Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sequences01:29

Sequences

371
Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
371
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

514
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
514
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

476
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
476
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

15.7K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
15.7K
Next-generation Sequencing03:00

Next-generation Sequencing

100.1K
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....
100.1K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

780
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
780

You might also read

Related Articles

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

Sort by
Same author

SERIPH: A Two-Step Extraction Protocol for Selective Enrichment of Semi-Extractable RNAs.

RNA (New York, N.Y.)·2026
Same author

LinearCapR: linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits.

Bioinformatics (Oxford, England)·2026
Same author

Age-related decline in nuclear envelope LINC complex drives neuronal aging via axon initial segment dysfunction.

EMBO reports·2026
Same author

A poorly reactogenic lipid nanoparticle-mRNA vaccine unveils an innate immune pathway for adverse reactions.

NPJ vaccines·2026
Same author

Simple and Thorough Detection of Related Sequences with Position-Varying Probabilities of Substitutions, Insertions, and Deletions.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.

Briefings in bioinformatics·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Training alignment parameters for arbitrary sequencers with LAST-TRAIN.

Michiaki Hamada1,2,3, Yukiteru Ono4, Kiyoshi Asai3,5

  • 1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.

Bioinformatics (Oxford, England)
|January 1, 2017
PubMed
Summary
This summary is machine-generated.

LAST-TRAIN enhances DNA sequence alignment accuracy by optimizing scoring parameters for specific datasets. This method reduces reference bias in mapping reads from various sequencing technologies, including Oxford Nanopore.

More Related Videos

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

9.2K
An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents
09:39

An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents

Published on: January 19, 2022

4.8K

Related Experiment Videos

Last Updated: Mar 9, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

9.2K
An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents
09:39

An Open-Source, Fully Customizable 5-Choice Serial Reaction Time Task Toolbox for Automated Behavioral Training of Rodents

Published on: January 19, 2022

4.8K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate DNA sequence alignment is crucial for genomic analysis.
  • Existing alignment tools may exhibit reference bias, particularly with diverse sequencing technologies.
  • Optimizing substitution and gap scores is essential for improving alignment fidelity.

Purpose of the Study:

  • To introduce LAST-TRAIN, a novel method for improving sequence alignment accuracy.
  • To develop a strategy for inferring dataset-specific substitution and gap scores.
  • To evaluate the performance of LAST-TRAIN in reducing reference bias for next-generation sequencing data.

Main Methods:

  • LAST-TRAIN infers substitution and gap scores based on substitution, insertion, and deletion frequencies within a given dataset.
  • The method was applied to DNA reads generated by IonTorrent and PacBio RS sequencing platforms.
  • Performance was assessed by evaluating reference bias in mapping Oxford Nanopore reads.

Main Results:

  • LAST-TRAIN successfully infers optimal scoring parameters tailored to specific datasets.
  • Application to IonTorrent and PacBio RS data demonstrated improved mapping capabilities.
  • A significant reduction in reference bias was observed for Oxford Nanopore reads when using LAST-TRAIN.

Conclusions:

  • LAST-TRAIN offers a robust approach to enhance DNA sequence alignment accuracy.
  • The method effectively mitigates reference bias, leading to more reliable genomic analyses.
  • LAST-TRAIN provides a valuable tool for researchers working with diverse sequencing data.