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

Integration of Synaptic Events01:28

Integration of Synaptic Events

1.5K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
1.5K
Graded Potential01:19

Graded Potential

3.8K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
3.8K

You might also read

Related Articles

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

Sort by
Same author

Movi 2: Fast and Space-Efficient Queries on Pangenomes.

Bioinformatics (Oxford, England)·2026
Same author

Faster run-length compressed suffix arrays.

Oasics : openaccess series in informatics·2026
Same author

Response to: "best practices when benchmarking CATCH for the design of genome enrichment probes".

Bioinformatics (Oxford, England)·2026
Same author

RLBWT-Based LCP Computation in Compressed Space for Terabase-Scale Pangenome Analysis.

bioRxiv : the preprint server for biology·2026
Same author

Run-length compressed metagenomic read classification with SMEM-finding and tagging.

iScience·2026
Same author

KeBaB: <i>k</i>-mer based breaking for finding long MEMs.

International Symposium on String Processing and Information Retrieval : SPIRE ... : proceedings. SPIRE (Symposium)·2025
Same journal

Faster Maximal Exact Matches with Lazy LCP Evaluation.

Proceedings. Data Compression Conference·2024
Same journal

Recursive Prefix-Free Parsing for Building Big BWTs.

Proceedings. Data Compression Conference·2024
Same journal

Computing matching statistics on Wheeler DFAs.

Proceedings. Data Compression Conference·2024
Same journal

CSTs for Terabyte-Sized Data.

Proceedings. Data Compression Conference·2024
Same journal

PHONI: Streamed Matching Statistics with Multi-Genome References.

Proceedings. Data Compression Conference·2021
Same journal

Denoising of Quality Scores for Boosted Inference and Reduced Storage.

Proceedings. Data Compression Conference·2017
See all related articles

Related Experiment Video

Updated: Jun 25, 2025

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
00:06

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.1K

Augmented Thresholds for MONI.

César Martínez-Guardiola1, Nathaniel K Brown2, Fernando Silva-Coira1

  • 1Universidade da Coruña, CITIC, A Coruña, Spain.

Proceedings. Data Compression Conference
|May 31, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances the MONI algorithm for faster pangenomic dataset pattern matching. A modification reduces time-consuming longest common extension queries, speeding up maximal exact match searches.

More Related Videos

In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
11:38

In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice

Published on: July 22, 2014

13.4K
Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

21.2K

Related Experiment Videos

Last Updated: Jun 25, 2025

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
00:06

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.1K
In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice
11:38

In vivo Imaging of Optic Nerve Fiber Integrity by Contrast-Enhanced MRI in Mice

Published on: July 22, 2014

13.4K
Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles
09:07

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

Published on: September 25, 2015

21.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Structures

Background:

  • Pangenomic datasets require efficient storage and querying.
  • The MONI algorithm (Rossi et al., 2022) offers space-efficient storage for pangenomic data.
  • The one-pass version of MONI (Boucher et al., 2021) faces performance bottlenecks in query times, particularly due to longest common extension (LCE) queries.

Purpose of the Study:

  • To optimize the performance of the one-pass MONI algorithm.
  • To reduce the computational overhead associated with LCE queries in MONI.
  • To improve the practical speed of finding maximal exact matches (MEMs) in large pangenomic datasets.

Main Methods:

  • Implemented a minor modification to the MONI algorithm.
  • Focused on reducing the frequency of longest common extension (LCE) queries during pattern matching.
  • Evaluated the impact of the modification on query times and data structure size.

Main Results:

  • Significantly reduced the number of LCE queries executed.
  • Achieved substantial practical speedups for MONI's pattern matching capabilities.
  • The modification resulted in only a marginal increase in the algorithm's memory footprint.

Conclusions:

  • The modified MONI algorithm offers a more efficient approach to querying pangenomic datasets.
  • Reducing LCE query reliance is a key strategy for accelerating pattern matching in bioinformatics.
  • This optimization makes MONI a more practical tool for analyzing large-scale genomic data.