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

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

You might also read

Related Articles

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

Sort by
Same author

Author Correction: CITED2 is a druggable epigenetic switch coupling neuronal maturation to regenerative decline.

EMBO molecular medicine·2026
Same author

SCW: building the whole-genome 3D structures based on extremely sparse single-cell Hi-C data.

BMC bioinformatics·2026
Same author

CITED2 is a druggable epigenetic switch coupling neuronal maturation to regenerative decline.

EMBO molecular medicine·2026
Same author

scHiGex: predicting single-cell gene expression based on single-cell Hi-C data.

NAR genomics and bioinformatics·2025
Same author

PANDA-3D: protein function prediction based on AlphaFold models.

NAR genomics and bioinformatics·2024
Same author

Learning Micro-C from Hi-C with diffusion models.

PLoS computational biology·2024

Related Experiment Video

Updated: May 8, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.3K

HiC4D-SPOT: a spatiotemporal outlier detection tool for Hi-C data.

Bishal Shrestha1, Zheng Wang1

  • 1Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33146, United States.

Briefings in Bioinformatics
|July 16, 2025
PubMed
Summary

HiC4D-SPOT is a new deep learning tool that analyzes 3D chromatin interactions in Hi-C data. It accurately detects anomalies like temporal inconsistencies and structural changes in chromatin organization.

Keywords:
ConvLSTMHi-Canomaly detectionspatiotemporal autoencoderspatiotemporal dynamics

More Related Videos

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.8K
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

747

Related Experiment Videos

Last Updated: May 8, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

17.3K
Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.8K
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

747

Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • The 3D chromatin organization is crucial for cellular functions like gene regulation and genome stability.
  • Detecting anomalies in spatiotemporal Hi-C data is challenging due to complex chromatin dynamics.

Purpose of the Study:

  • To develop an unsupervised deep learning framework, HiC4D-SPOT, for identifying structural anomalies in spatiotemporal Hi-C data.
  • To model chromatin dynamics and detect deviations from normal organization.

Main Methods:

  • Utilized a ConvLSTM-based autoencoder for unsupervised learning of chromatin dynamics.
  • Benchmarked HiC4D-SPOT using metrics like Pearson and Spearman Correlation Coefficients.
  • Validated the framework on simulated and experimental data, including time-swap experiments and differentiation studies.

Main Results:

  • Achieved high reconstruction fidelity with correlation coefficients of 0.9.
  • Successfully detected temporal inconsistencies, topologically associating domain (TAD) and loop perturbations.
  • Identified biologically relevant events such as HERV-H boundary weakening and cohesin-mediated loop dynamics.

Conclusions:

  • HiC4D-SPOT is an effective tool for analyzing 3D chromatin dynamics from spatiotemporal Hi-C data.
  • The framework enables the detection of significant structural anomalies and chromatin remodeling events.