Preparation of libraries for DNA sequencing for Illumina systems involves multiple steps. In a general workflow, purified DNA is fragmented, end-repaired, and A-tailed; adapters are ligated to the DNA fragments; libraries are amplified if necessary; and the prepared libraries are cleaned, quantitated, and normalized before loading onto a flow cell (Figure 1). Since library preparation plays a critical role in obtaining high-quality data , researchers should understand the underlying principles and considerations for the key steps in the workflow.
Common DNA sequencing methods include whole-genome sequencing, de novo sequencing, targeted sequencing, and exome sequencing (discussed below) (Figure 2). DNA may also be sequenced for epigenetic studies—e.g., methylation analysis (also known as bisulfite sequencing or Bis-Seq) and DNA–protein interaction sequencing (commonly known as ChIP-Seq), which are not covered in this section. The method of choice depends on the research goals and biological questions to address [2-4].
Figure 2. Common DNA sequencing methods. Exome and gene panel sequencing are considered targeted methods, since they only include subsets of the whole genome. Some gene panels may include promoter sequences.
Whole-genome sequencing, or WGS, is performed to sequence the entire genome of an organism using the total genomic DNA. WGS data of a sample is then compared to a reference sample or control—for instance, comparison between cancer cells and normal cells—for small and large genetic variations. Examples of these genetic variations include single nucleotide polymorphisms (SNPs); single nucleotide variations (SNVs); nucleotide insertions and deletions (indels); structural rearrangements such as inversions, duplications, and translocations; and copy number variations (CNVs) (Figure 3).
Figure 3. Common genetic variations.
WGS is useful for uncovering genetic mutations in an unbiased and detailed manner. However, it requires a large amount of sample input and involves extensive data processing, especially when analyzing the human genome, which is large and complex.
When genomic data for a particular organism are either unavailable or of insufficient quality, de novo sequencing (meaning “from the beginning”) is a method of building or updating the reference genome. Although a whole-genome sample may be used in sequencing, the lack of a reference sequence necessitates assembling overlapping short sequencing reads into longer contiguous sequences (contigs) (Figure 4A) using computational tools. The main goal is to generate an overall physical map that represents the whole genome without (large) gaps.
De novo sequencing usually relies on a hybrid approach for assembling the genome: reads from long-insert paired-end sequencing, referred to as mate-pair sequencing (with higher error rate), are used to build a scaffold, and reads from short-insert paired-end sequencing (with lower error rate) are used to fill in and improve the quality of a new genome map  (Figure 4B).
Figure 4. De novo sequencing and assembly. (A) Alignment of contiguous sequences. (B) Assembly of short-insert and long-insert paired-end reads into a reference genome.
Targeted sequencing (instead of WGS) is used when the goal of the experiment is to sequence specific genes, sets of related genes, or targeted regions of a genome. An example of targeted sequencing is screening for known cancer genes in different types of cancer cells. Therefore, targeted sequencing is hypothesis-driven and requires knowledge of the sequence of the reference genes or genomic regions. Since targeted sequencing does not require analysis of the whole genome (e.g., 3.2 x 109 base pairs for human), it allows more reads, better coverage, and higher depth, and therefore improved detection of rare variants at a lower cost than WGS.
To perform targeted sequencing, samples are enriched for the sequences of interest. Among methods available for enrichment of target sequences, the two most common approaches are hybrid capture and PCR amplification .
Figure 5. Target enrichment by hybrid capture. Blue = desired sequences, red = magnetic bead–bound probes.
Figure 6. Target enrichment by PCR amplification.
Exome sequencing is a special type of targeted method to sequence protein-coding regions of the genome, called the exome . While making up only about 1–2% of the human genome, the exome harbors approximately 85% of known disease-causing mutations. Therefore, whole-exome sequencing (WES) enables researchers to focus on identifying genetic mutations and variations that are significantly implicated in diseases.
The first step in NGS library preparation for Illumina systems is fragmentation of DNA into the desired size range, typically 300–600 bp depending on the application. Traditionally, two methods have been employed for DNA fragmentation: mechanical shearing and enzymatic digestion. Typically, 1–5 mg of input DNA is required for fragmentation, but often less is needed for enzymatic fragmentation approaches.
Between the two methods, mechanical shearing is more widely used because of its unbiased fragmentation and ability to obtain more consistent fragment sizes (Figure 7). On the other hand, enzymatic digestion requires lower DNA input and offers a more streamlined library preparation workflow.
Figure 7. Comparison of percentage of each base at each position in sequencing of samples prepared by mechanical shearing vs. enzymatic digestion. Mechanical shearing shows very little bias in base representation at the beginning of reads, but enzymatic digestion shows some base imbalance at this stage.
Mechanical shearing involves breakage of phosphodiester linkages of DNA molecules by applying shear force. Widely used methods include high-power unfocused sonication, nebulization, and focused high-frequency acoustic shearing.
Figure 8. Dependence of average fragment length distribution on number of sonication cycles (1 sonication cycle = 30 sec).
Enzymatic digestion is an effective alternative to the mechanical shearing methods. Endonucleases and nicking enzymes are usually employed to cleave both strands of DNA or nick individual strands to generate double-stranded breakage. To avoid sequence bias, enzymes with less cleavage specificity and/or cocktails of enzymes are used for fragmentation. The enzymatic digestion approach typically requires lower DNA input than mechanical shearing and thus is a method of choice when you have limited samples. In addition, enzymatic digestion and then downstream library preparation steps can be done in the same tube, thus enabling automation, streamlining the workflow, minimizing sample loss, reducing contamination risks, and decreasing hands-on time.
Some users may follow transposon-based library preparation as an alternative to mechanical shearing and enzymatic digestion (Figure 9) . Using transposons, this approach fragments DNA templates and simultaneously tags them with transposon sequences, generating blunt DNA fragments with transposed sequences at both ends. Adapters (and indexes) are added via adapter-addition PCR. Therefore, some steps of the conventional workflow, such as traditional DNA fragmentation, end conversion, and adapter ligation, are circumvented when following this approach.
Following the fragmentation step, DNA samples are subjected to end repair (also called end conversion). DNA fragments produced by mechanical shearing or enzymatic digestion have a mix of 5′ and 3′ protruding ends that need repair or conversion for ligation with the adapters. The following are key steps in the process to blunt, phosphorylate, and adenylate the termini (Figure 10) .
Figure 10. End conversion process.
The end conversion process involves a number of enzymatic steps, but some commercially available kits are designed to run all these reactions in a single tube, saving time and sample loss.
Adapters are a pair of annealed oligonucleotides that facilitate clonal amplification and sequencing reactions. Identical duplex adapters are ligated to both ends of the library fragments so that oligos on the flow cell can recognize them for sequencing. In library preparation, a stoichiometric excess of adapters relative to sample DNA is used to help drive the ligation reaction to completion. Ligation efficiency is critical for conversion of DNA fragments into sequenceable molecules and thus impacts conversion rate and yield of the libraries. Because library fragments are flanked by adapters, they are sometimes called inserts.
During formation of the adapter duplexes, two strands of oligos called P5 and P7 are annealed. The P5 and P7 adapters are named after their sites of binding to the flow cell oligos. The adapters are noncomplementary at their ends to prevent their self-ligation and thus form a Y shape after annealing. This Y shape is no longer maintained if library amplification is subsequently performed (Figure 11).
Figure 11. Adapter ligation.
Looking more closely, the library adapters are usually 50–60 nucleotides long and often consist of the features described below (Figure 12) [9-10].
Figure 12. Sequencing adapters. (* = phosphorothioate linkage)
For PCR-amplified libraries and RNA-Seq libraries, unique molecular identifiers (UMI) may be included to enable tracking of every library fragment and monitoring of deviations during library amplification .
Figure 13. Multiplex sequencing with pooled libraries. (Solid and striated red and green bars = different index sequences)
Index hopping is a phenomenon associated with multiplexing or pooling of library samples. When two or more libraries are sequenced together in the same flow cell, one of the indexes assigned to one library may become swapped with that of another library (Figure 14). Index hopping has always affected multiplex libraries (e.g., from cross-contamination of indexes) but has become more prominent when sequencing is performed on patterned flow cells with exclusion amplification chemistry . Index hopping has seriously implications in subsequent data analysis, such as incorrect assignment of sequencing data from one sample (library) to another.
Figure 14. Index hopping. (* = mutation of interest from Library 1)
Two main strategies have been employed to minimize the effect of index hopping during sequencing.
Figure 15. Combinatorial dual indexes (CDI) vs. unique dual indexes (UDI).
Depending on the need for amplification, DNA library preparation methods can be categorized as PCR-free or PCR-based. In either method, care should be taken to follow protocols that yield highly diverse and representative libraries of input samples from different amounts to help generate high-quality data.
Since PCR amplification can contribute to GC bias, PCR-free library preparation is usually the preferred method to create libraries covering high-GC or high-AT sequences, to help ensure library diversity [1,15]. Note that even with PCR-free library preparation methods, bias can be introduced during cluster generation and from the chemistry of the sequencing step itself.
Compared to PCR-based methods, PCR-free libraries require higher input amounts of starting material (although improvements have been made in lowering the input requirements). This can be challenging in scenarios such as using limited or precious samples and highly degraded nucleic acids. With PCR-free libraries, accurate assessment of library quality and quantity may be difficult, compared to PCR-amplified libraries .
Nevertheless, better representation and balanced coverage offered by PCR-free libraries make them attractive for the following applications:
The PCR-based method is a popular strategy for constructing NGS libraries, since it allows lower sample input and selective amplification of inserts with adapters at both ends. However, PCR can introduce GC bias, leading to challenges in data analysis. For example, GC bias may hinder de novo genome assembly and single-nucleotide polymorphism (SNP) discovery.
A number of factors can impact GC bias, and the following factors should be considered to achieve balanced library coverage :
Figure 16. Varying levels of GC bias in libraries amplified with different PCR enzyme master mixes.
With a given PCR enzyme or master mix, an increase in the number of PCR cycles usually increases GC bias. Therefore, a general recommendation is to run the minimum number of cycles (e.g., 4–8) that generates sufficient library yields for sequencing.
Decreasing the number of PCR cycles also reduces PCR duplicates and improves library complexity. PCR duplicates are defined as sequencing reads resulting from two or more PCR amplicons of the same DNA molecule. Although bioinformatic tools are available to identify and remove PCR duplicates during data analysis , minimizing PCR duplicates is important for efficient use of the flow cell in sequencing.
Other PCR artifacts can also result in reduced library quality and complexity. These artifacts include amplification bias (due to PCR stochasticity), nucleotide errors (from enzyme fidelity), and PCR chimeras (due to enzyme’s template switching) (Figure 17) .
Figure 17. Common PCR artifacts.
An important step in NGS library preparation is size selection and/or cleanup. Depending on the library preparation protocol, it may be performed following fragmentation, adapter ligation, or PCR amplification. As its name implies, the process selects the desired fragment size range, while removing unwanted components such as excess adapters, adapter dimers, and primers.
In NGS libraries, uniformity of fragment sizes is critical to enable maximum data output and reliable data analysis because there are limitations to sequencing read length as dictated by NGS applications. If DNA inserts are much longer than recommended, some portions of the inserts remain unsequenced. On the other hand, inserts shorter than recommended result in suboptimal use of sequencing reagents and resources. A mix of short and long inserts could lower sequencing efficiency and pose challenges in data analysis.
Removal of unligated adapters and adapter dimers (two adapters ligated to each other) is crucial to improve data output and quality. Excess adapters often compete with library fragments in binding to the flow cell, lowering data output. Even worse, adapter dimers can also clonally amplify and generate sequencing “noise”, which must be filtered out during the data analysis. With the introduction of patterned flow cells, excess unligated adapters make the libraries more prone to index hopping during sequencing .
Among methods used for size selection, agarose gel–based and magnetic bead–based are two of the most popular. Sample amounts, sample throughput, protocol time, and size range of the libraries may determine the suitability of either method .
Size selection from agarose gels is essentially a gel purification process in which DNA fragments separated through the gel according to size are collected (Figure 18). In addition to being simple and effective, the method allows flexibility in gel percentages for separation and collection of fragments in a narrow range. However, it requires large amounts of sample and a long processing time, although specialized gels are available to simplify the process [21-22].
Figure 18. Size selection by agarose gel.
Size selection by magnetic beads is widely used in NGS library preparation. This method relies on binding and unbinding of DNA fragments of different lengths to the magnetic beads, which is controlled by the ratio of beads to DNA and by buffer composition (Figure 19) [20,23]. Suitability with low sample amounts, high recovery of DNA, ability to automate, and flexibility to select the desired fragment size range make this method attractive to NGS users. Nevertheless, the method may not be suitable to separate fragments that are very close in molecular weights.
Figure 19. Size selection by magnetic beads. (A) Size distribution of library fragments with respect to their size cutoff. Above the graph is a description of the basic principle of a two-sided size selection protocol. (B) Schematic of two-sided size selection workflow.
Before NGS libraries are loaded onto the sequencer, they should be quantified and normalized so that each library is sequenced to the desired depth with the required number of reads. Concentrations of prepared NGS libraries can vary widely because of differences in the amount and quality of nucleic acid input, as well as the target enrichment method that may be used. While underclustering due to overestimated library concentrations can result in diminished data output, overclustering can result in low quality scores and problematic downstream analysis (Figure 20).
Figure 20. Library clustering on a flow cell.
Microfluidic electrophoresis separates fragments in NGS libraries based on size and can estimate the quantity of different size ranges using a reference standard (Figure 21). More commonly, however, the results of fragment analysis obtained by this method are used in conjunction with the two other methods listed below for more accurate quantitation of NGS libraries.
Figure 21. Fragment analysis of libraries by microfluidics-based electrophoresis.
The fluorometric assay uses fluorescent dyes that bind specifically to double-stranded DNA (dsDNA) to determine library concentration . After a short incubation of samples with a dye, the samples are read in a fluorometer, and library concentrations are calculated by (built-in) analysis software. Although the workflow is simple and takes only a few minutes per sample, this method may not scale well above 20–30 samples because samples are often read one at a time. Nevertheless, flexible input volumes and short incubation times allow for quick and easy testing of prepared libraries for concentrations. Since the measured concentration is for total dsDNA, the average size distribution of the libraries should be taken into account for accurate quantitation.
The qPCR-based assay quantifies NGS libraries by amplifying DNA fragments with the P5 and P7 adapters (Figure 22) . A qPCR standard curve is used to determine a broad range of library concentrations, even as low as femtomolar. Since the PCR primers are designed specifically to bind to the adapter sequences, the qPCR assays detect only properly adapted, amplifiable libraries that can form clusters during sequencing. Note, though, that qPCR can also amplify adapter dimers; therefore, melting curve analysis and/or fragment size analysis should be performed to assess specificity and accuracy of quantitation by qPCR. The final library concentration is calculated based on the following formula.
Figure 22. Schematic of primer binding in library quantitation by qPCR.
After preparation and quantitation, libraries of desired quantity and quality are ready to load on a flow cell for subsequent clonal amplification and sequencing.
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