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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...
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.
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...
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...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: May 20, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

From sequencer to supercomputer: an automatic pipeline for managing and processing next generation sequencing data.

Terry Camerlengo1, Hatice Gulcin Ozer, Raghuram Onti-Srinivasan

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA;

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

Next Generation Sequencing (NGS) analysis is resource-intensive. This study presents a scalable architecture to automate and manage NGS data processing, reducing computational and labor demands for core facilities.

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

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Last Updated: May 20, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

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:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next Generation Sequencing (NGS) generates large datasets requiring significant computational resources.
  • Data processing, management, and analysis of NGS experiments demand high-end computing infrastructure and manual effort.
  • Existing NGS workflows present challenges in scalability and resource efficiency.

Purpose of the Study:

  • To design and implement a scalable architecture for efficient NGS secondary analysis.
  • To address the resource-intensive nature of NGS data processing and management.
  • To demonstrate automated NGS experiment management in a core facility setting.

Main Methods:

  • Developed a scalable architecture centered around Illumina Genome Analyzer II sequencers and the Gerald data processing pipeline.
  • Integrated a Laboratory Information Management System (LIMS) named QUEST.
  • Implemented an Automation Server and a computer cluster for NGS pipeline processing.
  • Utilized a network-attached storage solution expandable up to 40TB.

Main Results:

  • The implemented architecture effectively manages and automates NGS experiments.
  • The system is designed to scale to multiple sequencers without increasing computing or labor resources.
  • Demonstrated a solution for resource-intensive NGS secondary analysis.

Conclusions:

  • The developed platform provides a robust solution for managing and automating NGS experiments in institutional settings.
  • The architecture addresses the challenges of resource intensity in NGS data analysis.
  • This approach enhances efficiency and scalability for core bioinformatics facilities.