Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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....

RNA-seq

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...

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Peculiar pigment pattern and population profile of a poisonous pufferfish.

PeerJ·2026

Same author

A reproducible compartment-aware k-mer set-algebra and MAW workflow for plant genomes maps contrasting nuclear-organelle sharing landscapes in Arabidopsis and rice.

Plant methods·2026

Same author

Minimum virtual dataset for reproducible triploid de novo genome assembly.

Scientific data·2026

Same author

Correction: Novel compound heterozygous ABCA2 variants cause IDPOGSA, a variable phenotypic syndrome with intellectual disability.

Journal of human genetics·2025

Same author

Genome sequencing provides high diagnostic yield and new etiological insights for intellectual disability and developmental delay.

NPJ genomic medicine·2025

Same author

Hemizygous SMARCA1 variants cause X-linked intellectual disability.

Journal of human genetics·2025

Search research articles

Related Experiment Video

Updated: Apr 3, 2026

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

PAFFT: A new homology search algorithm for third-generation sequencers.

Kazuharu Misawa¹, Ryo Ootsuki²

¹Advanced Center for Computing and Communication, RIKEN, Hirosawa 2-1, Wako, Saitama 351-0198, Japan.

|September 22, 2015

Summary

This summary is machine-generated.

Third-generation sequencers generate long DNA reads but are error-prone. A new homology search algorithm, PAFFT, extends MAFFT to detect global homology, improving gene identification from noisy sequencing data.

Keywords:

Homology search Third-generation sequencer

More Related Videos

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

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

Related Experiment Videos

Last Updated: Apr 3, 2026

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

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

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:

Third-generation DNA sequencers produce long reads but suffer from high error rates.
Sequencing errors complicate the identification of homologous genes.
Existing homology search algorithms struggle with error-prone, long-read data.

Purpose of the Study:

To develop a novel homology search algorithm to address challenges posed by third-generation sequencing data.
To improve the accuracy of gene identification from high-error-rate sequencing reads.

Main Methods:

Developed PAFFT, a novel homology search algorithm.
PAFFT is an extension of the established MAFFT multiple sequence alignment algorithm.
PAFFT is designed to detect global homology, unlike local homology detectors.

Main Results:

PAFFT successfully detects homologous regions even with high sequencing error rates.
The algorithm enhances the ability to identify homologous genes from third-generation sequencing data.
PAFFT demonstrates improved performance in homology searches with noisy, long reads.

Conclusions:

PAFFT is an effective tool for homology searching with error-prone, long-read sequencing data.
This new algorithm will facilitate broader applications of third-generation sequencers.
PAFFT overcomes limitations of existing methods in handling high sequencing error rates.