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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...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since 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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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

ART: a next-generation sequencing read simulator.

Weichun Huang1, Leping Li, Jason R Myers

  • 1Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. weichun.huang@nih.gov

Bioinformatics (Oxford, England)
|December 27, 2011
PubMed
Summary
This summary is machine-generated.

ART is a simulation tool that generates synthetic next-generation sequencing reads. It aids in testing and benchmarking analysis tools for sequencing data, supporting major platforms.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) data analysis requires robust tools for tasks like alignment and variant discovery.
  • Benchmarking and testing these tools necessitate accurate synthetic sequencing data.

Purpose of the Study:

  • To introduce ART, a software package for simulating next-generation sequencing reads.
  • To provide a tool that emulates real sequencing processes for reliable data simulation.

Main Methods:

  • ART simulates sequencing reads by modeling the empirical error profiles and quality values from large datasets.
  • It supports major NGS platforms including Roche 454, Illumina Solexa, and Applied Biosystems SOLiD.
  • Customizable error models and quality profiles offer flexibility for advanced users.

Main Results:

  • ART generates synthetic NGS reads that accurately reflect real-world sequencing data characteristics.
  • The tool facilitates the development and validation of bioinformatics pipelines for NGS data analysis.

Conclusions:

  • ART is a valuable resource for the bioinformatics community, enabling rigorous testing and benchmarking of NGS data analysis tools.
  • Its ability to emulate diverse sequencing technologies makes it adaptable for various research needs.