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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...
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Mismatch Repair01:36

Mismatch Repair

Overview
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...

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

A survey of error-correction methods for next-generation sequencing.

Xiao Yang1, Sriram P Chockalingam, Srinivas Aluru

  • 1The Broad institute, 7 Cambridge Center, Cambridge, MA 02142, USA. xiaoyang@broadinstitute.org

Briefings in Bioinformatics
|April 12, 2012
PubMed
Summary
This summary is machine-generated.

Accurate sequencing reads are crucial for next-generation sequencing (NGS) results. This review evaluates various error correction methods for NGS data, establishing benchmarks for standardized comparison and future research directions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) technologies generate vast amounts of data, necessitating accurate sequencing reads for reliable downstream applications.
  • Numerous error correction methods have been developed for NGS data, but a lack of standardized evaluation hinders comparative assessment.
  • The rapid advancement of sequencing technologies outpaces the development of standardized evaluation procedures for error correction methods.

Purpose of the Study:

  • To provide a comprehensive review of existing error correction methods for next-generation sequencing data.
  • To establish a common set of benchmark data and evaluation criteria for comparative assessment of error correction techniques.
  • To identify the current state-of-the-art and promising future research directions in NGS data error correction.

Main Methods:

  • A comprehensive review of various error correction methods for next-generation sequencing data.
  • Establishment of a common set of benchmark data and evaluation criteria.
  • Experimental assessment of selected error correction methods based on quality, run-time, memory usage, and scalability.

Main Results:

  • Experimental results detailing the performance of several error correction methods regarding quality, run-time, memory usage, and scalability.
  • Comparative assessment of different error correction techniques using standardized benchmarks and criteria.
  • Identification of the strengths and weaknesses of various error correction approaches.

Conclusions:

  • The study provides explicit recommendations for practitioners in selecting appropriate error correction methods for next-generation sequencing data.
  • The review highlights the current state-of-the-art in NGS error correction, identifying key challenges and limitations.
  • The established benchmarks and criteria facilitate future research and development of more effective error correction strategies.