High Throughput Sequencing
High Throughput Sequencing - Related Videos
11:36
A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
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Cited by 6 •
2016
We present a protocol for the functional assessment of comprehensive single-site saturation mutagenesis libraries of proteins utilizing high-throughput sequencing. Importantly, this approach uses orthogonal primer pairs to multiplex library construction and sequencing. Representative results using TEM-1 β-lactamase selected at a clinically relevant dosage of ampicillin are provided.
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
09:14
DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments
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Cited by 21 •
2016
We describe herein an assay by coupling DNA adenine methyltransferase identification (DamID) to high throughput sequencing (DamID-seq). This improved method provides a higher resolution and a wider dynamic range, and allows analyzing DamID-seq data in conjunction with other high throughput sequencing data such as ChIP-seq, RNA-seq, etc.
09:45
Microfluidics-based High-throughput Circulating Tumor Cell Sorting and Single-cell Sequencing Technology
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2025
Circulating tumor cells (CTCs) are critical for the study of cancer progression and metastasis. This article presents a high-throughput, integrated protocol for CTC enrichment and single-CTC sequencing, improving capture efficiency and CTC purity while reducing contamination and sequencing costs, thereby advancing precision oncology research and clinical applications.
10:05
Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis
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Cited by 12 •
2017
We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.
FAQs
During library preparation, DNA may be fragmented, while RNA may first be converted to complementary DNA. Individual nucleic-acid molecules then receive adapter tags before sequencing. These operations create the material that the sequencing reaction can examine in parallel, allowing the resulting data to represent many separate molecules rather than a single continuous starting sample.
Optical or electrical detection converts each base incorporation event into a measurable signal. Because millions of molecules are processed in parallel, the sequencing process can gather sequence information at large scale. This parallel signal collection supports analysis of genomes, transcriptomes, and microbial communities, where many nucleic-acid molecules must be assessed together.
Sequencing reactions produce reads that require computational processing before biological interpretation. Bioinformatics tools process and align these reads, then help interpret them for purposes such as identifying genetic variants, measuring gene expression, or characterizing genomes and microbial communities. The computational stage therefore connects raw sequence signals with biologically meaningful findings.
A workflow begins with DNA or RNA, followed by fragmentation or conversion of RNA to complementary DNA when appropriate. Adapters are added to individual molecules, and sequencing reactions detect incorporated bases through optical or electrical signals. Finally, bioinformatics tools process, align, and interpret the resulting reads for the selected biological question.
The workflow can begin with DNA or RNA. DNA may be fragmented, whereas RNA is converted to complementary DNA before individual molecules receive adapter tags. Sequencing reactions then generate optical or electrical signals, and bioinformatics tools analyze the reads. Together, these components support large-scale examination of genetic information from different biological samples.
Researchers choose this approach when a study requires large-scale genetic analysis rather than examination of only a few molecules. Its supported uses include genomics, transcriptomics, pathogen surveillance, evolutionary studies, disease-mechanism research, therapeutic-target investigation, and characterization of microbial communities. The appropriate application depends on whether the goal concerns sequence variation, gene expression, or broader genome composition.
In biology, the resulting data can reveal genetic variants, quantify gene expression, characterize genomes, or describe microbial communities. These outputs allow researchers to investigate evolutionary patterns, monitor pathogens, and examine mechanisms associated with disease. The same general workflow can therefore connect molecular sequence information with biological questions about organisms, populations, communities, and potential therapeutic targets.