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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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.
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: May 10, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Harnessing virtual machines to simplify next-generation DNA sequencing analysis.

Julie Nocq1, Magalie Celton, Patrick Gendron

  • 1Institute for Research in Immunology and Cancer, Laboratory for High-Throughput Genomics, Department of Medicine, University of Montreal, QC, Canada.

Bioinformatics (Oxford, England)
|June 22, 2013
PubMed
Summary

Virtual machines (VMs) offer a solution for sharing complex next-generation sequencing (NGS) software pipelines. Benchmarking shows minimized performance issues, facilitating NGS data analysis and clinical adoption.

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Related Experiment Videos

Last Updated: May 10, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates vast data, necessitating advanced bioinformatic tools.
  • Challenges in software configuration and reproducibility hinder NGS research and clinical application.
  • The expansion of NGS into clinical settings requires robust and accessible data analysis solutions.

Purpose of the Study:

  • To explore the potential of virtual machines (VMs) for sharing pre-configured next-generation sequencing (NGS) software pipelines.
  • To address the challenges of NGS data analysis reproducibility and clinical adoption.
  • To evaluate the performance and benefits of using VMs for NGS analysis.

Main Methods:

  • Discussed the concept of virtual machines (VMs) for encapsulating NGS data and analysis software.
  • Presented benchmarking results to assess VM performance compared to native operating systems.
  • Reviewed existing examples and potential applications of VMs in NGS analysis.

Main Results:

  • Virtual machines (VMs) can effectively package NGS data with pre-configured analysis software.
  • Benchmarking demonstrates that performance degradation in VMs can be minimized.
  • VMs offer a viable solution for simplifying NGS data analysis and enhancing reproducibility.

Conclusions:

  • Virtual machines (VMs) present a promising approach for standardizing and sharing NGS bioinformatic pipelines.
  • VMs can overcome performance limitations, making them practical for complex genomic data analysis.
  • The adoption of VMs can accelerate the integration of NGS technology into clinical diagnostics and research.