What is Next-Generation Sequencing Technology?

Some considerations for isolating nucleic acids:

Manual vs automated purification: Manual purification involves a technician using kits and reagents to extract DNA and RNA, which requires wet-lab training and expertise. Automated systems can be used to streamline purification workflows, increasing throughput and reproducibility, making this approach beneficial to many labs.

Measure DNA/RNA quality: Quality assessment of DNA/RNA is crucial for sequencing accuracy. Spectrophotometry measures purity and concentration, while fluorometry provides more sensitive quantification. High-quality nucleic acids support reliable downstream applications.

Some important steps involved in performing NGS library preparation include:

Amplify targets: For some types of NGS, like amplicon-based targeted sequencing, amplification is used to enrich for specific DNA/RNA sequences, helping ensure sufficient material for sequencing. Techniques like polymerase chain reaction (PCR) are commonly used to selectively amplify regions of interest.

Fragmentation: Many NGS workflows, such as whole genome sequencing, use a fragmentation step to break nucleic acids into smaller lengths for sequencing. This can be achieved through mechanical shearing, enzymatic digestion, or sonication, creating fragments of optimal size for library preparation.

Adapter ligation: Adapter ligation attaches short DNA sequences (adapters) to the ends of fragmented DNA/RNA. These adapters are essential for binding to sequencing platforms and allow for the identification and amplification of fragments during sequencing.

Size selection: Size selection steps process DNA/RNA fragments into a specific size range, helping improve sequencing efficiency and accuracy. Techniques like gel electrophoresis or bead-based methods are used to select appropriately sized fragments.

Library quantification: Library quantification measures the concentration of DNA/RNA fragments in the prepared library. Accurate quantification is crucial for optimal sequencing performance, often done using qPCR or fluorometric methods.

Library pooling: Library pooling combines multiple samples into a single sequencing run, improving throughput and cost-efficiency. Careful quantification and balancing of each library are necessary to obtain the desired amount of data for each sample from in the sequencing output.

Manual vs automated library prep: Manual library preparation involves hands-on steps that can be time-consuming and prone to variability. Automated systems standardize the process, increasing efficiency, reproducibility, and throughput, making them suitable for high-volume sequencing projects.

Some key steps in the sequencing process include:

Run planning: Run planning involves setting up sequencing parameters, including sample loading, sequencing chemistry, and run duration. Proper planning helps support optimal data quality and efficient use of sequencing resources.

Clonal amplification: Clonal amplification generates multiple copies of each DNA fragment, creating clusters or colonies. This step is essential for signal detection during sequencing, typically achieved through emulsion or bridge PCR.

Sequencing by synthesis: Sequencing by synthesis reads the nucleotide sequence of DNA fragments by incorporating labeled nucleotides into an anchored template. Each incorporation event is detected, allowing the determination of the DNA sequence. Other approaches can also be used for NGS, such as nanopore sequencing, single-molecule real-time sequencing (SMRT), and sequencing by ligation.

Base calling and data output:  Base calling converts raw sequencing data into nucleotide sequences, generating FASTQ files that include quality metrics. This step is crucial for assessing the accuracy and reliability of the sequencing run, providing essential information for downstream analysis.

Some important steps in NGS data analysis include:

Demultiplexing: Demultiplexing separates sequences based on unique barcode identifiers. This step processes the raw FASTQ data that includes all pooled libraries to correctly assign reads to write a new set of files that represent each sample, enabling accurate downstream analysis.

Quality filtering: Quality filtering removes low-quality reads and sequences with errors, helping ensure that only high-quality data are used for analysis. This step improves the reliability and accuracy of the final results.

Alignment to reference: Alignment maps sequencing reads to a reference genome, producing sequencing alignment map (SAM) and binary alignment map (BAM) files. This step determines the genomic location of sequences, quantifying read depth across regions of interest, and enabling reliable detection of genetic variation.

Variant calling: Variant calling identifies genetic mutations such as single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs) from aligned sequences, generating variant call format (VCF) files. This step is crucial for understanding genetic differences and their potential implications.

Results visualization: Results visualization uses various tools to graphically represent sequencing data, making it easier to interpret and analyze. Visualization aids in identifying patterns, anomalies, and insights within the data.

Analysis and reporting tools: Analysis and reporting tools compile sequencing data into comprehensive reports, providing detailed insights and interpretations. These tools are essential for communicating findings and making informed decisions based on the sequencing results.