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Next-generation Sequencing

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....

Genome Annotation and Assembly

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.

Evolutionary Relationships through Genome Comparisons

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...

Sanger Sequencing

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...

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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

Analysis of next-generation sequencing data using Galaxy.

Daniel Blankenberg¹, Jennifer Hillman-Jackson

¹Department of Biochemistry and Molecular Biology, Penn State University, 505 Wartik Laboratory, University Park, PA, 16802, USA, dan@bx.psu.edu.

Methods in Molecular Biology (Clifton, N.J.)

|April 19, 2014

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) generates vast data. This guide explains using the Galaxy platform for practical bioinformatics analysis and data interpretation, including server setup and tool installation.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) generates massive datasets.
Analyzing and interpreting NGS data presents significant computational challenges.
Efficient bioinformatics workflows are crucial for extracting biological insights from sequencing data.

Purpose of the Study:

To provide practical informatics methods and strategies for NGS data analysis.
To demonstrate the use of the web-based Galaxy platform for transforming NGS data into usable information.
To guide users in setting up and utilizing their own Galaxy instances.

Main Methods:

Utilizing the Galaxy web-based platform for bioinformatics analyses.
Exploring diverse NGS data analysis workflows within Galaxy.
Providing instructions for local and cloud-based Galaxy server deployment.
Demonstrating the installation of additional tools into a Galaxy environment.

Main Results:

The Galaxy platform offers a user-friendly interface for complex NGS data analysis.
Example analyses showcase the transformation of raw NGS data into interpretable results.
Successful deployment of Galaxy servers on local hardware and cloud resources is achievable.
Customization of Galaxy instances through tool installation is feasible.

Conclusions:

The Galaxy platform empowers researchers to effectively analyze and interpret large-scale NGS data.
Practical implementation of Galaxy facilitates accessible and reproducible bioinformatics research.
This approach addresses the data analysis bottleneck posed by high-throughput sequencing technologies.