Related Experiment Video
Updated: Feb 18, 2026
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
12.3K
Challenges in the Setup of Large-scale Next-Generation Sequencing Analysis Workflows.
Pranav Kulkarni1, Peter Frommolt1
1Bioinformatics Core Facility, CECAD Research Center, University of Cologne, Germany.
Computational and Structural Biotechnology Journal
|November 22, 2017
Summary
Setting up Next-Generation Sequencing (NGS) analysis workflows requires bioinformatics expertise and careful consideration of software, hardware, and data management. This review covers key aspects for scientific applications, highlighting current challenges and solutions.
Area of Science:
- Life Sciences
- Bioinformatics
- Genomics
Background:
- Next-Generation Sequencing (NGS) is a powerful research tool but demands significant bioinformatics expertise for data analysis.
- Challenges include tool selection, high-performance computing (HPC) parallelization, automation, data storage, and result exploitation.
- NGS is expanding into clinical diagnostics, introducing new technological, legal, and ethical considerations.
Purpose of the Study:
- To summarize key considerations for establishing NGS data analysis architectures, primarily for scientific research.
- To provide an overview of the current state-of-the-art and challenges in NGS data analysis.
Main Methods:
- Review of current literature and industry practices in NGS data analysis.
- Analysis of various IT architectures, including in-house solutions, on-site technologies, and Everything as a Service (XaaS) models.
Main Results:
- Numerous approaches exist for NGS data analysis, varying by institution.
- Key factors for analysis architecture setup include software, HPC, automation, storage, and result interpretation.
- The field faces ongoing technological, legal, and ethical challenges, especially with clinical integration.
Conclusions:
- Establishing robust NGS analysis architectures is crucial for scientific discovery and requires addressing complex bioinformatics and IT challenges.
- Careful planning of analysis workflows, infrastructure, and data management is essential for maximizing the value of NGS data.
- The mini-review provides a framework for researchers and institutions navigating the complexities of NGS data analysis.
Related Concept Videos
03:00Next-generation Sequencing
99.2K
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....
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
99.2K
03:36Genome Annotation and Assembly
21.1K
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.
21.1K