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

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

225
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
225
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

491
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
491
Statistical Significance01:50

Statistical Significance

22.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.2K
Cell Lines01:16

Cell Lines

10.4K
A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...
10.4K
Histone Variants at the Centromere02:30

Histone Variants at the Centromere

5.1K
Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3...
5.1K
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K

You might also read

Related Articles

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

Sort by
Same author

Whole Genome Sequencing Reveals a <i>RET</i> Enhancer Risk Haplotype Associated with Hirschsprung Disease in Mowat Wilson Syndrome.

medRxiv : the preprint server for health sciences·2026
Same author

De Novo Variation in Autism by Sex and Diagnostic Status in 41,367 Parent-Child Trios.

medRxiv : the preprint server for health sciences·2026
Same author

Time trends in the male to female ratio for autism incidence: population based, prospectively collected, birth cohort study.

BMJ (Clinical research ed.)·2026
Same author

Investigating the neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies.

Nature communications·2025
Same author

Generation and characterization of a knockout mouse of an enhancer of EBF3.

Biology open·2025
Same author

Haplotypic resolution of the challenging genomic regions of MHC and KIR using a combination of targeted sequencing and a novel assembly pipeline.

Nucleic acids research·2025

Related Experiment Video

Updated: Feb 10, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

996

Multi-omics Differential Inference for Functional Interpretation (MoDIFI): A Statistical Framework to Prioritize Cell

Arvinden Vr1, Grace Tzun-Wen Shaw2, Juana Manuel1

  • 1Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.

Biorxiv : the Preprint Server for Biology
|February 9, 2026
PubMed
Summary

Choosing the right cell line is crucial for studying noncoding variants in neurodevelopmental disorders (NDDs). Our new method, MoDIFI, helps identify cell-specific regulatory effects for accurate functional testing.

Keywords:
ATACBayes FactorBayesianCell lineHi-CRNAneurodevelopmental disordernoncodingstatistical framework

More Related Videos

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants
06:35

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants

Published on: October 10, 2022

2.4K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Related Experiment Videos

Last Updated: Feb 10, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

996
A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants
06:35

A Scalable, Cell-Based Method for the Functional Assessment of Ube3a Variants

Published on: October 10, 2022

2.4K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Area of Science:

  • Genomics
  • Neuroscience
  • Computational Biology

Background:

  • Noncoding variants are implicated in neurodevelopmental disorders (NDDs).
  • Regulatory effects of these variants are often cell-type specific.
  • Selecting appropriate in vitro models for high-throughput assays is challenging.

Purpose of the Study:

  • To determine which cell lines and regulatory regions best reveal allele-specific effects of noncoding variants.
  • To develop a computational framework for integrating multi-omics data to predict cell-specific regulatory activity.

Main Methods:

  • Generated matched RNA-seq, ATAC-seq, and Hi-C profiles across human and mouse neuronal cell lines, plus a non-neuronal line.
  • Developed MoDIFI (Multi-omics Differential Inference for Functional Interpretation), a Bayesian framework.
  • Quantified cell-line-specific regulatory activity using posterior inclusion probabilities for differential gene-loop interactions.

Main Results:

  • MoDIFI successfully integrated orthogonal multi-omics data to identify cell-line-resolved regulatory maps.
  • Identified regulatory regions supported by coordinated 3D contacts, chromatin accessibility, and transcriptional output.
  • Highlighted both shared synaptic programs and context-dependent regulatory mechanisms across different cell types.

Conclusions:

  • MoDIFI provides a practical strategy for prioritizing informative cell lines and regulatory elements for NDD variant functional testing.
  • This approach facilitates the targeted investigation of noncoding variation in neurodevelopmental disorders.
  • Enables more efficient and accurate functional validation of genetic variants linked to NDDs.