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

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

You might also read

Related Articles

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

Sort by

Same author

Water hydration of polyethylene glycol dimethyl ether.

The Journal of chemical physics·2023

Same author

[Advance of research on endoplasmic reticulum stress and genetic epilepsy].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2023

Same author

Sacubitril/valsartan: research progress of multi-channel therapy for cardiorenal syndrome.

Frontiers in pharmacology·2023

Same author

Kinetically Controlled Star Copolymer Self-Assembly for Rapid Fabrication of Nanoparticles with High Encapsulation Capacity.

Chemistry (Weinheim an der Bergstrasse, Germany)·2023

Same author

Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG.

Renal failure·2023

Same author

Overlapping Symptoms of Functional Gastrointestinal Disorders: Current Challenges and the Role of Traditional Chinese Medicine.

The American journal of Chinese medicine·2023

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 13, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.

Jiaqi Zhang¹, Manav Chakravarthy¹, Ritambhara Singh^1,2

¹Department of Computer Science, Brown University, 115 Waterman St., Providence, 02906, RI, United States.

Biorxiv : the Preprint Server for Biology

|November 18, 2024

Summary

This summary is machine-generated.

scMultiNODE integrates gene expression and chromatin accessibility data from developing single cells. This novel method enhances understanding of cellular dynamics and developmental trajectories using multi-modal single-cell analysis.

Keywords:

autoencoders multi-modal data integration neural ordinary differential equations optimal transport single-cell development temporal single-cell analysis

More Related Videos

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Related Experiment Videos

Last Updated: May 13, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Understanding cell development requires analyzing single-cell genomic profiles over time.
Integrative analysis of multiple measurements (modalities) across developmental stages offers comprehensive insights.
Acquiring same-cell multi-modal data is resource-intensive, limiting integrative studies.

Purpose of the Study:

To propose scMultiNODE, an unsupervised integration model for multi-modal single-cell data.
To integrate gene expression and chromatin accessibility measurements in developing single cells.
To preserve cell type variations and capture cellular dynamics during integration.

Main Methods:

scMultiNODE employs autoencoders for nonlinear low-dimensional cell representation.
Optimal transport is used to align cells across different measurements.
Neural ordinary differential equations model cell development with a dynamic latent space.

Main Results:

scMultiNODE effectively integrates temporally profiled multi-modal single-cell data.
The model outperforms existing methods by preserving cellular dynamics.
The joint latent space generated by scMultiNODE aids downstream single-cell development analysis.

Conclusions:

scMultiNODE provides a robust framework for integrating multi-modal single-cell data.
The method enhances the study of cellular dynamics and developmental trajectories.
scMultiNODE facilitates deeper insights into complex biological processes from single-cell measurements.