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BMC Genomics

Research article
Open Access

dCATCH-Seq: improved sequencing of large continuous genomic targets with double-hybridization

BMC Genomics201718:811

https://doi.org/10.1186/s12864-017-4159-7

©  The Author(s). 2017

Received: 3 April 2017

Accepted: 5 October 2017

Published: 23 October 2017

Abstract

Background

Targeted sequencing is a powerful tool with broad application in both basic and translational sciences. Relatively low on-target rates for most current targeted sequencing studies influence the required coverage and data quality for subsequent applications.

Results

We present an improved targeted sequencing method that uses two rounds of in solution hybridization with probes synthesized from genomic clone templates, termed dCATCH-Seq. Independent captures of two large continuous genomic regions across three cell types within the human major histocompatibility complex (MHC) that spans ~3.5 Mb and a ~250 kb region on chromosome 11 demonstrated that dCATCH-Seq was highly reproducible with ~95% capture specificity. Comprehensive analyses of sequencing data generated using the dCATCH-Seq approach also showed high accuracy in the detection of genetic variants and HLA typing. The double hybridization capture approach can also be coupled with bisulfite sequencing for DNA methylation profiling of both CpG and non-CpG sites.

Conclusions

Altogether, dCATCH-Seq is a powerful and scalable targeted sequencing approach to investigate both genetic and epigenetic features.

Keywords

Targeted sequencingMhcHlaDNA methylationHybridizationBac

Background

The primary design of targeted sequencing is to capture genetic variants within intended regions [1]. Some targets, such as exome capture, has been broadly used in both basic and translational research that includes characterization of genetic diversity and demographic history in human populations [2, 3], identification of etiological variants [4], cross-species genome comparisons [5], and even phylogenetic estimation [6].

There are three main techniques [7] for targeted enrichment of DNA sequences that include hybridization-based capture either in solution or on a solid support, e.g., TruSeq (Illumina), SureSelect (Agilent), and SeqCap (Roche NimbleGen) platforms [8–10], selective circularization, and PCR amplification. In principle, targeted sequencing approaches should efficiently capture DNA molecules within the intended genomic regions with little to no sequences outside these regions of interest. However, most of current targeted sequencing studies, including commercialized whole-exome sequencing, generally show a target specificity in a range from 40% to 80%, and rarely approach 90%, regardless of probes used in hybrid selection [9–15]. Although some off-target regions neighboring intended targets such as splice sites and intron edges in exome capture “splash”, could be informative [16], the capture of the non-target regions at different levels may substantially impact data quality and adequate coverage across the intended targets, and eventually require more sequencing costs. Some strategies in measuring target efficiency have also been developed, e.g., SeqCap qPCR kit (Roche) and Multipoint Test for Targeted-enrichment Efficiency (MTTE) [17].

We previously reported the clone adapted template capture hybridization sequencing (CATCH-Seq) procedure that synthesizes probe sets from a pool of selected BAC or fosmid DNA templates for in solution hybrid capture [13]. We have employed an updated target capture strategy to improve on target capture efficiency of large genomic regions by two rounds of hybridization in solution that we term double CATCH-Seq (dCATCH-Seq). To evaluate the performance of this procedure, we independently tested two large continuous genomic regions, including the entire major histocompatibility complex (MHC), covering a size of 3.5 Mb, using a custom probe set generated from a pool of 140 reference BAC DNAs. Our updated approach provides a more efficient alternative to the previously reported approach, and demonstrates the feasibility of capturing large and diverse genomic regions that enables new applications such as high resolution HLA typing.

Results

Double versus single hybridization capture

Synthesized BAC-based probes with an average size of ~250 bp were previously used for development of the CATCH-Seq procedure by in-solution hybridization [13, 14]. Following a protocol presented in the previous CATCH-seq, but undergoing two rounds of hybridization capture (see Methods), we referred to this strategy as dCATCH-Seq. We first compared the target efficiency of dCATCH-Seq versus a single round of capture using the standard CATCH-Seq method. One region was captured with a pool of probes using a BAC DNA as a template covering ~248.6 kb on chromosome 11 (Fig. 1a). For both methods, we captured with at least five technical replicates along with 2 × 100 bp paired-end sequencing. A total of 170,700 and 140,200 reads on average were obtained across technical replicates for the single and double hybridizations, respectively, and on average 96.3% of the reads were mapped to the reference genome (Additional file 1: Table S1). The on target rate (target specificity) was calculated for all replicates by dividing the number of mapped reads within the target coordinates by the total number of mapped reads. On target rates were 92.2% ± 2.65% and 37.2% ± 4.34% (mean ± s.d.) for the double and single hybridization approaches, respectively (Fig. 1b). We then merged the data across technical replicates for each approach, which resulted in over 100X coverage across the intended region (Table 1). The target specificity was more than doubled for dCATCH-Seq with each approach having identical sequencing depth. Using read depth covered within 200 bp non-overlapping windows across the target site, dCATCH-Seq had an extended proportion of read bins covered with higher depth compared to single hybridization (Fig. 1c), regardless of the fraction of repeats (repeat-masked or not).
Fig. 1

Comparison of double versus single hybridization capture approach. a Wiggle plot showing the coverage across an intended region on chr11 (chr11:68,467,857–68,716,491, hg19). Two tracks shown in dark blue are the read coverage in 50-bp bins for dCATCH-Seq and CATCH-Seq approaches (b) Barplot of on-target rates for single and double captures. Error bars are the standard deviations from technical replicates (n ≥ 5). c Plot of the proportion of reads against the read depth based on coverage in 200 bp non-overlapping windows across the intended region on chr11. Rmsk, repeat maskering, means plot based on the repeat masked read depth. d Density plot showing the comparison of read depth normalized by using log2-transformed RPM values between CATCH-seq and dCATCH-seq data. The linear regression equation and Pearson’s Correlation coefficient are shown in the left top corner. e Scatterplot showing the comparison of read depth (log2-transformed) for heterozygous variants called from CATCH-seq (x-axis) and dCATCH-seq (y-axis)

Table 1

Summary of merged sequencing data in a comparison between dCATCH-Seq and CATCH-Seq

Experiment

Total reads

Aligned reads

Mean PCR duplicates (%)

Aligned reads on target

On-target rate (%)

Uniquely aligned readsa

Uniquely aligned reads on targeta

On-target rate (%)a

Mean coverage on target

dCATCH-Seq

CATCH-Seq

853,358

841,154

414,965

802,931

47.4

2.8

382,625

291,808

92.21

36.34

406,331

762,445

379,084

289,558

93.29

37.98

145.98

110.18

aUniqueness and relevant statistics are calculated from reads with mapping quality (MAPQ) ≥ 20

We next addressed whether two rounds of capture could introduce greater bias within the targeted intervals. We normalized read coverage between the two methods by calculating the read depth per 200 bp window divided by the total number of mapped reads within the entire target (with log2 transformation) to measure uniformity of coverage across the target in both approaches. Both approaches were highly concordant (R2 = 0.93, P < 1X10−16, Fig. 1d), which suggests no further regional bias was introduced from an additional capture in the dCATCH-Seq method.

We further conducted a comparative analysis between these two approaches in terms of the accuracy in the identification of genetic variants. In total, 259 single nucleotide variants (SNVs) were identified by two methods, where 253 were known single nucleotide polymorphisms (SNPs). Among them, 115 were homozygous SNVs. All were concordantly determined by both methods. Except for three discordantly identified SNVs located in the simple repeat (polyadenine) regions (Additional file 2: Figure S1), the remaining 144 heterozygous SNVs are concordantly called from the dCATCH-Seq data and the CATCH-Seq data. Therefore, an estimation of the accuracy rate in variant calls for dCATCH-Seq was ~99.2% relative to the single hybridization approach. We also presented a comparison of the allelic depth for 141 heterozygous SNVs called from both methods. As Fig. 1e shows, the dCATCH-Seq approach did not introduce allelic bias relative to the single hybridization.

We also compared the Indel calls between the two methods. Of 32 Indels identified, 30 (~93.8%) showed a concordance of genotype calls (Additional file 1: Table S2). The remaining two discordantly called Indels were identified as heterozygous from the dCATCH-Seq data, while as homozygous from the CATCH-Seq data (Additional file 2: Figure S2). Using independent CATCH-seq assays in two replicates, we further validated that these two Indels are heterozygous insertions (Additional file 2: Figure S3).

Double hybridization capture on the MHC region

We next tested two conditions with or without PCR amplification after first round of capture using a synthesized probe set on a ~3.5 Mb continuous MHC region that covers the three major classes of MHC molecules with DNA libraries prepared from K562 cells (Fig. 2a). The sequencing data showed that both conditions were capable of capturing the entire MHC region with identically high target specificity (92.7%) and alignment rate (97%, Table 2). Comparative analyses based on both raw read coverage and normalized read coverage binned into 200 bp windows also showed that the two conditions exhibited high concordance (R2 = 0.972, P < 1X10−16, Fig. 2b and c).
Fig. 2

Double capture and sequencing of the MHC region in K562 cells. a Wiggle plot showing the capture of the human MHC region with a size of ~3.5 Mb (chr6:29,651,300–33,160,000, hg19) under two conditions (with PCR amplification or not). b Plot of the proportion of reads against the read depth based on coverage in 200-bp non-overlapping windows across the MHC region for the two conditions. Rmsk: repeat masker. c Density plot showing the comparison of read depth normalized by using the log2-transformed RPM value between two conditions

Table 2

Summary of the MHC regional capture using dCATCH-Seq in three cell genomes

Cell

Conditiona

Total reads

Aligned reads

Alignment rate (%)

PCR duplicate rate (%)

Aligned reads on target

On-target rate (%)

Uniquely aligned reads on targetb

On-target rate (%)b

K562

With PCR

2,882,056

2,744,145

97.79

2.67

2,503,434

91.23

2,342,653

92.77

K562

Without PCR

1,922,388

1,795,403

97.67

4.43

1,633,616

90.99

1,528,150

92.71

GM12878

Without PCR

4,243,904

3,836,649

98.44

8.39

3,492,283

91.02

3,228,811

92.28

U937

Without PCR

1,489,332

1,244,031

95.96

13.63

1,033,606

83.09

983,376

86.38

aConditions are defined based on with or without PCR amplification of DNA library after the first round of capture

bUniqueness and relevant statistics are calculated from reads with MAPQ ≥20

By comparing the efficiency of detecting structural variants (SVs) between the two conditions, we also found a similar performance for both (Additional file 2: Figure S4). In comparison with previously reported SVs from a whole-genome sequencing study in the same cell line [18], both methods could detect the majority of reported SVs across the MHC region (Additional file 2: Figure S4). In the present study, we also discovered two novel deletions in K562 cell genome. One was a homozygous deletion spanning ~2.5 kb in size (Additional file 2: Figure S5A, B), and the other was an ~800 bp heterozygous deletion (Additional file 2: Figure S5C, D). Both deletions were confirmed by the PCR using primers that flanked the respective deletions (Additional file 2: Figure S5B and S5D). Altogether, our results suggest that PCR amplification of DNA library after the first hybridization capture is an optional step.

We again performed dCATCH-Seq on the human MHC region with no PCR amplification after the first hybridization capture with DNA libraries prepared from GM12878 and U937 cells. Similar to results from K562 cells, we reproducibly captured the entire MHC region with a target specificity of ~90% (Fig. 3a, Additional file 2: Figure S6 and Table 2) in these two cell genomes. A comparative analysis of the allelic depth for 8913 heterozygous SNVs identified across the MHC region for GM12878 cell lines showed that both alleles were almost equally captured (Fig. 3b). The result was consistent with a previous study showing that probes in long length (e.g., > 150 bases) are tolerant to polymorphisms [19]. We also observed a similar result for the allelic depth of 7116 heterozygous SNVs called in U937 cells (Additional file 2: Figure S7). We further evaluated the accuracy of variant calls on the MHC region. Compared to microarray genotyping data, there were 277 SNPs within the sequenced target that overlapped with microarray probes. Among them, three SNPs (two were heterozygous and one homozygous in our dCATCH-Seq data) showed the discordance with microarray calls. Overall, the genotyping accuracy was ~99% in agreement between the dCATCH-Seq approach and microarray data, which is consistent with our previous observations shown above.
Fig. 3

Double capture and sequencing of the MHC region in the GM12878 genome. a Genome-wide distribution of read coverage in GM12878 genome for dCATCH-Seq. Red bars shown above chromosomes represent the read coverage in 5 kb windows. Black bars shown below chromosomes represent the gap (unassembled) regions in reference human genome. b Scatterplot of read counts (log2-transformed) for both alleles called among heterozygous variants called within the MHC region. Colors in a rainbow mode represent the density of heterozygous variants

We successfully obtained genotypic information for 26 HLA genes in two cell genomes and determined HLA typing with dCATCH-Seq data (Additional file 1: Table S3). To evaluate the accuracy of HLA genotyping, we compared our results with a previous report in the GM12878 genome [20]. The results showed that among 15 typed HLA genes, 13 genes were concordantly typed, especially the five classic HLA class I (HLA-B, C) and HLA class II (HLA-DQA1, DQB1, DRB1) genes that were further validated in this previous study [20]. Taken together, regardless of selected genomic features and size, dCATCH-Seq is a robust method to capture any DNA sequences with extremely high target specificity with no substantial diminution of library diversity and introduced bias.

Double capture followed by bisulfite sequencing

Targeted bisulfite sequencing technology is a robust approach to capture regional DNA methylation levels [21]. To demonstrate the utility of the double capture approach coupled with bisulfite sequencing, we used the dCATCH-Seq procedure on the same targets shown above followed by bisulfite conversion and sequencing. Due to the severe effect of library degradation by bisulfite treatment [22], additional PCR cycles are required to amplify the bisulfite-treated DNA library (e.g., > 20 cycles), which results in high PCR duplication rate in the bisulfite sequencing data. We first determined whether the PCR amplification would introduce bias in quantifying DNA methylation levels. We compared the DNA methylation levels based on a read coverage threshold of at least 10 between two conditions (deduplication of PCR amplified reads or not). The sequencing results for both conditions tested in two cells were summarized in Additional file 1: Table S4. A comparative analysis showed a high concordance rate of CpG methylation levels between the two conditions across both the chr11 region (n = 2666, R2 = 0.99, P < 1X10−16, Additional file 2: Figure S8A) and the MHC region (n = 3942, R2 = 0.97, P < 1X10−16, Additional file 2: Figure S8C). Similar patterns were found when comparing non-CpG methylation calls in either CHG or CHH dinucleotide context on both regions (Additional file 2: Figure S8B and Figure S8D).

Lastly, we compared DNA methylation profiles between dCATCH-Seq and whole-genome bisulfite sequencing (WGBS) data in the same cell lines. We used the targeted region on chr11 as an example, and the result showed a high concordance (n = 208, R2 = 0.82, P < 1X10−16, Fig. 4a) of CpG methylation levels detected by both methods. Similarly, both determined a low DNA methylation profiling of the CHG and CHH sites (Fig. 4b and c), which are predominantly found present in brain tissue and embryos [23–25]. A similar pattern with a slightly less correlation (R2 = 0.65, P < 1X10−16, Fig. 4d and e) was observed across the MHC region in GM12878 cells. Altogether, we conclude that the dCATCH-Seq method could also be used to investigate DNA methylation profiles across any genomic regions of interest, and additional PCR cycles did not introduce bias in methylation calls.
Fig. 4

Identification of DNA methylation levels by double capture followed by bisulfite sequencing. Correlation of DNA methylation levels at a region on chr11 (a-c) and on the MHC region (d-e) identified by dCATCH-BSseq (y-axis) and whole-genome bisulfite-sequencing (WGBS, x-axis) techniques in the CpG, CHG and CHH dinucleotide contexts, respectively. Colorful dots shown in A-D denote the density of DNA methylation sites, and histograms shown outside the axes denote the distribution of DNA methylation levels

Discussion

For efficient targeted sequencing for research and diagnostic purposes, capture of DNA or RNA sequence with high specificity is critical. In this study, we presented an improved targeted DNA sequencing approach by using a strategy of two rounds of hybrid selection. Relative to our previous and other reports about the target specificity [9–15], the present approach significantly increases the efficiency of target enrichment. A previous study also used a similar strategy, but captured small, non-continuous and less complex genomic intervals (<20 kb) with array-based synthesized probes [26]. We also demonstrate high accuracy and reproducibility of the dCATCH-Seq method in DNA methylation quantitative analyses.

Owing to different design and purpose on capturing genomic regions, a comparison of the dCATCH-Seq method with other target enrichment platforms, e.g. exome enrichment, is generally inappropriate. Given that the enrichment technique for some commercial target enrichment platforms [7], including TruSeq (Illumina), SureSelect (Agilent), SeqCap (Roche NimbleGen), xGen (IDT), MYbaits (MYcroarray) and FleXelect (FlexGen), is similar with the dCATCH-Seq, all based on the hybrid capture, we speculate that the strategy of two-round hybridization approach might be plausible for these hybridization-based target enrichment platforms regardless of target size.

Capture of the entire MHC region by dCATCH-Seq with high efficiency is one of the most promising applications in this study. Because this region is broadly associated with numerous diseases, including cancers [27–29], autoimmune [30–32] and infectious diseases [33], etc. Diverse genetic interactions among MHC, Killer-cell Immunoglobulin-like Receptors (KIRs) and T-cell Receptors (TCRs) are also reported in human populations [34–36].

Several additional efforts could be taken to extend the application of our method with other techniques, such as targeted RNA-seq [37], single cell targeted sequencing [38], hybrid capture of viral or transgene integration sites in host genomes [39], enrichment of the human ancient DNA extracted from archaeological samples (e.g., bones) [40], cross-species DNA capture of ultraconserved elements (UCEs) or mitochondrial genomes for phylogenomic studies [41–43]. Conversely, the double capture approach is probably also useful for negative selection to remove unwanted molecules or by-products, such as contaminating DNA within a sample. For example, it is possible to apply the double capture approach to maximally eliminate the rRNA molecules for total RNA-seq [44]. Likewise, because there are approximately 30–50% of sequenced reads mapped to the mitochondrial genome for transposase-accessible chromatin with sequencing (ATAC-seq) technology [45, 46], the double capture strategy could be probably applicable to remove the mitochondrial DNA sequence.

Finally, some limitations remain in this study. The first limitation is the unequal coverage depth across targeted regions, which is a common issue for the target enrichment methods [7]. Two potential factors could explain this issue. One factor might be due to the usage of blocking reagents, a common and necessary strategy to reduce the background signal for hybridization-based assays. The other factor could be due to the inherent DNA sequences, including G + C content, number and types of repetitive elements across targeted regions. The second limitation is the applicability and efficiency of the double-capture strategy for low-input DNA samples, particularly when the input DNAs down to the levels of tens of nanogram. Because the low amount of DNA input will substantially increase the PCR duplicates in sequencing data and subsequently reduce the superiority for the double-capture approach. Such trade-off between PCR duplicate rates and DNA amount has also been reported in previous target enrichment method [47].

Conclusions

In conclusion, the dCATCH-Seq approach is a powerful and scalable approach to interrogate genetic and epigenetic features on genomic regions of interest, and has the potential to be further combined with other functional genomics approaches.

Methods

Cell culture and DNA extraction

GM12878 B-lymphoblast, K562 (a generous gift from Dr. Myer’s lab at HudsonAlpha Institute) and U937 cell lines (purchased from ATCC, cat. no. CRL-1593.2) were cultured in RPMI-1640 medium, supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine and 1% penicillin-streptomycin in a 37 °C incubator with 5% CO2. Cells were collected when grown to ~90% confluence. Following three PBS washes, genomic DNA was extracted using proteinase K and phenol-chloroform method. Genomic DNA concentration was determined by Qubit fluorometer (ThermoFisher).

Enrichment of targets by two rounds of hybridization

All BAC-based probes across intended regions were obtained from Ubiquity Genomics, Inc. DNA library construction, target capture and enrichment were previously described [13] with a few modifications. Briefly, input DNA quantities of 1 μg and 3 μg were used for standard and bisulfite converted dCATCH-Seq approaches, respectively. DNAs were sheared to ~250 bp in size using the Covaris-S220, followed by end-repair, dA-tailing and paired-end Illumina adapter (methylated adapters used for bisulfite sequencing) ligation using NEBNext reagents or Biodynami NGS DNA library prep kit. For the first capture, a hybridization reaction was assembled containing 1× library DNA, 30× human Cot-1 DNA (American Genetics), 2500× BAC-derived DNA probes, and 1× hybridization buffer and denatured at 95 °C for 5 min followed by incubation of the reaction at 65 °C for ~48 h. The first capture was performed according to our previous protocol with streptavidin-coated Dynabeads (ThermoFisher), that were washed once at room temperature for 10 min in 1× SSC with 0.1% SDS and twice at 65 °C for 10 min in 0.1× SSC with 0.1% SDS. After the first capture, an optional PCR enrichment step of the captured library was included for the dCATCH-Seq method, with 2X KAPA HiFi master mix (KAPA Biosystems) under the PCR conditions: 45 s at 98 °C; ten cycles of 15 s at 98 °C, 30 s at 60 °C, and 45 s at 72 °C. The hybridization conditions for the second capture were identical with the first, except for incubation of the hybridization reaction at 65 °C for ~24 h, and the captured samples were washed twice at room temperature by 10 min in 1× SSC with 0.1% SDS and three times at 65 °C by 10 min in 0.1× SSC with 0.1% SDS. Finally, DNA was amplified by PCR using indexed primer pairs using the same PCR conditions as above. For the dCATCH-Seq bisulfite sequencing approach, the captured DNA library was bisulfite converted using the EpiTect Bisulfite kit (Qiagen) according to the manufacturer’s instructions for small quantities of fragmented DNA. The bisulfite-treated DNA was amplified by using 5 U Platinum Taq DNA Polymerase (ThermoFisher) under the PCR conditions: 98 °C for 1 min, followed by 20–26 cycles of (95 °C for 30 s and 62 °C for 3 min). We confirmed the amplification and correct product size range by gel electrophoresis on a 1.7% agarose gel. Amplified libraries were purified with SPRI beads, and library sizes determined by Agilent Bioanalysis DNA high sensitivity (Agilent), and library concentrations quantified by KAPA Quant kit for Illumina (KAPA Biosystems). Libraries were sequenced according to standard Illumina protocol on MiSeq or HiSeq 2500 sequencers.

Public data collection

We collected (1) microarray genotyping data with two replicates (sample ID: GSM1028244 and GSM1028245) for the U937 cell line, (2) WGBS data for K562 (sample ID: GSE86747) and GM12878 cell lines (sample ID: GSM1002650), and (3) SVs data from a whole-genome sequencing study for K562 cells [18].

Data processing and statistics

Reads were demuxed based on their index sequence at the Genomic Services Laboratory at HudsonAlpha. After removal of adapter sequences, low-quality reads, and trimmed reads that were shorter than 20 bp by using cutadapt (v1.3.1), filtered reads were aligned with bowtie2 (version 2.1.0) [48] to the human hg19 reference genome. Reads were re-aligned, recalibrated and SNVs and Indels were called using the GATK toolkit (version 3.3) [49]. Variants were filtered for quality as previously described [11]. Briefly, we filtered out variants as follows: (1) mapping quality score < 20; (2) ≥ 3 SNPs detected within 10 bp distance; (3) variant confidence/quality by depth < 2; (4) strand bias score > 50; (5) genotype score < 15; (6) read depth < 10. The reads per million (RPM) was calculated as the read counts per 200-bp non-overlapping window aligned on the target region divided by per million reads scaling factor. Read mapping was visualized in the Integrative Genomics Viewer (IGV) software [50]. With BAM files as input, HLA genes were typed using the SOAP-HLA [15] with default parameters. Structural variants were called by using Pindel [51] with ≥4 supporting reads and ≥100 bp in size. The sequencing data from the bisulfite protocol with dCATCH-Seq and WGBS were aligned and DNA methylation levels at CpGs and non-CpGs (CHG and CHH dinucleotides) were estimated by using bismark (version v0.14.1) with default parameters [52]. CpGs, CHGs or CHHs with at least 10X coverage were retained for comparative analyses. Except for relevant programs described, all other bioinformatics analyses were implemented using customized Perl scripts and R programming. All customized codes are available upon request.

Verification of structural variation

Primer pairs were designed (Additional file 1: Table S5 in and Additional file 2: Figure S5) for each SV by using 200-bp sequences flanking both sides of the deletion region. PCR products were amplified with extracted genomic DNA templates from cells with 5 U NEB Taq DNA Polymerase under the PCR conditions: 95 °C for 3 min, followed by 30 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 45 s. PCR products were run on 2% agarose gels.

Abbreviations

dCATCH-Seq: 

Double CATCH-Seq

KIR: 

Killer-cell Immunoglobulin-like Receptor

MHC: 

Major histocompatibility complex

SNP: 

Single nucleotide polymorphism

SNV: 

Single nucleotide variant

SV: 

Structural variant

TCR: 

T-cell Receptor; RPM: reads per million

WGBS: 

Whole-genome bisulfite sequencing

Declarations

Acknowledgments

We thank the members of the Genomic Services Laboratory at HudsonAlpha for providing sequence data.

Funding

This study was supported by the HudsonAlpha Institute Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

All data generated during the current study are available in the NCBI Sequence Read Archive under Study Accession number SRP114449. All the supporting data are included as Additional Files.

Authors’ contributions

YZ and JS designed and optimized the method with intellectual contribution from KD and DA. YZ performed the experiments and analyzed the data. JS and KD assisted in the experiments. YZ prepared the manuscript with revision from JS, KD and DA. All authors read, interpret and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

CATCH-Seq is used by both academic and commercial enterprises, including Ubiquity Genomics, which was founded by D. Absher. The remaining authors declare no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
HudsonAlpha Institute for Biotechnology, Huntsville, USA
(2)
Ubiquity Genomics Inc., Huntsville, USA

References

  1. Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, Shendure J. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12(11):745–55.View ArticlePubMedGoogle Scholar
  2. Fu W, O'Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, Gabriel S, Rieder MJ, Altshuler D, Shendure J, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2013;493(7431):216–20.View ArticlePubMedGoogle Scholar
  3. Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, McGee S, Do R, Liu X, Jun G, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science. 2012;337(6090):64–9.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR, Zumbo P, Nayir A, Bakkaloglu A, Ozen S, Sanjad S, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A. 2009;106(45):19096–101.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Vallender EJ. Expanding whole exome resequencing into non-human primates. Genome Biol. 2011;12(9):R87.View ArticlePubMedPubMed CentralGoogle Scholar
  6. McCormack JE, Harvey MG, Faircloth BC, Crawford NG, Glenn TC, Brumfield RT. A phylogeny of birds based on over 1,500 loci collected by target enrichment and high-throughput sequencing. PLoS One. 2013;8(1):e54848.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Mertes F, Elsharawy A, Sauer S, van Helvoort JM, van der Zaag PJ, Franke A, Nilsson M, Lehrach H, Brookes AJ. Targeted enrichment of genomic DNA regions for next-generation sequencing. Brief Funct Genomics. 2011;10(6):374–86.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Meienberg J, Zerjavic K, Keller I, Okoniewski M, Patrignani A, Ludin K, Xu Z, Steinmann B, Carrel T, Rothlisberger B, et al. New insights into the performance of human whole-exome capture platforms. Nucleic Acids Res. 2015;43(11):e76.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Clark MJ, Chen R, Lam HY, Karczewski KJ, Chen R, Euskirchen G, Butte AJ, Snyder M. Performance comparison of exome DNA sequencing technologies. Nat Biotechnol. 2011;29(10):908–14.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Asan, Xu Y, Jiang H, Tyler-Smith C, Xue Y, Jiang T, Wang J, Wu M, Liu X, Tian G, et al. Comprehensive comparison of three commercial human whole-exome capture platforms. Genome Biol. 2011;12(9):R95.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Zhang Y, Li B, Li C, Cai Q, Zheng W, Long J. Improved variant calling accuracy by merging replicates in whole-exome sequencing studies. Biomed Res Int. 2014;2014:319534.PubMedPubMed CentralGoogle Scholar
  12. Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZX, Pool JE, Xu X, Jiang H, Vinckenbosch N, Korneliussen TS, et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science. 2010;329(5987):75–8.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Day K, Song J, Absher D. Targeted sequencing of large genomic regions with CATCH-Seq. PLoS One. 2014;9(10):e111756.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Yigit E, Zhang Q, Xi L, Grilley D, Widom J, Wang J-P, Rao A, Pipkin ME. High-resolution nucleosome mapping of targeted regions using BAC-based enrichment. Nucleic Acids Res. 2013;41(7):e87.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Cao H, Wu J, Wang Y, Jiang H, Zhang T, Liu X, Xu Y, Liang D, Gao P, Sun Y, et al. An integrated tool to study MHC region: accurate SNV detection and HLA genes typing in human MHC region using targeted high-throughput sequencing. PLoS One. 2013;8(7):e69388.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Samuels DC, Han L, Li J, Quanghu S, Clark TA, Shyr Y, Guo Y. Finding the lost treasures in exome sequencing data. Trends Genet. 2013;29(10):593–9.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Klonowska K, Handschuh L, Swiercz A, Figlerowicz M, Kozlowski P. MTTE: an innovative strategy for the evaluation of targeted/exome enrichment efficiency. Oncotarget. 2016;7(41):67266–76.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Yao F, Ariyaratne PN, Hillmer AM, Lee WH, Li G, Teo ASM, Woo XY, Zhang Z, Chen JP, Poh WT, et al. Long span DNA paired-end-tag (DNA-PET) sequencing strategy for the interrogation of genomic structural mutations and fusion-point-guided reconstruction of amplicons. PLoS One. 2012;7(9):e46152.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W, Fennell T, Giannoukos G, Fisher S, Russ C, et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol. 2009;27(2):182–9.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Dilthey AT, Gourraud P-A, Mentzer AJ, Cereb N, Iqbal Z, McVean G. High-accuracy HLA type inference from whole-genome sequencing data using population reference graphs. PLoS Comput Biol. 2016;12(10):e1005151.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Deng J, Shoemaker R, Xie B, Gore A, LeProust EM, Antosiewicz-Bourget J, Egli D, Maherali N, Park IH, Yu J, et al. Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nat Biotechnol. 2009;27(4):353–60.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Miura F, Enomoto Y, Dairiki R, Ito T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 2012;40(17):e136.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Ramsahoye BH, Biniszkiewicz D, Lyko F, Clark V, Bird AP, Jaenisch R. Non-CpG methylation is prevalent in embryonic stem cells and may be mediated by DNA methyltransferase 3a. Proc Natl Acad Sci U S A. 2000;97(10):5237–42.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Guo JU, Su Y, Shin JH, Shin J, Li H, Xie B, Zhong C, Hu S, Le T, Fan G, et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat Neurosci. 2014;17(2):215–22.View ArticlePubMedGoogle Scholar
  26. Schmitt MW, Fox EJ, Prindle MJ, Reid-Bayliss KS, True LD, Radich JP, Loeb LA. Sequencing small genomic targets with high efficiency and extreme accuracy. Nat Meth. 2015;12(5):423–5.View ArticleGoogle Scholar
  27. Shukla SA, Rooney MS, Rajasagi M, Tiao G, Dixon PM, Lawrence MS, Stevens J, Lane WJ, Dellagatta JL, Steelman S, et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol. 2015;33(11):1152–8.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Gragert L, Fingerson S, Albrecht M, Maiers M, Kalaycio M, Hill BT. Fine-mapping of HLA associations with chronic lymphocytic leukemia in US populations. Blood. 2014;124(17):2657–65.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Beksac M, Gragert L, Fingerson S, Maiers M, Zhang MJ, Albrecht M, Zhong X, Cozen W, Dispenzieri A, Lonial S, et al. HLA polymorphism and risk of multiple myeloma. Leukemia. 2016;30(11):2260–4.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Raychaudhuri S, Sandor C, Stahl EA, Freudenberg J, Lee HS, Jia X, Alfredsson L, Padyukov L, Klareskog L, Worthington J, et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet. 2012;44(3):291–6.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Lenz TL, Deutsch AJ, Han B, Hu X, Okada Y, Eyre S, Knapp M, Zhernakova A, Huizinga TW, Abecasis G, et al. Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases. Nat Genet. 2015;47(9):1085–90.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Zhou F, Cao H, Zuo X, Zhang T, Zhang X, Liu X, Xu R, Chen G, Zhang Y, Zheng X, et al. Deep sequencing of the MHC region in the Chinese population contributes to studies of complex disease. Nat Genet. 2016;48(7):740–6.View ArticlePubMedGoogle Scholar
  33. Trowsdale J, Knight JC. Major histocompatibility complex genomics and human disease. Annu Rev Genomics Hum Genet. 2013;14:301–23.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Parham P, Moffett A. Variable NK cell receptors and their MHC class I ligands in immunity, reproduction and human evolution. Nat Rev Immunol. 2013;13(2):133–44.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Marrack P, Scott-Browne JP, Dai S, Gapin L, Kappler JW. Evolutionarily conserved amino acids that control TCR-MHC interaction. Annu Rev Immunol. 2008;26:171–203.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Norman PJ, Hollenbach JA, Nemat-Gorgani N, Guethlein LA, Hilton HG, Pando MJ, Koram KA, Riley EM, Abi-Rached L, Parham P. Co-evolution of human leukocyte antigen (HLA) class I ligands with killer-cell immunoglobulin-like receptors (KIR) in a genetically diverse population of sub-Saharan Africans. PLoS Genet. 2013;9(10):e1003938.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C, Jeddeloh JA, Mattick JS, Rinn JL. Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat Biotechnol. 2011;30(1):99–104.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Leung ML, Wang Y, Waters J, Navin NE. SNES: single nucleus exome sequencing. Genome Biol. 2015;16:55.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Duncavage EJ, Magrini V, Becker N, Armstrong JR, Demeter RT, Wylie T, Abel HJ, Pfeifer JD. Hybrid capture and next-generation sequencing identify viral integration sites from formalin-fixed, paraffin-embedded tissue. J Mol Diagn. 2011;13(3):325–33.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Reich D, Green RE, Kircher M, Krause J, Patterson N, Durand EY, Viola B, Briggs AW, Stenzel U, Johnson PL, et al. Genetic history of an archaic hominin group from Denisova cave in Siberia. Nature. 2010;468(7327):1053–60.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Faircloth BC, Branstetter MG, White ND, Brady SG. Target enrichment of ultraconserved elements from arthropods provides a genomic perspective on relationships among hymenoptera. Mol Ecol Resour. 2015;15(3):489–501.View ArticlePubMedGoogle Scholar
  42. Jones MR, Good JM. Targeted capture in evolutionary and ecological genomics. Mol Ecol. 2016;25(1):185–202.View ArticlePubMedGoogle Scholar
  43. Mason VC, Li G, Helgen KM, Murphy WJ. Efficient cross-species capture hybridization and next-generation sequencing of mitochondrial genomes from noninvasively sampled museum specimens. Genome Res. 2011;21(10):1695–704.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Ameur A, Zaghlool A, Halvardson J, Wetterbom A, Gyllensten U, Cavelier L, Feuk L. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat Struct Mol Biol. 2011;18(12):1435–40.View ArticlePubMedGoogle Scholar
  45. Sos BC, Fung H-L, Gao DR, Osothprarop TF, Kia A, He MM, Zhang K. Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay. Genome Biol. 2016;17(1):20.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Tsompana M, Buck MJ. Chromatin accessibility: a window into the genome. Epigenetics Chromatin. 2014;7(1):33.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22(5):939–46.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Meth. 2012;9(4):357–9.View ArticleGoogle Scholar
  49. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43(5):491–8.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25(21):2865–71.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Krueger F, Kreck B, Franke A, Andrews SR. DNA methylome analysis using short bisulfite sequencing data. Nat Meth. 2012;9(2):145–51.View ArticleGoogle Scholar

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