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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

452
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...
452
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

12.5K
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...
12.5K
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

14.2K
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...
14.2K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

4.2K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
4.2K
Geometric Sequences01:30

Geometric Sequences

221
In systems where values diminish by a constant proportion at each stage, the resulting sequence follows a geometric structure. Each new value in the sequence is obtained by applying a fixed multiplier to the preceding term. This regular, proportional decline type is often used to represent processes involving gradual loss, such as energy dissipation or reduction in amplitude over time.When analyzing the total effect of such a process across unlimited iterations, the series of values is referred...
221
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.7K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Algebraic and Diagrammatic Methods for the Rule-Based Modeling of Multiparticle Complexes.

PRX life·2026
Same author

Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.

Genetics·2026
Same author

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.

Journal of mathematical biology·2026
Same author

PoolParty: streamlined design of DNA sequence libraries in Python.

bioRxiv : the preprint server for biology·2026
Same author

Genetic background shapes AI-predicted variant effects.

bioRxiv : the preprint server for biology·2026
Same author

Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.

bioRxiv : the preprint server for biology·2026
Same journal

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

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

Related Experiment Video

Updated: Jan 2, 2026

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

11.3K

Logomaker: beautiful sequence logos in Python.

Ammar Tareen1, Justin B Kinney1

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.

Bioinformatics (Oxford, England)
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

Logomaker is a new Python tool that simplifies creating customized sequence logos for DNA, RNA, and protein data. This tool makes complex biological sequence visualization accessible for researchers.

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.4K
Visualize Drosophila Leg Motor Neuron Axons Through the Adult Cuticle
08:33

Visualize Drosophila Leg Motor Neuron Axons Through the Adult Cuticle

Published on: October 30, 2018

10.0K

Related Experiment Videos

Last Updated: Jan 2, 2026

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

11.3K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.4K
Visualize Drosophila Leg Motor Neuron Axons Through the Adult Cuticle
08:33

Visualize Drosophila Leg Motor Neuron Axons Through the Adult Cuticle

Published on: October 30, 2018

10.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Sequence logos are crucial for visualizing biological sequence data.
  • Generating and customizing sequence logos in Python has been challenging.

Purpose of the Study:

  • Introduce Logomaker, a Python API for creating high-quality sequence logos.
  • Provide a user-friendly tool for custom biological sequence visualization.

Main Methods:

  • Logomaker accepts input from numerical matrices or multiple sequence alignments.
  • Logos are generated as native matplotlib objects for easy integration and styling.

Main Results:

  • Logomaker enables the creation of both standard and highly customized sequence logos.
  • The API facilitates stylization and incorporation into multi-panel figures.

Conclusions:

  • Logomaker offers a powerful and flexible solution for sequence logo generation in Python.
  • The tool enhances the visualization capabilities for DNA, RNA, and protein sequence analysis.