The phrase “Garbage in, garbage out” is commonly used in scientific contexts to emphasize that low-quality input, such as poorly prepared DNA libraries, leads to inaccurate sequencing results. Regarding next-generation sequencing (NGS), the quality of the input material is crucial in ensuring reliable and accurate results.
High-quality library sequencing plays a vital role in generating accurate sequencing data. Optimal libraries contain purified target sequences that exhibit the correct size distribution. They are appropriately ligated to sequencing adaptors and in sufficient concentrations for the chosen sequencing platform.
Achieving this may require in-house optimization based on the sample type, specific application, and sequencing platform. However, certain universal strategies can enhance the quality of your library preparation process. This blog will outline six essential tips to help improve your sequencing library and optimize the overall sequencing outcome.
Extraction: The Critical First Step
The extraction of nucleic acids is a fundamental step in nearly all next-generation sequencing (NGS) studies, whether identifying sequence-based biomarkers in hematological malignancies or analyzing gene expression changes in solid tumors. The method you choose for extraction can significantly impact the quality of your sequencing libraries.
- Impact of Suboptimal Extraction Methods: An adequate extraction method can improve library quality. Poor cell lysis can result in insufficient nucleic acid yields, while contaminants in the sample may interfere with enzymes involved in downstream processes like ligation.
- Bias in Data: An incomplete extraction can introduce bias, particularly in studies involving gene expression analysis of various cell types. The efficiency of mRNA extraction must reflect the proportional presence of different cell types in the body to ensure accurate results. Inconsistent lysis conditions for other cell types can skew data.
- Challenges with FFPE Samples: Formalin-fixed, paraffin-embedded (FFPE) samples are often challenging for nucleic acid extraction due to chemical cross-linking. This cross-linking binds nucleic acids to proteins and other DNA or RNA strands, making obtaining clean, intact samples difficult.
- Effects of Damaged DNA: The presence of degraded DNA not only compromises the quality of the data but can also lead to misinterpretation. DNA damage can make it hard to distinguish between genuine mutations and those induced by the degradation process, potentially affecting the accuracy of sequencing results.
However, techniques are available to break these cross-links and preserve the original DNA/RNA integrity, ensuring high-quality sequencing data and minimizing the risk of false conclusions.
Reducing PCR Cycles: Balancing Amplification and Accuracy
Polymerase chain reaction (PCR) amplification is essential to generate sufficient material for sequencing, but it also increases the risk of introducing bias into the library sequencing.
- PCR Cycles and Bias: Amplifying DNA through PCR can lead to biased results, especially in regions with different nucleotide compositions. AT-rich regions amplify more efficiently than GC-rich regions because they are easier to denature. As PCR cycles increase, the risk of amplifying a limited subset of nucleic acid sequences rises, leading to reduced diversity and potential skewing of the dataset, particularly in low-concentration samples.
- The challenge with Low Concentration Samples: PCR amplification is critical for samples with low nucleic acid concentrations, but excessive cycles may distort the data. Low input levels combined with higher PCR cycles can result in significant bias, skewing the sequencing results.
- Strategies for Minimizing Bias: While reducing PCR cycles can be a solution, it’s only sometimes feasible if a certain concentration threshold is required for sequencing. To address this:
- Increase Starting Material: Optimizing the extraction process to increase the starting material can help reduce the number of PCR cycles needed, though this isn’t always possible.
- Choose an Optimal Library Kit: Selecting a high-efficiency sequencing libraries kit can minimize the need for extensive PCR cycles. Kits with efficient end repair, 3′ end ‘A’ tailing, and adaptor ligation can help reduce bias in the amplification process.
- Alternative Approaches for Better Results:
- Hybridisation Enrichment Strategy: A hybridization enrichment strategy is more robust and accurate than amplicon-based enrichment. Hybridization minimizes errors by reducing the number of PCR cycles, leading to better uniformity of coverage, fewer false positives, and superior variant detection.
By optimizing PCR cycles and choosing the right tools, you can maintain sequencing accuracy while minimizing amplification biases.
Implementing a Robust Sequence Identification Strategy
Effective sequence identification methods are essential to ensure accurate sequencing and minimize errors in downstream data analysis.
- Unique Molecular Identifiers (UMIs): UMIs are short molecular sequences that function as barcode tagging in a sample library. It allows for differentiating true variants from errors introduced during library preparation, target enrichment, or sequencing. UMIs are especially beneficial for identifying low-frequency variants and detecting PCR errors.
- Unique Dual Indexes (UDIs): UDIs involve using two different index barcodes (i5 and i7) ligated to each sequence molecule. While UMIs are applied to individual molecules in the sample, UDIs are assigned to entire libraries. Combinatorial indexing involves using combinations of i5 and i7 barcodes across multiple libraries on a sequencing plate, with each combination being unique to a specific library. In contrast, non-redundant indexing employs unique i7 and i5 pairs for each library, ensuring greater accuracy.
- Benefits of UDIs and UMIs: These strategies enhance sequencing workflows by improving demultiplexing accuracy, reducing index hopping, and ensuring precise variant identification. Integrating UDIs and UMIs into modern sequencing workflows facilitates more reliable results and data separation from multiple samples.
Using these advanced indexing strategies helps optimize sequencing performance and ensures more accurate data analysis.
Minimizing Handling Steps in Library Preparation
Library preparation can be time-consuming, with multiple steps in the lab increasing the chances of error and contamination.
- Reducing Errors: The more steps involved, the higher the risk of mistakes, such as cross-contamination or introducing contaminants that can affect sequencing quality. Even experienced researchers are not immune to human error, which can lead to incorrect data or compromised results.
- Optimized Kits to Streamline Protocols: Some suppliers, like OGT, have developed kits to reduce the complexity of library preparation. For example, OGT’s Universal NGS Complete Workflow uses optimized enzymes and buffers to minimize hands-on time and steps. Combining fragmentation, end repair, and A-tailing into one step reduces the need for extra clean-up or quality control steps, helping to decrease errors.
- Automation to Reduce Handling: To further minimize human error, consider automation. Automation reduces the need for manual handling, reducing contamination risks and improving workflow efficiency. OGT’s customized automation workflow eliminates the need for secondary equipment, enhancing throughput and improving library sequencing quality.
By simplifying processes and utilizing automation, the risk of human error can be significantly reduced, leading to more reliable and accurate sequencing results.
Accurately Quantify Your Library
Once your sequencing libraries are prepared, accurate quantification and normalization are crucial before the sequencing run begins. Precise quantification ensures optimal results.
Overestimating the library concentration can result in insufficient input for the sequencer, leading to reduced coverage. Conversely, underestimating the concentration can overload the sequencer. In the case of pooling multiple libraries, incorrect quantification can cause uneven concentrations, leading to inconsistent data quality.
Various methods for nucleic acid quantification include UV absorption, intercalating dyes, quantitative PCR (qPCR), and droplet digital emulsion PCR. While no single method is universally regarded as the gold standard, fluorometric methods may overestimate the concentration as they detect all double-stranded DNA in the sample. qPCR methods, on the other hand, are highly sensitive and precisely measure adaptor-ligated sequences.
Follow Laboratory Finest Practices
Maintaining the integrity of your samples is critical, especially when they are irreplaceable, such as patient samples or difficult-to-culture cells. A small oversight, like improper storage of an enzyme mix, can lead to the failure of your library preparation, wasting both time and resources.
To ensure a successful sequencing library, it’s essential to follow proper laboratory practices:
- Proper Storage: Always store reagents and samples at the recommended temperature and conditions to avoid degradation or inactivity, which can affect your results.
- Aliquoting Solutions: Prevent damage caused by repeated freeze-thaw cycles by aliquoting your solutions into smaller portions.
- Minimize Contamination: Clean work surfaces with the appropriate disinfectant and use laminar flow hoods or still boxes when handling sensitive samples. Be especially cautious with RNA to prevent RNase-induced degradation.
- Reagent Mixing: Thoroughly mix reagents before use to ensure even distribution of components. When necessary, keep reagents on ice to maintain enzyme activity.
- Pipetting Accuracy: Use proper pipetting techniques to minimize errors. Consider using master mixes to reduce variability from individual pipetting actions.
- Fresh Solutions: Prepare fresh 70% ethanol (ETOH) daily for washes, as evaporation can alter the concentration, potentially leading to loss of DNA during elution and compromising your samples.
- Work on Ice: Prepare reactions on ice to prevent premature activation of enzymes, which can occur at room temperature.
Adhering to these best practices can help safeguard your samples and ensure the success of your sequencing preparation.
Conclusion – Maximizing the Potential of NGS
Achieving optimal results in next-generation sequencing (NGS) requires careful attention to the factors that influence the quality and quantity of your library sequencing. While challenges may vary depending on sample types or specific applications, adhering to best practices and choosing the right library preparation kit are key to ensuring high-quality, reliable sequencing data.
By focusing on accurate sample extraction, minimizing PCR cycles, implementing robust sequence identification strategies, reducing handling errors, and accurately quantifying your library, you can significantly improve the success of your NGS experiment.
These strategies help streamline your workflow and enhance the integrity of your results, whether you’re exploring genomic biomarkers or conducting targeted sequencing studies. Partnering with experienced networks that offer optimized workflows and expert support can further elevate the reliability of your sequencing process.
If you want to simplify your NGS workflows and consistently ensure high-quality results, consider exploring advanced solutions that support your research and deliver the precision you need.
