Next-generation Sequencing
Next-generation Sequencing - Related Videos
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Next-generation Sequencing
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2021
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold. Next-Generation Sequencing Methods Although all next-generation methods use different technologies, they all share a set of standard features.
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Targeted DNA Methylation Analysis by Next-generation Sequencing
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Cited by 89 •
2015
Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.
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Isolation of Murine Retinal Endothelial Cells for Next-Generation Sequencing
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Cited by 4 •
2021
This protocol describes a method for the isolation of murine postnatal retinal endothelial cells optimized for cell yield, purity, and viability. These cells are suitable for next-generation sequencing approaches.
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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Cited by 120 •
2014
Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Cited by 2 •
2022
Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 µm.
FAQs
Library preparation places fragmented DNA or RNA into a form that can be processed in parallel. The workflow may include amplification or another processing step before sequencing begins. These steps are important because they determine how the original nucleic acid enters the high-throughput readout, allowing the resulting sequences to be aligned and interpreted computationally.
Sequencing cycles translate nucleotide incorporation or other signal-generating reactions into sequence information. Because many molecules are read in parallel, the method can generate large datasets rapidly and support biological analysis at scale. Computational alignment then helps organize the resulting reads for biological interpretation across large datasets.
Compared with traditional sequencing approaches, NGS emphasizes speed, scale, and sensitivity, making large-scale biological analysis more accessible. That distinction matters when the research question extends beyond a small number of sequences, such as examining an entire genome, exome, transcriptome, or defined target set. The chosen scope determines the biological information produced.
After sequencing, computational tools align the resulting sequences and interpret them. This stage connects the raw readout to biological questions, including identifying genetic variation, measuring gene expression, characterizing pathogens, and investigating disease mechanisms. The experiment’s value therefore depends not only on sequence generation but also on how the data are analyzed.
Whole-genome, exome, transcriptome, and targeted sequencing provide different analytical scopes. Researchers can select among them according to whether they need broad genomic information, information from a defined biological component, expression-related data, or focused analysis of selected sequences. This flexibility allows the sequencing design to match the intended biological outcome.
NGS supports genetic variation studies, gene-expression measurement, pathogen characterization, and investigations of disease mechanisms. These applications make it useful across molecular biology and genomics, while its ability to analyze biological material at large scale also contributes to personalized medicine. The resulting data can connect sequence-level observations with broader biological or clinical questions.