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Accumulating evidence demonstrates that the three-dimensional (3D) organization of chromosomes within the eukaryotic nucleus reflects and influences genomic activities, including transcription, DNA replication, recombination and DNA repair. In order to uncover structure-function relationships, it is necessary first to understand the principles underlying the folding and the 3D arrangement of chromosomes. Chromosome conformation capture (3C) provides a powerful tool for detecting interactions within and between chromosomes. A high throughput derivative of 3C, chromosome conformation capture on chip (4C), executes a genome-wide interrogation of interaction partners for a given locus. We recently developed a new method, a derivative of 3C and 4C, which, similar to Hi-C, is capable of comprehensively identifying long-range chromosome interactions throughout a genome in an unbiased fashion. Hence, our method can be applied to decipher the 3D architectures of genomes. Here, we provide a detailed protocol for this method.
Genomes carry both genetic and epigenetic information and serve as a scaffold for reading and transmitting both types of inheritable information. Insight into the 3D organization of the genome, including the multilevel folding and positioning of chromosomes within the nucleus, is essential for understanding various genomic functions . To date, two types of tools have been used to dissect chromosome structure: microscopy-based imaging technologies and more recently developed molecular and biochemical tools. DNA imaging technologies are based on electron microscopy and light microscopy (reviewed in [2-4]). Electron microscopes have been typically employed in studies using cell-free systems, whereas light microscopy-based techniques, such as DNA fluorescence in situ hybridization (FISH)  and live-cell imaging  have been applied to visualize the organization of chromosomes in the nuclei of single cells in situ. Although microscopy has provided important insights into the 3D architecture of chromosomes, including their dynamic nature and non-random organization, limitations in resolution and throughput have reduced microscopy's utility in understanding genome structure-function relationships.
During the past decade, several biochemical methods have been developed for characterizing genome architecture (reviewed in [7, 8]). By measuring spatial proximity, these new techniques offer detailed molecular views of chromosome structure beyond the resolution limits of microscopy. One subset of techniques includes chromatin immunoprecipitation (ChIP) and DamID methods, which probe physical contacts between genomic loci of interest and nuclear landmarks such as the nuclear envelope or nucleolus, yielding important information about the position of genomic loci in nuclear space [9-12]. Another set of molecular tools, including RNA-TRAP  and 3C-based methods , are able to measure the relative spatial proximity between individual genomic loci, providing insight into the local or global folding of chromosomes and into the relative positioning of individual chromosomes in relationship to one another.
The relative simplicity of 3C has led to its widespread adoption in studies of long-range chromatin interactions, making it and its derivatives the most commonly used tools for characterizing chromosome structure [15-25]. 3C is based on the principle of proximity ligation. Briefly, under conditions of very low DNA concentrations (usually less than 0.8 μg/μl), ligation between two cross-linked chromatin fragments is strongly favored over random inter-molecular ligation between two unassociated chromatin fragments . All of the restriction enzyme digestion-based 3C techniques share four experimental steps: 1) cells are fixed with formaldehyde, which cross-links chromatin interactions; 2) the cross-linked chromatin is digested with a restriction enzyme (RE1); 3) DNA ends are ligated under conditions that favor intra-molecular ligation (proximity ligation); and 4) cross-links are reversed and DNA is recovered. However, the various 3C derivatives differ in their downstream steps for detecting chromatin interactions.
We recently developed a genomic method for mapping all the chromatin interactions that occur within a genome in an unbiased manner . Briefly, the method starts with construction of a 3C library, followed by digestion of the library with a second restriction enzyme (RE2). As in the 4C protocol, the resulting DNA fragments are circularized to form small DNA circles. The circular DNA is subsequently digested again with the primary 3C RE1 to linearize the DNA. The reopened RE1 sites serve as anchoring sites for the interacting DNA fragments and are ligated with an adapter containing an EcoP15I restriction site. The anchoring sites are then marked with biotin through DNA circularization, and the DNA circles are cut by the enzyme EcoP15I to produce biotin-labeled paired-end tags of 25-27 bp. The resulting biotin-labeled paired-end tags, representing the interacting DNA fragments, are pulled down with streptavidin beads, and paired-end sequencing enables the detection of ligation junctions (Figure 1).
Chromatin interaction libraries generated with our method consist of DNA molecules with uniform structure and size (Figure 1B), unlike those constructed with other recently developed similar methods such as Hi-C [19, 21] and TCC . This unique feature of our method provides a straightforward way to calculate the interaction frequency of each individual chromatin interaction. Therefore, our method can be very useful for characterizing the 3D architectures of relatively simple genomes at unprecedented resolution (kb) as well as for the identification of functionally relevant (statistically significant) long-range chromatin interactions between distant genomic elements (such as promoter-enhancer interactions) on a whole-genome scale. In principle, this method is applicable to all genomes. Here, we describe the step-by-step protocol for the haploid budding yeast genome.
To capture dynamic chromosomal contacts, it is necessary to covalently link the interacting protein-protein or protein-DNA partners together. There are several cross-linking agents available. Among them, formaldehyde is the most widely used because 1) formaldehyde is cell-permeable; 2) the cross-linking reaction mediated by formaldehyde is very efficient and readily controllable (usually temperature and reaction time are the two adjustable parameters); 3) formaldehyde-mediated cross-linking can be conveniently reversed; and 4) formaldehyde is readily commercially available. Hence, we used formaldehyde to cross-link yeast cells. Unlike mammalian cells, yeast cells are protected by a cell wall. To achieve efficient restriction enzyme digestion of the chromosomes in the yeast nuclei, it is necessary to disrupt the cell wall and to isolate spheroplasts. There are two alternative strategies to prepare cross-linked yeast spheroplasts: either isolating spheroplasts before carrying out cross-linking or cross-linking the cells first before isolating spheroplasts. However, it is possible that the 3D organization of the yeast genomes might be disturbed during the processing of spheroplast isolation before cross-linking. We therefore chose the latter strategy. Another general consideration before starting a 3C-based experiment is related to the probabilistic nature of the results from 3C-based techniques. 3C methods are unable to describe the topological variability between individual cells in a given population, whereas genome topologies can be quite different between cells at different cell cycle stages. Therefore, in certain experiments when cell homogeneity is a necessity, yeast cells may first be synchronized at a particular cell cycle stage before subjected to cross-linking. Finally, it is critical to optimize cross-linking conditions, since insufficient cross-linking will result in missing some chromatin interactions, while over-cross-linking will lead to inefficient RE digestion and higher noise levels due to random chromatin collisions. As shown in Figure 2A, we found that cross-linking with 1% formaldehyde at room temperature for 10 minutes is an appropriate condition for yeast cells.
Efficient RE1 digestion of chromatin is critical for the successful construction of a 3C library. Criteria for RE1 choice are discussed in detail in section 2.2.1. Before RE1 digestion, the cross-linked spheroplasts need to be treated with the anionic surfactant, sodium dodecyl sulfate (SDS), to remove non-cross-linked proteins and to make the chromatin accessible for RE1 digestion. However, SDS treatment at 65 °C can reverse formaldehyde-mediated cross-linking. Hence, it is important to optimize the incubation time and temperature for SDS treatment. We found that treating cross-linked spheroplasts with 0.6% SDS at 65 °C for 20 minutes followed by 1 hour at 37 °C with shaking can lead to good outcomes. After chromatin solubilization, the non-ionic detergent, Triton X-100, should be added to the reaction to neutralize the SDS before carrying out RE1 digestion.
All 3C methods are based on proximity ligation, the principle of which is that extremely low DNA concentrations strongly favor ligations within a single molecule over ligations between two molecules. Linked DNA fragments within the same chromatin complex behave as a single molecule. Hence, low DNA concentrations will reduce the noise from random inter-molecular ligations. However very low DNA concentrations require large reaction volumes, increasing cost, and complicating the recovery of DNA. We chose to carry out the 3C ligation reaction in a volume of 25 ml with a DNA concentration of 0.3 or 0.5 ng /μl.
After 3C ligation, proteins in the chromatin complexes are digested by proteinase K in the presence of SDS. DNA is then recovered by isopropanol-mediated precipitation.
At this point, the construction of a typical 3C library is completed. However, DNA samples of a 3C library are usually not suitable for high throughput sequencing on current next generation sequencing platforms due to their large range of size distribution. For example, the size distribution of a HindIII-mediated 3C library of the haploid yeast genome ranges from less than 1kb to bigger than 10kb (Figure 2A). Hence, all the downstream experimental steps aim at coupling the 3C technique with the next generation sequencing technologies to achieve comprehensive identification of chromatin interactions on a whole-genome scale. In the 3C library constructed above, each pair of interacting DNA fragments is joined together at a RE1 site. Hence, these RE1 sites can serve as markers for the chromatin interactions. To label these RE1 sites with biotin for further isolation, it is necessary to re-open these sites. To release these RE1 sites without losing the chromatin pairing information, it is necessary to find a second anchor point for each pair of interacting DNA fragments. The more frequently occurring RE2 sites can serve as the second anchor points.
After RE2 digestion, DNA fragments in the 3C library are circularized to form DNA circles. Hence, each pair of the interacting DNA fragments is now connected via an RE1 site at one end and an RE2 site at the other end (Figure 1B). DNA fragments that failed to form circles can be degraded by the ATP-dependent plasmid-safe DNase. Since the purified circular DNA is not stable, it must be linearized immediately.
EcoP15I is a type III restriction-modification enzyme. In the presence of ATP, EcoP15I functions as a site-specific DNA endonuclease whose cutting site is 25-27 bp downstream of its recognition site, whereas in the presence of S-adenosylmethionine and absence of ATP, Ecop15I can also function as a DNA methyltransferase. In this method, we first use its DNA methyltransferase activity to methylate the EcoP15I sites in the yeast genome to protect them from being cleaved by EcoP15I. We then take advantage of its unique restriction enzyme activity to produce paired-end tags, which represent the pairs of interacting DNA fragments.
All subsequent steps may be carried out in DNA LoBind micro-tubes (Eppendorf).
The paired-end tags produced by EcoP15I digestion need to be end-repaired and ligated to appropriate sequencing adaptors before the library can be amplified and subjected to high throughput sequencing on a selected sequencing platform. In step 45, we describe how we carried out the ligation reaction with our custom sequencing adaptors for the Illumina sequencing platform, which provides an example. Indeed, it should be more efficient to use the commercially available Illumina “Y-shape” PE adaptor.
The biotin-labeled DNA fragments can be isolated using commercially available streptavidin Dynabeads, and the two nicks in each DNA fragment resulting from the ligation reaction (step 45) need to be repaired by DNA polymerase I.
It is important to ensure linear amplification of the library.
Since our method is an extension of 4C, all of the general considerations for 3C [28, 29] and 4C [7, 30] experiments also apply to this protocol. Here, we highlight some issues which, based on our experience, are critical to achieving the best outcomes.
There are three general considerations regarding the choice of the restriction enzymes for the first (RE1) and second (RE2) digestion: 1) cutting frequency -- usually RE1 should be a 6-base cutter, whereas RE2 should be a 4-base cutter; 2) sensitivity to DNA methylation – neither RE1 nor RE2 should be sensitive to DNA methylation; and 3) digestion efficiency, which is especially important for RE1, because not all restriction enzymes are able to digest cross-linked chromatin efficiently. To date, a few enzymes, such as HindIII, EcoRI, BglII, and Nco I, have shown good performance. We also suggest an added consideration specific to the choice of RE2. Since the recognition sites of any given enzyme are not evenly distributed throughout a genome, to obtain higher resolution from a 3C library generated by an RE1, we suggest that two different RE2 combinations be used to generate two sub-libraries for sequencing. On the one hand, since these two sub-libraries are from the same 3C library, they should overlap significantly. On the other hand, since the genome-wide distributions of the recognition sites of the two RE2 enzymes are different, the two sub-libraries should complement one another. Hence, to maximize their complementarity, the greater the difference between the recognition sequences of the two RE2, such as MspI (CCGG) and MseI (TTAA), the better.
There are at least five types of noise associated with 3C experiments: 1) cross-linking-captured random collisions occurring within and between chromosomes; 2) incomplete digestion products from the restriction enzyme-mediated chromatin fragmentation step; 3) ligation of restriction sites at either end of a DNA fragment (self-ligation); 4) re-ligation of immediately adjacent DNA fragments (adjacent ligation); and 5) random inter-molecular ligation of non-adjacent DNA fragments due to Brownian motion of DNA fragments in solution during proximity ligation. Although the last four types of noise can be eliminated at the data analysis stage by employing appropriate bioinformatic tools, the inefficiencies they introduce impede large scale sequencing efforts. Furthermore, bioinformatic tools may not completely eliminate noise arising from random collisions because the frequency of such non-specific interactions is related to the genomic distance between the two interrogated sites . Hence, it is important to optimize the respective experimental conditions to reduce the various types of noise during library construction. For example, the frequency of random inter-molecular ligation during proximity ligation is significantly influenced by DNA concentration. However, as mentioned in section 2.1.3, although low DNA concentrations will reduce the noise from random inter-molecular ligation, they will also require large volumes for the ligation reaction. To estimate the DNA concentration that is sufficient to limit the frequency of random inter-molecular ligation to an acceptable level during 3C ligation, we previously constructed two independent sets of experimental libraries differing by DNA concentration (~0.5 μg/ml or ~0.3 μg/ml) at the 3C ligation step . We observed that both conditions yield similar results (see discussion in section 3.3 below), indicating that a DNA concentration of ~0.5 μg/ml is sufficient to yield good outcomes.
This is a lengthy protocol and we suggest carrying out quality control analysis for several critical steps.
The efficiency of RE1 digestion and 3C ligation can be qualitatively assessed by DNA gel electrophoresis (Figure 2A). For example, after Hind III or Eco RI digestion of cross-linked yeast cells, genomic DNA should run as a smear with the majority of the bands smaller than 10kb, whereas, after subsequent 3C ligation, there should be an apparent shift of the smear to a larger size range (Figure 2A). The efficiency of RE1 digestion can also be quantitatively assessed using real-time PCR assays. In our previous studies, by surveying multiple randomly picked RE1 sites throughout the genome, we observed that the digestion efficiency of cross-linked yeast chromosome by Hind III or Eco RI was usually around 90% .
Based on our experience, about 30% of RE2 digested DNA template will form circular DNA during the circularization step (step 27), and at the end of step 32, only 10% of the DNA will remain (i.e. 12 μg RE2 digested DNA template will yield about 1.2 μg RE1 linearized DNA after step 32). Too little remaining DNA indicates inefficient DNA circularization, whereas too much remaining DNA indicates inefficient digestion of ATP-dependent plasmid-safe DNase.
It is important to keep the molar ratio between the EcoP15I enzyme and the DNA template as close as possible to 1:1, as both excess and insufficient amounts of enzyme will result in inefficient digestion. The PCR products of the library generated with this protocol should run in a tight band (207-209 bp, when using the Illumina paired end adaptors) on a 3% agarose gel (Figure 2B), whereas inefficient EcoP15I digestion will result in an apparent decrease in the intensity of the specific band and the presence of a background smear (Figure 2C).
To characterize the genome topology and to uncover its potential functional implications, the completion of library construction and high throughput sequencing is just the first step of a long journey---biological insights cannot be obtained without sophisticated computational analysis. Here we outline the basic data analysis pipeline we have implemented for characterizing the haploid budding yeast genome .
In principle, the sequence reads of the libraries constructed by our method can be mapped to the appropriate reference genome with any short sequence alignment algorithm (Maq, BWA, SOAP, Bowtie, etc.). For libraries constructed with budding yeast, due to the small size and relative simplicity of the S. cerevisiae genome, sequence reads can be directly mapped to the reference genome using the Maq tool (http://maq.sourceforge.net/) with default parameters . For mammalian genomes such as the human genome, which are characterized by their immense size and complex repetitive sequences, one can take advantage of the unique feature of our libraries, i.e. each paired-end genomic tag is 25-27bp in length with a RE1 site (HindIII or EcoRI) at its end. Instead of directly mapping to the human reference genome, a custom reference genome can be built by extracting the 60 bp genomic sequence flanking each RE1 site (HindIII or EcoRI, 30 bp upstream and 30 bp downstream), which is not only much smaller in size than the original reference genome but also drastically reduces the sequence complexity.
The pipeline for identifying statistically confident chromatin interactions is summarized in Figure 3. Briefly, self-ligations and ligations between adjacent restriction fragments are eliminated, and the existence of an RE1 site (e.g. HindIII or EcoRI) in each of the remaining ligation products should be confirmed. Then the mappability (i.e., uniqueness) of each RE1 (e.g. HindIII or EcoRI) fragment in the yeast genome is calculated, and all the ligation products that contained at least one unmappable fragment are discarded. To estimate a false discovery rate (FDR), the remaining intra-chromosomal interactions are subdivided into 5kb bins as measured by the genomic distance between the midpoints of the two ligated fragments in order to account for the strong influence of genomic proximity on ligation frequency. All the remaining inter-chromosomal interactions can be placed into a separate bin. In each bin, the interactions are ranked according to their sequencing frequency and a p value relative to all other possible interactions in the same bin is assigned. Lastly, the p value of each interaction is converted into a q value (defined as the minimal FDR threshold at which the interaction is deemed significant), upon which the chromosomal interactions are ranked library-wide. This approach allows us to distinguish a propensity for relatively short-range interactions, which arise from the polymer-like behavior of chromosomes, from interactions due to higher order chromosome folding.
To assess whether an experiment is successful, the results from several types of statistical analysis can be used as indicators. First, due to the polymer properties of chromatin fibers and the fact that cross-linking is able to retain chromosome conformation, experimental libraries constructed with cross-linked cells should exhibit a strong inverse correlation between the frequency of intra-chromosomal ligations of RE1 (e.g. HindIII or EcoRI) fragments and their genomic distance [19, 26]. In contrast, this polymer-like behavior should not be observed in control libraries constructed with uncross-linked cells or purified DNA. Second, for the same reason, the percentile of long-range (defined as ≥ 20 kb, non-adjacent) intra-chromosomal ligations should be significantly higher in the experimental libraries than in the control libraries . Moreover, the observed ratio of the percentage of long-range intra-chromosomal ligations (≥ 20 kb) to that of the inter-chromosomal ligations should be significantly higher in the experimental libraries than in the control libraries and also higher than the expected ratio of RE1 fragments (e.g. HindIII or EcoRI) (Figure 4). Third, for all possible combinations of the RE1 (e.g. HindIII or EcoRI) fragments in a genome, high correlations between captures from the 5’ ends and those from the 3’ ends should be observed . Fourth, high reproducibility between biological replicates should be observed.
Individual chromatin interactions identified by our method can be validated by DNA FISH and/or 3C experiments. Detailed descriptions of 3C confirmation experiments can be found in the 3C literature [29, 32]. When using real-time PCR to quantify 3C products, it is important to confirm the specificity of the PCR primers both by DNA gel electrophoresis and melting curve analysis. FISH experiments always serve as the “gold standard” for validating chromatin interactions identified by 3C-based methods. Recent studies have provided good examples of how to validate Hi-C data with FISH techniques [33, 34].
Once the chromatin interactions have been identified and validated, genome topology can be visualized and analyzed by using a variety of computational tools. For example, both folding patterns of individual chromosomes (intra-chromosomal interactions) and the interaction patterns between different chromosomes (inter-chromosomal interactions) can be visualized using either 2D heat maps (Figure 5A) or Circos diagrams (Figure 5B). As an example, the interaction pattern between budding yeast chromosomes I and III is shown as a 2D heat map in Figure 5A and a Circos diagram in Figure 5B. In both figures, the enrichment of inter-chromosome interactions around the two centromeres is apparent.
2D heat maps can also be used to visualize the chromosome-chromosome spatial relationship. As shown in Figure 5C, by analyzing each chromosome pair in terms of the ratio of observed over expected interactions, we found that the smaller chromosomes (I, III, VI, and IX) exhibit a higher probability to contact each other, while only three pairs of larger chromosomes (IV and VII, IV and XII, and IV and XV) displayed relatively high contact probabilities.
Clustering and receiver operating curve (ROC) analysis are two tools that can be employed to study structural features of chromosomes based on the information of chromatin interactions . For example, by applying a hierarchical average-link clustering algorithm, we observed that early-firing DNA replication origins clustered into at least two discrete regions (Figure 5D), which was recently demonstrated to be mediated by Forkhead transcription factors .
In summary, by comprehensively mapping the chromatin interactions using our method, we were able to generate a map at kilobase resolution of the haploid budding yeast genome, which recapitulates the well-known organizational features of the genome, including the Rabl configuration, centromere clustering, telomere pairing, and clustering of the tRNA genes. We also revealed some new structural features of the yeast genome, such as the unique conformation of chromosome XII and clustering of the early-firing DNA replication origins .
Despite the successful application of our method to characterize the budding yeast genome, transitioning to diploid mammalian genomes requires several technical issues to be considered. First, in a diploid mammalian genome, each chromosome has a homologous partner, and it is not clear whether the two homologous chromosomes interact with other chromosomes in the same way or not. Hence, it might be important to distinguish the chromatin interactions involving any given chromosome from those of its homologous partner. This issue might be addressed by using single-nucleotide polymorphism analysis to distinguish homologous chromosomes. Second, the genomes of mammals are much larger than in yeast, and much more sequencing will be required to obtain sufficient long-range chromosomal interactions to build a high-resolution map. Using our current method, the long-range interactions (non-adjacent intra-chromosomal (≥ 20 kb) and inter-chromosomal ligations) only account for about 20% of the total ligation products in a library. The percentage of the short-range interactions (non-adjacent intrachromosomal (<20kb)) is also about 20%, while various types of background noise (see Section 2.3.2) account for up to 60% of the total ligation products in a library. Therefore, achieving a high signal to noise ratio minimizes an already enormous sequencing effort. Third, in our current method the paired-end tags, representing the two interacting genomic fragments, are produced by EcoP15I digestion and are only 25-27 bp in length. Since the genome size and the size of repetitive regions are much larger in mammalian cells, the mapping efficiency of sequence reads, in particular, the mappability for repetitive regions could be relatively low. The strategy of building a reduced custom reference genome as described in Section 3.1 will dramatically improve the mapping speed but might not be sufficient to significantly improve the mappability for repetitive genomic regions. Hence, more sophisticated computational approaches, such as the approach employed by the CHIA-PET technology (in which the paired end tag is only 18 bp [16, 36, 37]), might be required. Fourth, like all other 3C-based methods, our method is associated with various types of experimental biases. For example, we observed restriction enzyme site-dependent differences in ligation efficiency and mappability differences of RE1 fragments . In a mammalian genome, the various biases will become even more severe . Hence, mammalian chromatin interaction data produced by our method may also be normalized as described in Yaffe et al . Fifth, our method contains more experimental steps than Hi-C [19, 21] and TCC , although it contains fewer experimental steps involving chromatin fragments, which are much more difficult to deal with than DNA fragments. In these regards, our method is best suited for characterizing the topologies of relatively simple genomes at high resolution (kb) and for the identification of functionally relevant (statistically significant) long-range chromatin interactions between distant genomic elements (such as promoter-enhancer interactions) on a whole-genome scale.
Supported by NIH grants P01GM081619 (CBA), P41RR0011823 (WSN), and the Howard Hughes Medical Institute (SF). We thank Ferhat Ay for his assistance in preparing Figure 4.
1 M sorbitol
100 mM potassium phosphate (PH 7.5)
10x T4 DNA ligase Buffer
500 mM Tris-HCl, PH7.5
100 mM DTT
100 mM MgCl2
10 mM ATP
TE Buffer (PH 8.0)
10 mM Tris-HCl (PH 8.0)
1 mM EDTA
HindIII-ECoP 15I-Adaptor-F 5’ /5Phos/AGC TCT GCT GTA C 3’
HindIII-ECoP 15I-Adaptor-R 5’ /5Phos/ ACA GCA G 3’
Eco RI-ECoP 15I-Adaptor-F 5’ /5Phos/AAT TTC TGC TGT AC 3’
Eco RI-ECoP 15I-Adaptor-R 5’ /5Phos/ACA GCA GA 3’
Biotin-internal adaptor-F 5’ /5Phos/CGTACAT(Bio)CCGCCTTGGCCGT 3’
Biotin-internal adaptor-R 5’ /5Phos/GGCCAAGGCGGATGTACGGT 3’
Illumina-PE-Adaptor-A-F 5’ AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T 3’
Illumina PE-Adaptor-A-R 5’ AGA TCG GAA GAG CGT CGT GTA GGG AAA GAG TGT AGA TCT CGG TGG TCG CCG TAT CAT TTT 3’
Illumina -PE-Adaptor-B-F 5’ CAA GCA GAA GAC GGC ATA CGA GAT CGG TCT CGG CAT TCC TGC TGA ACC GCT CTT CCG ATC T 3’
Illumina -PE-Adaptor-B-R 5’ AGA TCG GAA GAG CGG TTC AGC AGG AAT GCC GAG ACC GAT CTC GTA TGC CGT CTT CTG CTT GTT 3’
Illumina -lib-PCR-F 5’ AAT GAT ACG GCG ACC ACC GAG ATC TAC 3’
Illumina -lib-PCR-R 5’ CAA GCA GAA GAC GGC ATA CGA GAT CGG 3’
Gel Doc™ XR+ System (Bio-Rad)
Microcentrifuge, for example, Sorvall Legend micro17R (for 1.5 ml tubes)
Centrifuge for 15 and 50 ml Falcon tubes, for example Sorvall Legend RT
Gene Amp PCR system 9700 (ABI)
7900 HT Fast Real-time PCR system (ABI)
Nanodrop-1000 spectrophotometer (Thermo Scientific)
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