Open Access

Improved genome sequencing using an engineered transposase

  • Amirali Kia1,
  • Christian Gloeckner3,
  • Trina Osothprarop1,
  • Niall Gormley2,
  • Erin Bomati1,
  • Michelle Stephenson1,
  • Igor Goryshin4 and
  • Molly Min He1Email author
Contributed equally
BMC Biotechnology201717:6

https://doi.org/10.1186/s12896-016-0326-1

Received: 24 June 2016

Accepted: 23 December 2016

Published: 17 January 2017

Abstract

Background

Next-generation sequencing (NGS) has transformed genomic research by reducing turnaround time and cost. However, no major breakthrough has been made in the upstream library preparation methods until the transposase-based Nextera method was invented. Nextera combines DNA fragmentation and barcoding in a single tube reaction and therefore enables a very fast workflow to sequencing-ready DNA libraries within a couple of hours. When compared to the traditional ligation-based methods, transposed-based Nextera has a slight insertion bias.

Results

Here we present the discovery of a mutant transposase (Tn5-059) with a lowered GC insertion bias through protein engineering. We demonstrate Tn5-059 reduces AT dropout and increases uniformity of genome coverage in both bacterial genomes and human genome. We also observe higher library diversity generated by Tn5-059 when compared to Nextera v2 for human exomes, which leads to less sequencing and lower cost per genome. In addition, when used for human exomes, Tn5-059 delivers consistent library insert size over a range of input DNA, allowing up to a tenfold variance from the 50 ng input recommendation.

Conclusions

Enhanced DNA input tolerance of Tn5-059 can translate to flexibility and robustness of workflow. DNA input tolerance together with superior uniformity of coverage and lower AT dropouts extend the applications of transposase based library preps. We discuss possible mechanisms of improvements in Tn5-059, and potential advantages of using the new mutant in varieties of applications including microbiome sequencing and chromatin profiling.

Background

Library construction plays an important role for high-throughput next generation sequencing (NGS). Plethora of library construction methods have been developed in the past few years [13]. Most common library preparation methods follow a basic procedure with minor variations [1]. This procedure is usually lengthy and includes several steps. First, DNA is fragmented by sonication, nebulization or shearing to desired sizes. The fragmented DNA is then repaired and end-polished including blunt-end and A-tailing. Finally, platform-specific adaptors are ligated to DNA library [2, 4]. Typically, this process results in significant sample loss, and therefore, requires a DNA input amount of over 200 ng, sometimes up to 1 ug. This workflow also limits the throughput. Nextera, developed by Epicentre (an Illumina company), is an alternative approach to streamline the workflow, improve turnaround time and reduce DNA input and increase throughput. Nextera takes advantages of an in vitro transposition reaction, using a transposase Tn5 and a free transposon end that contains a transposase recognition site Mosaic End (ME) and the sequencing adaptor to form a Transposome™ complex. When this complex is incubated with target double-stranded DNA (dsDNA), the target is fragmented and the transferred strand of the transposon end including the sequencing adaptor is covalently attached to the 5′ end of the target fragment, resulting in a sequenceable DNA library [5, 6]. Combining DNA fragmentation and sequencing adaptor tagging in one single tube reaction results in fast turnaround time of less than 2 h and requires low DNA input as little as 50 ng. In addition, by varying the concentration of Transposome complexes relative to the input DNA, the size distribution of the fragmented and tagged DNA library can be controlled. Nextera libraries can also incorporate barcodes, enabling multiplexed sequencing on a single instrument run, and thus significant cost savings.

The transposase in the Transposome complex integrates the ME into target sites by joining its 3’OH termini to staggered positions on the top and bottom DNA strands of the target. This staggered joining results in a target site duplication of a defined number of base pairs, which can be used to map precisely the site of integration for the transposon [7, 8]. In genome sequencing applications, it is important that the Transposome inserts the sequencing tags into target DNA with little to no sequence bias, leading to more uniform and complete coverage of insertion sites. By using in vitro analysis of the insertion sites of a fosmid model system [9, 10], it has been reported that many transposases have preferred insertion sequence bias. For example, Tn5, Mu and Hermes bias towards the sequences containing guanosine (G) and cytidine (C) [9, 11], while Mos1 and PiggyBac bias towards the sequences containing adenosine (A) and thymine (T) [1214]. Although ligation based methods have their own bias [15], the transposases’ insertion bias is usually more pronounced. This sequence preference yields less uniform spacing of insertions and hence potentially less uniform coverage in genome sequencing.

In this study, we engineer Tn5 transposase to have altered insertion bias. The changed insertion bias in mutant Tn5-059 with mutations K212R/P214R/G251R/A338V, leads to more uniform coverage of a bacterial genome B. cereus. We further demonstrate the benefits of using Tn5-059 in human genome sequencing including more uniform coverage and less sensitivity to DNA input amount variation. We discuss the mechanism of these improvements, strategies to further improve coverage uniformity, and new applications that could take advantage of these features of engineered Tn5-059.

Methods

Tn5 mutant library construction

Tn5 mutants were generated by random mutagenesis using error prone PCR on the entire wild type Tn5 transposase (NCBI accession code ‘3ECP_A’, Additional file 1). Site saturation was performed on the modeled DNA binding site of the protein. The mutagenized Tn5 fragments were inserted into a modified pET11a vector for expression in E. coli (Illumina Madison). The vector is kanamysin resistant for plasmid stability and has a Strep Tag II-sumo fusion downstream of the T7 promoter/lac operon at the N-terminus of Tn5 coding region to aid purification. The driver mutations identified by linear regression were combined by standard site-directed mutagenesis (Qiagen).

Mutant protein expression and purification

Mutant library was plated, single colonies were selected to inoculate 1 L Luria Broth (LB) media with 50 μg/mL kan and were allowed to grow to OD600 = 0.5. Expression of Tn5 mutant transposases was then induced by addition of 100 μM Isopropyl β-D-1-thiogalactopyranoside (IPTG) and continued incubation at 18 °C for 19 h.

Cells were harvested by centrifugation and resuspended in TNE1 buffer (100 mM Tris, pH 8.0, 1 M NaCl, 1 mM Ethylenediaminetetraacetic acid (EDTA), 1 mM Dithiothreitol (DTT)) containing complete protease-inhibitor mix (Roche). A glass homogenizer was used to break-up the cell pellet before being passed through a microfluidizer three times for lysis. Sodium deoxycholate was added to the lysate (0.1% final) and the mixture was incubated at room temperature while stirring for 15 min followed by 15 min at 4 °C. While stirring at 4 °C, 5% polyethylenimine, pH 7.5 was added to the mixture (0.5% final) and stirred further for 1 h to precipitate nucleic acids which was removed by centrifugation (45,000 g for 20 min at 4 °C). Saturated ammonium sulfate was added to the supernatant at a 1:1 ratio and the mixture was stirred at 4 °C for 1 h and then centrifuged (45,000 g for 20 min at 4 °C). The pellet containing the Tn5 mutant proteins was resuspended in 10 mL TNE1, centrifuged to remove particulates and the resulting supernatant was further diluted 5× with TNE1 and loaded onto a StrepTrap High Performance (HP) column (GE Healthcare) that was equilibrated with TNE1 using an AKTA Pure (GE Healthcare).

Post load, the StrepTrap HP column was washed with 10 column volumes (CV) of 100 mM Tris, pH 8.0, 4 M NaCl, 1 mM EDTA followed by 10 CV 100 mM Tris, pH 7.5, 100 mM NaCl, 1 mM EDTA. The protein was then eluted with a 10 CV gradient using 100 mM Tris, pH 7.5, 100 mM NaCl, 1 mM EDTA, 5 mM Desthiobiotin (IBA-lifesciences). Fractions containing peaks at OD280 were pooled and applied to a HiTrap Heparin HP column that was equilibrated with 100 mM Tris, pH 7.5, 100 mM NaCl, 0.2 mM EDTA (GE Healthcare). After binding, the column was washed with 15 CV equilibration buffer followed by a 20 CV salt gradient (100 mM-1 M NaCl). A single eluted peak at 0.5 M NaCl was shown by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) to contain the Strep-Sumo-Tn5 mutants at 66 kDa. The eluted peak was concentrated in a Vivaspin 20 centrifugal concentrator with a 10 kDa MWCO then diluted 1:1 with 100% glycerol for storage at −20 °C. From this expression and purification, the yield of Tn5 mutant transposases was approximately 5 mg per 1 L culture.

Transposome assembly and activity normalization

The 19 bp Tn5 mosaic end (ME) transposon sequence (also containing the 14 and 15 bp 5’ adaptor sequence compatible with Illumina paired-end sequencing) was annealed by heating the single stranded oligos at 95 °C for 5 min then reducing the temperature 5 °C every 2 min down to 20 °C. The annealed ME’s were combined with purified Tn5 transposases at a 1.2:1 molar ratio (ME:transposase) and incubated at 37 °C for 1 h. The resulting Transposome assembly was stored at −20 °C until use.

Tagmentation activity of each mutant was normalized using the standard method and reagents specified in the Nextera DNA library preparation method (Illumina). In short, 25 ng B. cereus genomic DNA (gDNA) was tagmented by various concentrations of mutants or standard Tn5 from Illumina Nextera kit as a control. The size of resulting fragmented DNA was analyzed on Agilent’s High Sensitivity DNA bioanalyzer chip. The concentrations of Tn5 mutants were then normalized to achieve the same DNA fragment size distribution where the area under the curve between 100 and 300 bp was 20–30%, 301–600 bp 30–40%, and 601–7000 bp 30–40%, whereas the total area under the curve between 100 and 7000 bp was ≥90% (Additional file 2: Figure S1).

DNA library preparation, sequencing and data analysis

5 μL of each Tn5 mutant Transposome at normalized concentration was used to prepare sequencing libraries for E. coli gDNA (balanced genome), R. sphaeroides (GC rich genome), and B. cereus genomic DNA (AT rich genome) using the standard Nextera DNA library preparation method. The libraries were then sequenced on MiSeq following Illumina’s standard protocol. Bias plots was generated by counting the number of times each nucleotide was observed in each cycle for all the reads and reporting it as a percentage. Bias plot shows an overall tagmentation bias of a transposase. Note that the bias at each position can be independent of other positions. Coverage plots show the percentage of bases observed at different sequencing depth. They were generated using samtools’ mpileup option (http://www.htslib.org/). Normalized GC curves and AT/GC dropouts were generated using Picard Tools (http://broadinstitute.github.io/picard/).

Linear regression

Linear regression models were used to estimate the weight of each individual mutation in the insertion bias. Each linear regression model was applied to the content of the dominant nucleotide at a base position in the reads, resulting in a model per base position. E. coli sequencing results were used for fitting the models. Hence, the following nucleotides where used as the dominant nucleotide between base positions 1 and 15: GTTTA***CTGTGCG. Since Tn5 acts as a homodimer, dominant nucleotides observed at positions 6 through 9 are always complement bases to those in positions 1 through 4. Therefore, we ignored models for bases 6, 7 and 8 but kept base position 9. Although position 9 replicates the behavior of position 1 due to symmetry, position 1 is affected by sequencing artifacts so we use position 9 to better capture the features of position 1.

Ten-fold cross validation was used for training and weights were averaged through the 10 cross trainings. Input matrix for the predictor variables consisted of rows for each mutant and columns for all observed mutations. Mutations that always appeared together caused singularity in the matrix. Hence, all but one of the columns associated with those mutations were dropped. Least square method was used to solve for the weight vectors.

For each position, the mutations that had significant positive or negative weights were picked. New mutant library was created by combining mutations from different positions. Hence, mutations are grouped based on similar effect. Groups may have common mutations that have effect on multiple positions simultaneously.

Tn5/DNA binding stability assay

Standard Tn5 was shown to remain bound to its target DNA post tagmentation (unpublished results). Therefore, gap fill of the tagmented DNA by a polymerase and further amplification of the DNA by PCR will be prevented by steric hindrance. However, Tn5 dissociates from the tagmented DNA upon elevated temperature, thus allowing further gap fill and PCR of the DNA by a polymerase. The temperature required to allow PCR reaction of a polymerase can thus be used to compare Tn5/DNA binding stabilities of various Tn5 mutants. The higher temperature required, the more stable the complex.

The mutant Tn5-059 or standard Tn5 was used to tagment B. cereus gDNA in 1 mL reaction by following the standard Nextera protocol except the TD buffer was replaced by 20 mM Tris Acetate pH 7.5, 5 mM magnesium acetate. TD buffer contains magnesium, so this should not create an extra combined effect with the mutations to change the insertion bias. Aliquots of 25 μL reaction were dispensed into a PCR plate in triplicate and were incubated at 55 °C for 5 min followed by centrifugation (1000 g for 5 min at 4 °C).

For the second step containing PCR, a PCR mix was prepared by combining 200 μL PPC (PCR Primer cocktail), 200 μL i501, 200 μL i507, and 400 μL NPM (reagents supplied in the standard Nextera DNA library preparation kit). 25 μL of this mix was then added to each of the 25 μL tagmentation reaction aliquot on the PCR plate, mixed by pipette and returned to the thermocycler. The temperature gradient between 72 °C and 95 °C was generated across the 12 columns of the plate and held for 1 min, the plate was then incubated at 72 °C for 5 min followed by 98 °C for 30 s. After this gap fill-denaturation step, 5 cycles of PCR specified in the Nextera DNA library preparation method was performed. The plate was then centrifuged at 1000 g for 5 min at 4 °C. Each tagmentation-PCR amplified reaction was then purified using a Zymo Clean and Concentrator 96 well plate and eluted with 25 μL 10 mM Tris, pH 8.0. A triplicate of negative control sample was prepared following the standard tagmentation protocol including Zymo cleaning to remove Tn5 transpoase but without the PCR step. A triplicate of positive control was prepared following the standard tagmentation protocol including Zymo cleaning to remove Tn5 transposase and the PCR step to amplify tagmented DNA. All purified DNA products were then diluted 1:10 in 10 mM Tris, pH 8.0 and quantitated using Picogreen and a lambda DNA standard.

The results demonstrated that standard Tn5 releases from the tagmented DNA and thus allows subsequent gap fill and PCR reactions at 74.2 °C, while Tn5-059 does so at 76.6 °C (Additional file 3: Figure S2). The higher temperature required for Tn5-059 indicated a more stable DNA binding complex.

Results and Discussions

Random mutagenesis library of Tn5 transposase that aims to cover the entire coding region is constructed such that every mutant has approximately 1 to 6 random mutations. About 1000 such mutants are expressed, purified and used in a standard Nextera protocol to tagment E. coli genomic DNA followed by sequencing analysis of the DNA libraries. Bias plots (Fig. 1) are used to assess the insertion bias of a transposase by comparing the percentage of observed bases at each base position (or sequencing cycle) to the average base composition of E. coli genome. Standard Tn5 has an insertion bias of 21 bases, 15 of which can be observed in a sequenced read (Fig. 1a). The bias plot demonstrates a symmetry between base 1 and base 9 with the center of the symmetry at base 5 (Fig. 1a). Since Tn5 functions as a homodimer [7, 8], we hypothesize that the 9 base pair overlap of the target DNA within the two monomers results in this symmetry (Additional file 4: Figure S3). Out of the 1000 mutants we examined, many of them showed altered bias plots (two examples are shown in Fig. 1b and c), indicating their insertion bias have changed at some base positions, likely caused by the positions of mutations.
Fig. 1

Bias plots, showing percentage of observed bases at each position (or sequencing cycle). Plots show insertion bias for standard Tn5 in NexteraV2 kit and two Tn5 mutants from sequencing E. coli genomic DNA. The intensities after position (cycle) 20 are the base composition of E. coli genomic DNA. a Standard Tn5 in NexteraV2 kit. Notice the symmetry in the plot centered at position 5, between positions 1 through 9 b Tn5 mutant, notice the change in the insertion bias at positions 3 through 7 and position 12 c Tn5 mutant, notice the change in the insertion bias at positions 3 through 7, as well as higher G bias at position 1

Since every mutant in the random library usually contains more than one mutation, we use linear regression models to segregate the effect of each mutation in a mutant, and subsequently to identify all the driver mutations that cause changes in insertion bias. A separate linear regression is applied at each base position that contains an insertion bias. This led to the extraction of the individual effects of each mutation on the insertion bias at different base positions (See Additional file 5: Figure S4 and Additional file 6: Figure S5). Not surprisingly, most driver mutations are found close to the target DNA binding site or at the interface of two Tn5 monomers (Fig. 2). We then combine the driver mutations for each base position to create a set of combination mutants (Table 1).
Fig. 2

Tn5 dimer with selected driver amino acids in spherical representation. These positions are selected based on multiple linear regression models. K212 is in pink and P214 in yellow. a Top view. b Side view

Table 1

Selected positions from linear regression for further recombination and constructing super mutants. Column headers indicate the nucleotide position starting from where Tn5 inserts its ME (Also refer to Fig. 1 and Additional File 4: Figure S3)

P1

P2

P3

P4

P5

P9

P10

P11

P12

P13

P14

 

I046

T047

I046

W125

A195

A338

E146

K212

P214

 
 

R138

D119

G251

H127

M343

 

E190

   
 

T334

K249

S264

       
 

W125

K252

A338

       
 

K249

         
The combination mutants are then expressed, purified and used to prepare sequencing library from B. cereus genomic DNA. We selected B. cereus genomic DNA at this stage since it has higher AT content and Tn5 is known to have a GC insertion bias. B. cereus will help us stress our selection assay and hence better differentiation of the performance of mutants. Figure 3 shows the difference in insertion bias between NexteraV2 and mutant Tn5-059 (K212R/P214R/G251R/A338V). Interestingly, we observe a structural correlation between the positions of the mutations and the positions of the DNA bases that displayed changes in the bias plot. For example, mutations K212R/P214R in Tn5-059 are in close proximity to bases 10–15 (Fig. 2a) in the structural model.
Fig. 3

Percentage of observed bases at each cycle using standard Tn5 in NexteraV2 kit (a), or Tn5-059 (b) for sequencing B. cereus genomic DNA. In particular, the two plots differ at positions 3, 4, 6, 7, 11, 13 and 14. For Tn5-059, the bias within positions 10–15 is much closer to the overall genome composition

The observed bases between positions 10 and 15 are better separated in bias plot and are closer to the overall genome composition, indicating less insertion bias (Fig. 3b). New mutations can also create artificial distortions to the bias plot and alter the expected symmetry in the plot. For example, a single mutation at K120, which is located in the middle of the dimer complex and most probably interacts with the target DNA, can cause the target DNA overlap to alternate between 9 bp and 10 bp or to switch completely to 10 bp (Additional file 7: Figure S6). These changes in target DNA overlap will distort the symmetry in the bias plot, which in turn have a spurious effect in the linear regression models, forcing it to pick these mutations as false positive mutations. The actual effect of these mutations on activity remains unknown and needs further investigation.

We compare the full sequencing metrics of B. cereus genomic DNA prepared by Tn5-059 or standard Tn5 in NexteraV2 kit, respectively. Mutant Tn5-059 shows less under-covered and over-covered regions when compared to standard Tn5, confirming Tn5-059 yields more uniform coverage of B. cereus genome (Fig. 4a). Normalized GC plots, defined as the number of reads per window normalized to the average number of reads per window across the whole genome are shown in Fig. 4b. Tn5-059 has a more uniform normalized coverage over regions with different GC content than standard Tn5 (Fig. 4b), which is a result of improved AT dropout (Fig. 4c). In the case of B. cereus genomics DNA, we observed approximately 65% reduction in AT dropout. We conclude that the improvement observed in both coverage uniformity and AT dropout of Tn5-059 is a direct result of altered insertion bias (Fig. 3).
Fig. 4

Sequencing results for B. cereus genomic DNA. Libraries are prepared using 50 ng of input DNA. a Uniformity of Coverage. Sequencing results are down sampled to 24× coverage. Tn5-059 shows improved uniformity when compared to standard Tn5. b Normalized GC plot. Grey bar shows schematic GC composition of B. cereus genome. Tn5-059 has a more uniform coverage with less AT dropout. c AT/GC dropout percentages. Both enzyme have no GC dropout while Tn5-059 shows significant improvement in AT dropout

We further characterize the behaviors of Tn5-059 in human genome sequencing. In this experiment, activities of Tn5-059 and standard Tn5 are normalized. Similarly, a more uniform coverage is observed with Tn5-059 (Fig. 5a), resulted from approximately 35% reduction in AT dropout without significantly changing GC dropout (Fig. 5c). Consistently, we observe a more uniform normalized coverage, especially at regions with higher GC content, when compared to standard Tn5 (Fig. 5b). Another important metric for library preparation methods is library diversity, an estimate of number of unique molecules in the library generated [16]. Tn5-059 shows 20–50% higher library diversity, achieving an approximate ~64 million unique molecule for human exome sequencing, likely resulting from a more uniform coverage [17]. This feature allows less sequencing and thus lower cost to reach the same sequencing depth. It is interesting to observe that Tn5-059 forms a tighter complex with target DNA after tagmentation (Additional file 3: Figure S2). Mutations of Tn5-059 increased its positive charges in the DNA binding site. It is conceivable that tighter DNA binding of Tn5-059 increases its chance of insertion reactions with varieties of DNA motifs, and hence reduces its insertion bias. Tighter DNA binding of Tn5-059 also leads to increased efficiency of tagmenting limited amount of input DNA, as shown in chromatin accessibility studies [18].
Fig. 5

Sequencing results for Human genomic DNA. Libraries are prepared using 50 ng of input DNA. Similar enzyme concentrations of Tn5-059 and standard Tn5 are used and 2x151 bp sequencing run is performed on a HiSeqX. a Uniformity of Coverage. Sequencing results are down sampled to 20× coverage. Tn5-059 shows improved uniformity over standard Tn5 in NexteraV2 kit. b Normalized GC plot. Grey bar shows schematic GC composition of Human genome. Standard Tn5 has a clear bias towards GC rich regions while undercovers AT rich regions. c AT/GC dropout. Tn5-059 improves AT dropout while adding little on GC dropout

Tn5-059 shows high tolerance to the input DNA for a large range of DNA input (Fig. 6). With the increasing amount of input DNA, the sizes of tagmented DNA increase for Tn5. Tn5-059, on the other hand, shows steady fragment size distributions over a wide range of DNA input amount. The hypothesis is that the lowered Kd of Tn5-059 to DNA reduces the ability of Tn5-059 to sample different DNA regions with various sequences, especially when transposase is in excess of DNA. If the catalytic efficiency remains the same as Tn5 or better, Tn5-059 is likely to insert into more varieties of sequences of DNA. Further characterizations of DNA binding and insertion kinetics of Tn5 vs. Tn5-059 is required to fully understand the mechanism.
Fig. 6

Fragment size distribution after tagmentation using Tn5 and Tn5-059. Horizontal axis is the fragment sizes in base pairs, vertical axis is the amount of observed fragments at different sizes in fluorescent units [FU] a Same concentration of Tn5 is applied to three different DNA inputs (25 ng, 50 ng or 100 ng). Lower DNA input shifts the fragment size distribution to the left, an indication of smaller fragments. b Tn5-059 demonstrates little changes in fragment size distribution over the same range of DNA input

Conclusion

We have demonstrated that mutant Tn5-059 possesses the ability to tagment genomic DNA in a more uniform fashion that leads to better quality and lower costs of sequencing. In addition, Tn5-059 has higher DNA input tolerance and hence yielding a more robust system for low DNA input experiments. These features open up the utility of Nextera in genomic research. We can now enable Nextera-based library prep for microbiome sequencing, human whole genome sequencing and chromatin profiling even at the single cell level [18, 19].

Abbreviations

dsDNA: 

Double stranded DNA

DTT: 

Dithiothreitol

EDTA: 

Ethylenediaminetetraacetic acid

gDNA: 

Genomic DNA

HP: 

High Performance

IPTG: 

Isopropyl β-D-1-thiogalactopyranoside

LB: 

Luria Broth

ME: 

Mosaic End

NGS: 

Next-generation sequencing

SDS-PAGE: 

Sodium dodecyl sulfate polyacrylamide gel electrophoresis

Declarations

Acknowledgements

We thank Sheila Dodge, Niall Lennon, Maura Costello and Justin Abreu from Broad institute of MIT and Harvard for evaluating the enzyme. We also thank Ramesh Vaidyanathan and Joanne Decker from Epicentre, an Illumina company, to help with enzyme purification.

Funding

No funding was obtained for this study.

Availability of data and materials

The data discussed in this publication have been deposited in NCBI’s Sequence Read Archive (SRA) and are accessible through SRA accession numbers SRR3397074 and SRR3397076.

Authors’ contributions

MMH oversaw the project. AK performed data analysis including bioinformatics, data mining and designing combination mutants. CG designed the mutagenesis library and high throughput screening method. TO performed library preps, sequencing runs and protein purification and characterization. EB helped with mutagenesis library construction and mutant design. NG helped with high throughput assay development. IG helped with primary library screening. MS helped with protein purification. AK and CG contributed equally to this work. All authors read and approved the final manuscript.

Competing interests

A.K., C.G., T.O., N.G., E.B., M.S., M.M.H. declare competing financial interests in the form of stock ownership and/or paid employment by Illumina Inc. The remaining authors declare no competing financial interests. A.K., C.G., T.O., E.B., M.M.H. are co-inventors on patent application PCT/US2015/025889; published as US20150291942, WO2015160895A2 and WO2015160895A3.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

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)
Department of Protein Engineering, Illumina Inc
(2)
Technology Development, Illumina Inc
(3)
NEO New Oncology GmbH
(4)
Illumina Inc

References

  1. Marine R, Polson SW, Ravel J, Hatfull G, Russell D, Sullivan M, et al. Evaluation of a transposase protocol for rapid generation of shotgun high-throughput sequencing libraries from nanogram quantities of DNA. Appl Environ Microbiol. 2011;77:8071–9.View ArticleGoogle Scholar
  2. Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, et al. Library construction for next-generation sequencing: Overviews and challenges. BioTechniques. 2014;56:1–31.View ArticleGoogle Scholar
  3. Nascimento FS, Wei-Pridgeon Y, Arrowood MJ, Moss D, da Silva AJ, Talundzic E, et al. Evaluation of library preparation methods for Illumina next generation sequencing of small amounts of DNA from foodborne parasites. J Microbiol Methods. 2016;130:23–6. Elsevier B.V.View ArticleGoogle Scholar
  4. van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation sequencing: Tone down the bias. Exp Cell Res. 2014;322:12–20.View ArticleGoogle Scholar
  5. Caruccio N. Preparation of Next-Generation Sequencing Libraries Using Nextera™ Technology: Simultaneous DNA Fragmentation and Adaptor Tagging by In Vitro Transposition. In: Kwon YM, Ricke SC, editors. High-Throughput Next Generation Sequencing. Totowa: Humana Press; 2011. p. 241–55.View ArticleGoogle Scholar
  6. Adey A, Morrison HG, Asan, Xun X, Kitzman JO, Turner EH, et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 2010;11:R119. BioMed Central Ltd.View ArticleGoogle Scholar
  7. Davies DR, Goryshin IY, Reznikoff WS, Rayment I. Three-dimensional structure of the Tn5 synaptic complex transposition intermediate. Science. 2000;289:77–85. American Association for the Advancement of Science.View ArticleGoogle Scholar
  8. Reznikoff WS. Tn5 as a model for understanding DNA transposition. Mol Microbiol. 2003;47:1199–206.View ArticleGoogle Scholar
  9. Green B, Bouchier C, Fairhead C, Craig NL, Cormack BP. Insertion site preference of Mu, Tn5, and Tn7 transposons. Mob DNA. 2012;3:3. BioMed Central Ltd.View ArticleGoogle Scholar
  10. Goryshin IY, Miller JA, Kil YV, Lanzov VA, Reznikoff WS. Tn5/IS50 target recognition. Proc Natl Acad Sci U S A. 1998;95:10716–21.View ArticleGoogle Scholar
  11. Gangadharan S, Mularoni L, Fain-Thornton J, Wheelan SJ, Craig NL. DNA transposon Hermes inserts into DNA in nucleosome-free regions in vivo. Proc Natl Acad Sci U S A. 2010;107:21966–72. National Acad Sciences.View ArticleGoogle Scholar
  12. Yusa K. piggyBac Transposon. Microbiol Spectr. 2015;3:MDNA3-0028-2014.View ArticleGoogle Scholar
  13. Richardson JM, Dawson A, O’hagan N, Taylor P, Finnegan DJ, Walkinshaw MD. Mechanism of Mos1 transposition: insights from structural analysis. EMBO J. 2013;25:1–11.Google Scholar
  14. Yusa K, Zhou L, Li MA, Bradley A, Craig NL. A hyperactive piggyBac transposase for mammalian applications. Proc Natl Acad Sci U S A. 2011;108:1531–6. National Acad Sciences.View ArticleGoogle Scholar
  15. Seguin-Orlando A, Schubert M, Clary J, Stagegaard J, Alberdi MT, Prado JL, et al. Ligation Bias in Illumina Next-Generation DNA Libraries: Implications for Sequencing Ancient Genomes. PLoS ONE. 2013;8:e78575–11. Hoheisel JRD, editor. Public Library of Science.View ArticleGoogle Scholar
  16. Lander ES, Waterman MS. Genomic mapping by fingerprinting random clones: a mathematical analysis. Genomics. 1988;2:231–9.View ArticleGoogle Scholar
  17. Schlingman D, Kia A, Khanna A, Burgess J, Grunenwald H, Ruotti V, et al. TruSeq Rapid Exome: New improved exome enrichment strategy using a mutant transposase. Baltimore: American Society of human genetics. 2015.Google Scholar
  18. Sos BC, Fung H-L, Gao DR, Osothprarop TF, Kia A, He MM, et al. Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay. Genome Biol. 2016;17:1. BioMed Central.View ArticleGoogle Scholar
  19. Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4. American Association for the Advancement of Science.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017