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Related Concept Videos

Mismatch Repair

Mismatch Repair

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Proofreading

Proofreading

Synthesis of new DNA molecules is carried out by the enzyme DNA polymerase, which adds nucleotides on the daughter strand complementary to the template DNA strand. DNA polymerase has a higher affinity to add the correct base and ensures fidelity during DNA replication. Furthermore, it exhibits proofreading activity during replication, using an exonuclease domain that cuts off incorrect nucleotides from the nascent DNA strand.
Errors During Replication are Corrected by the DNA Polymerase...

Proofreading

Proofreading

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.

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....

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Related Experiment Video

Updated: Apr 7, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Error filtering, pair assembly and error correction for next-generation sequencing reads.

Robert C Edgar¹, Henrik Flyvbjerg²

¹Tiburon, CA 94920, USA and.

Bioinformatics (Oxford, England)

|July 4, 2015

Summary

This summary is machine-generated.

Next-generation sequencing error correction is improved using three methods: read filtering, paired-read assembly, and amplicon error correction. These techniques significantly reduce error rates in sequencing data, enhancing accuracy.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Bioinformatics

Background:

Next-generation sequencing (NGS) generates large datasets, but low coverage can obscure sequencing errors, making them indistinguishable from true biological variation.
Accurate interpretation of NGS data is crucial for biological discovery.

Purpose of the Study:

To develop and implement robust methods for reducing sequencing error frequencies in NGS data.
To improve the reliability of genomic analyses by distinguishing true biological variation from sequencing errors.

Main Methods:

Implemented three independent error reduction strategies: read filtering based on expected error rates, assembly of overlapping read pairs, and abundance-based error correction for amplicon reads.
Developed and integrated these methods into the USEARCH software package.
Evaluated the accuracy of posterior quality scores calculated by published paired-read assemblers.

Main Results:

Demonstrated significant reductions in sequencing error frequencies, particularly for reads with high initial error rates.
Successfully applied read filtering, paired-read assembly, and amplicon error correction to improve data quality.
Identified that most existing paired-read assemblers compute inaccurate posterior quality scores.

Conclusions:

The implemented methods effectively reduce sequencing errors in NGS data, enhancing data accuracy and reliability.
The USEARCH package provides valuable tools for bioinformaticians to improve the quality of their sequencing data.
Accurate quality scoring is essential for robust downstream analyses, and current methods may require revision.