Ngs Analysis
Ngs Analysis - Related Videos
09:18
Mycobacterial DNA Extraction using Bead Beating in Custom Buffer Followed by NGS Workflow
0 Views •
2025
This protocol shows bead-beating combined with DNA capture bead purification provides a fast and consistent method for extracting DNA from Mycobacterium tuberculosis samples, making it an effective choice for next-generation sequencing applications.
12:05
RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing
0 Views •
Cited by 5 •
2021
Here we describe the stages of sample collection and preparation for RIBO-seq in bacteria. Sequencing of the libraries prepared according to these guidelines results in sufficient data for comprehensive bioinformatic analysis. The protocol we present is simple, uses standard laboratory equipment and takes seven days from lysis to obtaining libraries.
08:15
gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair
0 Views •
2014
gDNA enrichment for NGS sequencing is an easy and powerful tool for the study of constitutional mutations. In this article, we present the procedure to analyse simply the complete sequence of 11 genes involved in DNA damage repair.
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
0 Views •
Cited by 8 •
2017
Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
08:38
Targeted DNA Methylation Analysis by Next-generation Sequencing
0 Views •
Cited by 83 •
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.
FAQs
Quality control identifies unreliable sequencing data, while read trimming removes problematic portions before later analysis. These safeguards help prevent sequencing errors and experimental noise from being carried into alignment, assembly, variant calling, or transcript quantification. In practice, they improve confidence that downstream differences reflect biological signals rather than artifacts introduced during data generation.
Alignment and assembly are alternative processing choices used to organize sequence reads relative to a reference genome. Their inclusion before downstream analysis creates a sequence-level foundation for variant calling or transcript quantification. The selected route affects how the reads are represented for subsequent comparison, helping researchers extract biological information from large sequencing datasets.
Statistical comparisons are central to separating reliable signals from noise in NGS analysis. Variant calling examines sequence differences, while transcript quantification estimates the measured abundance of transcripts. These outputs answer different biological questions: one focuses on changes in DNA sequence, and the other on RNA-related expression patterns. The distinction helps match computational results to the experiment's purpose.
After sequencing reads are available, a typical workflow begins with quality control and read trimming. Researchers then align reads or assemble them against a reference genome, depending on the analysis, and apply downstream methods such as variant calling or transcript quantification. Each stage progressively converts raw sequence information into results suitable for biological interpretation.
It can support genome characterization, gene expression studies, pathogen surveillance, and disease research. These applications use large-scale sequence information for different purposes, from examining genomic features to evaluating expression-related changes or monitoring pathogens. Because the same general computational framework supports several question types, researchers can adapt downstream analysis to the biological system under study.
By processing sequence data into variants, transcript measurements, or other interpretable results, NGS analysis helps researchers relate molecular changes to phenotypes. This connection is useful in biology because it moves beyond cataloging sequence information toward understanding how genomic or expression differences correspond to observable characteristics. It can also inform experimental design by highlighting which measurements require further investigation.