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

SN2 Reaction: Stereochemistry02:23

SN2 Reaction: Stereochemistry

In an SN2 reaction, the nucleophilic attack on the substrate and departure of the leaving group occurs simultaneously through a transition state. As the nucleophile approaches the substrate from the back-side, the configuration of the substrate carbon changes from tetrahedral to trigonal bipyramidal and then back to tetrahedral, leading to an inversion in the configuration of the product.
If the substrate is an achiral molecule at the α-carbon, the inversion of configuration is not observed.
SN1 Reaction: Stereochemistry02:15

SN1 Reaction: Stereochemistry

This lesson provides an in-depth discussion of the stereochemical outcomes in an SN1 reaction.
In the first step of an SN1 reaction, the bond between the electrophilic carbon and the leaving group ionizes to generate the carbocation intermediate. The second step of the mechanism is the nucleophilic attack.
In the formed carbocation, the positively charged carbon is sp2 hybridized with a trigonal planar geometry. As all the three substituents lie on the same plane, a plane of symmetry for the...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Binomial Expansion Using Pascal's Triangle01:30

Binomial Expansion Using Pascal's Triangle

Expanding a binomial expression such as (a + b)n results in a predictable sequence of terms that can be systematically derived using Pascal’s Triangle. This triangular array of numbers plays a central role in understanding and computing the coefficients of binomial expansions.Pascal’s Triangle is constructed such that each row corresponds to the coefficients of a binomial raised to a power. The topmost row, known as the zeroth row, corresponds to (a + b)0, and each successive row gives the...
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the others.
Reversible and Irreversible Processes01:14

Reversible and Irreversible Processes

The thermodynamic processes can be classified into reversible and irreversible processes. The processes that can be restored to their initial state are called reversible processes. It is only possible if the process is in quasi-static equilibrium, i.e., it takes place in infinitesimally small steps, and the system remains at equilibrium However, these are ideal processes and do not occur naturally. An ideal system undergoing a reversible process is always in thermodynamic equilibrium within...

You might also read

Related Articles

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

Sort by
Same author

Quantifying Hierarchical Conflicts in Homology Statements.

Journal of molecular evolution·2025
Same author

A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.

iScience·2025
Same author

A Joint LLM-KG System for Disease Q&A.

IEEE journal of biomedical and health informatics·2025
Same author

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

IEEE journal of biomedical and health informatics·2024
Same author

ASTER: A Method to Predict Clinically Relevant Synthetic Lethal Genetic Interactions.

IEEE journal of biomedical and health informatics·2024
Same author

Avoiding inferior clusterings with misspecified Gaussian mixture models.

Scientific reports·2023
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

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

Investigations on Multiple Protein Scaffold Filling.

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

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

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

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

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

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

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

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Sorting signed permutations by inversions in O(nlogn) time.

Krister M Swenson1, Vaibhav Rajan, Yu Lin

  • 1Laboratory for Computational Biology and Bioinformatics, EPFL (Ecole Polytechnique Fédérale de Lausanne), Lausanne, Switzerland. akswenson@uottawa.ca

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

Researchers developed new algorithms for sorting signed permutations by genomic inversions. The study shows that sorting by inversions can likely be achieved in O(n log n) time, improving computational genomics efficiency.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Related Experiment Videos

Last Updated: Jun 14, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

  • Computational Genomics
  • Bioinformatics Algorithms

Background:

  • Genomic inversions are fundamental in computational genomics.
  • Previous algorithms for sorting by inversions have achieved polynomial and linear time complexities.
  • The efficiency of sorting signed permutations by inversions in O(n log n) time remained an open question.

Purpose of the Study:

  • To investigate the feasibility of sorting signed permutations by inversions in O(n log n) time.
  • To introduce novel algorithms for addressing this computational challenge.

Main Methods:

  • Development of a simple, fast randomized algorithm.
  • Design of a deterministic refinement algorithm.
  • Extensive experimental evaluation of algorithm performance.

Main Results:

  • The deterministic algorithm runs in O(n log n + kn) time, where k is a small, data-dependent parameter.
  • Experimental results indicate that k's average and standard deviation are small constants.
  • These constants appear independent of permutation size.

Conclusions:

  • The study provides a qualified answer to the O(n log n) sorting by inversions question.
  • Evidence suggests that almost all signed permutations can be sorted in O(n log n) time.
  • Further theoretical proof is needed to confirm this finding for all cases.