The massively parallel sequencing technology known as next-generation sequencing (NGS) has revolutionized the biological sciences. With its ultra-high throughput, scalability, and speed, NGS enables researchers to perform a wide variety of applications and study biological systems at a level never before possible.
Today's complex genomic research questions demand a depth of information beyond the capacity of traditional DNA sequencing technologies. Next-generation sequencing has filled that gap and become an everyday research tool to address these questions.
Benefits of NGS for COVID-19
Next-generation sequencing (NGS) provides an effective, unbiased way to identify coronavirus strains and other pathogens without prior knowledge of the organisms.1 Sequencing was used to identify the novel coronavirus causing COVID-19 (SARS-CoV-2) early in the outbreak2. NGS continues to provide public health officials, vaccine and drug developers, and researchers with critical evidence, and allows labs to:
Determine the source of infection and route of transmission.
Identify and characterize co-infections and the role of complex disease.
Provide information on coronavirus strain typing to monitor viral spread.
Screen targets for possible COVID-19 therapeutics.
Comprehensively sequence respiratory pathogens (including coronaviruses and recent flu strains) and antimicrobial resistance alleles.
NGS technology has fundamentally changed the kinds of questions scientists can ask and answer. Innovative sample preparation and data analysis options enable a broad range of applications. For example, NGS allows researchers to:
Rapidly sequence whole genomes.
Deeply sequence target regions.
Utilize RNA sequencing (RNA-Seq) to discover novel RNA variants and splice sites, or quantify mRNAs for gene expression analysis.
Analyze epigenetic factors such as genome-wide DNA methylation and DNA-protein interactions.
Sequence cancer samples to study rare somatic variants, tumor subclones, and more.
Study the human microbiome.
Identify novel pathogens.
The high incidence of genetic disorders has probed the medical industry to invest in new technologies for genetic engineering and gene transfer studies. Several medical centers and research units are investing in the study of dyslexia, down’s syndrome, and other genetic inconsistencies. This has created fresh avenues for growth across the global spatial genomics and transcriptomics market. In addition to this, the use of next-generation genetic studies for understanding genetic disorders has also given a thrust to market expansion.
The importance of microbiology in genetic studies has created a boatload of opportunities for growth and expansion across the global spatial genomics and transcriptomics market. The use of spatial genomics to understand the structure and composition of genes has enabled the inflow of fresh revenues into the global market. Besides, the use of genetic studies in the domain of veterinary care has also generated humongous opportunities for market expansion. The study of human and animal genes often goes hand-in-hand for the purpose of core research and analysis.
The next-generation sequencing workflow contains three basic steps: library preparation, sequencing, and data analysis. Learn the basics of each step and discover how to plan your NGS workflow.
Before starting the next-generation sequencing workflow, isolate and purify your nucleic acid. Some DNA extraction methods can introduce inhibitors, which can negatively affect the enzymatic reactions that occur in the NGS workflow. For best results, use an extraction protocol optimized for your sample type. For RNA sequencing experiments, convert RNA to cDNA by reverse transcription. After extraction, most NGS workflows require a QC step. We recommend using UV spectrophotometry for purity assessment and fluorometric methods for nucleic acid quantitation.
Library preparation is crucial to the success of your NGS workflow. This step prepares DNA or RNA samples to be compatible with a sequencer. Sequencing libraries are typically created by fragmenting DNA and adding specialized adapters to both ends. In the Illumina sequencing workflow, these adapters contain complementary sequences that allow the DNA fragments to bind to the flow cell. Fragments can then be amplified and purified. To save resources, multiple libraries can be pooled together and sequenced in the same run—a process known as multiplexing. During adapter ligation, unique index sequences, or “barcodes,” are added to each library. These barcodes are used to distinguish between the libraries during data analysis.
During the sequencing step of the NGS workflow, libraries are loaded onto a flow cell and placed on the sequencer. The clusters of DNA fragments are amplified in a process called cluster generation, resulting in millions of copies of single-stranded DNA. On most Illumina sequencing instruments, clustering occurs automatically. In a process called sequencing by synthesis (SBS), chemically modified nucleotides bind to the DNA template strand through natural complementarity. Each nucleotide contains a fluorescent tag and a reversible terminator that blocks incorporation of the next base. The fluorescent signal indicates which nucleotide has been added, and the terminator is cleaved so the next base can bind. After reading the forward DNA strand, the reads are washed away, and the process repeats for the reverse strand. This method is called paired-end sequencing.
After sequencing, the instrument software identifies nucleotides (a process called base calling) and the predicted accuracy of those base calls. During data analysis, you can import your sequencing data into a standard analysis tool or set up your own pipeline. Today, you can use intuitive data analysis apps to analyze NGS data without bioinformatics training or additional lab staff. These tools provide sequence alignment, variant calling, data visualization, or interpretation.
Galaxy is an online genomics analysis tool that allows users to perform a number of integrative data analyses on genomic datasets. Though not a database itself, it is directly linked into many genomic resources such as the UCSC Genome Browser. Galaxy allows users to upload data, parse it, reorder columns, and change file formats for browser compatibility. Galaxy also provides several tools for data integration. For example, it has tools for dataset intersection and union analysis, enabling users to compare their datasets with annotated genomic loci, with output directly viewable on the Genome Browser. In the process, users can create and save not just new files, but entire workflows that can be re-used and shared with others. Best of all, Galaxy provides a platform to run tools developed by the community. In the near future, tools like Galaxy will provide bench scientists a one-stop-shop for data analysis: given sequencing reads, add-ons will map these reads and call peaks, allowing for subsequent analyses.
Another popular online tool is DAVID122 (http://david.abcc.ncifcrf.gov) used for GO analysis. Therefore, using the range of tools available online, with a few clicks one can map ChIP-Seq reads at Galaxy, call peaks with CisGenome, use Galaxy’s intersection tool to find overlapped genes, and finally upload the TF-bound gene list to DAVID for GO annotation .Though not as efficient as a single tool, this method allows a significant amount of analysis to be done without the need to write new software.