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Related Concept Videos

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....

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...

Genome Copying Errors

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.

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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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...

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Related Experiment Video

Updated: Mar 24, 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

ADEPT, a dynamic next generation sequencing data error-detection program with trimming.

Shihai Feng¹, Chien-Chi Lo², Po-E Li³

¹Genome Science Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. sfeng@lanl.gov.

BMC Bioinformatics

|March 2, 2016

Summary

This summary is machine-generated.

ADEPT is a new dynamic error detection method for Illumina sequencing data. It improves true positive error discovery rates without increasing false positives, especially in the middle of reads.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Next-Generation Sequencing

Background:

Illumina sequencing technology generates millions of short reads with inherent errors.
Sequencing errors pose significant challenges for de novo genome assembly, metagenomics, and SNP discovery.

Purpose of the Study:

To present ADEPT, a novel dynamic method for detecting sequencing errors.
To improve the accuracy of error detection in next-generation sequencing reads.

Main Methods:

ADEPT utilizes nucleotide quality scores, neighboring base information, and read positions.
It compares position-specific quality scores against the overall distribution within a sequencing run.

Main Results:

ADEPT demonstrates a higher true positive rate for error discovery compared to existing methods.
The method effectively reduces false positives, particularly for errors located in the middle of sequencing reads.

Conclusions:

ADEPT is a unique tool for dynamic error assessment in sequencing reads.
It addresses position-dependent under-prediction issues common in error prediction.
ADEPT enhances true error identification while minimizing false positives, especially within read middles.