`2h|G|N
`2h|N|G
`2h|G|G
`ON|N|N
`ON|G|N
`ON|N|G
`ON|G|G
`30m|G|G
`neg
`
`15000
`
`10000
`
`5000
`
`0
`
`Raw fluorescence
`
`5
`
`10
`
`Cycles
`
`15
`
`20
`
`00008
`
`

`

`Per−base GC content
`
`C
`
`100
`
`80
`
`60
`
`40
`
`% GC
`
`Fold 80 base penalty
`
`02
`
`0
`
`100
`
`0
`
`20
`
`40
`
`60
`
`Cycle
`
`80
`
`100
`
`80
`
`60
`
`40
`
`02
`
`0
`
`% GC
`
`Standard protocol
`Modi(cid:31)ed protocol
`Median Insert Size
`
`A
`
`1234
`
`Fold 80 base penalty
`
`B
`
`200
`
`150
`
`Median Insert Size (bps)
`
`100
`
`50
`
`0
`
`00009
`
`

`

`Sequence (5'-3')
`Oligo name
`ACACTCTTTCCCTACACGACGCTCTTCCGATCT
`adapter_1
`GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
`adapter_2
`AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC
`PCR_fw
`CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTC
`PCR_indx_1
`CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTC
`PCR_indx_2
`CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTC
`PCR_indx_3
`CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTC
`PCR_indx_4
`CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTC
`PCR_indx_5
`CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTC
`PCR_indx_6
`CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTC
`PCR_indx_7
`CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTC
`PCR_indx_8
`CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTC
`PCR_indx_9
`PCR_indx_10 CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTC
`PCR_indx_11 CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTC
`PCR_indx_12 CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTC
`
`00010
`
`

`

`Source
`Model
`
`Partial SS
`60.48
`
`over-night ligation
`adjusted enzyme mix
`adjusted incubation
`
`Residual
`Total
`
`11.11
`48.97
`0.41
`
`5.11
`65.60
`
`df
`3
`
`1
`1
`1
`
`12
`15
`
`MS
`20.16
`
`11.11
`48.97
`0.41
`
`0.43
`4.37
`
`F
`47.3
`
`P