Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Incorporating Scale Uncertainty into Differential Expression Analyses Using ALDEx2.

Current protocols·2026

Same author

Machine learning reveals sequence and methylation determinants of SaCas9-PAM interactions in bacteria.

Nucleic acids research·2026

Same author

Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.

NAR genomics and bioinformatics·2025

Same author

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis.

bioRxiv : the preprint server for biology·2024

Same author

A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets.

Nature communications·2023

Same author

Metabolically-targeted dCas9 expression in bacteria.

Nucleic acids research·2023

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

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

Expanding the UniFrac Toolbox.

Ruth G Wong¹, Jia R Wu¹, Gregory B Gloor¹

¹Department of Biochemistry, University of Western Ontario, London, Ontario, Canada.

|September 16, 2016

Summary

This summary is machine-generated.

Unweighted UniFrac analysis is sensitive to sequencing depth and rarefaction, potentially obscuring microbiome data structure. New UniFrac metrics (information and ratio UniFrac) offer improved robustness and outlier separation for microbiome research.

More Related Videos

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Published on: October 6, 2019

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Published on: January 16, 2014

Related Experiment Videos

Last Updated: Mar 15, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Published on: October 6, 2019

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Published on: January 16, 2014

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

The UniFrac distance metric is crucial for microbiome analysis, enabling group separation.
However, standard UniFrac requires consistent sequencing depth, limiting its application.
Unweighted UniFrac is particularly sensitive to rarefaction and sequencing depth variations.

Purpose of the Study:

To investigate the sensitivity of unweighted UniFrac to rarefaction and sequencing depth.
To introduce novel UniFrac weightings that mitigate these sensitivities.
To enhance the ability to separate groups and identify outliers in microbiome datasets.

Main Methods:

Demonstration of unweighted UniFrac's sensitivity using uniform datasets.
Analysis of subcompositional effects contributing to sensitivity.
Introduction and evaluation of information UniFrac and ratio UniFrac metrics.

Main Results:

Unweighted UniFrac shows high sensitivity to rarefaction and sequencing depth, even in datasets lacking clear structure.
Subcompositional effects are identified as a key cause of this sensitivity.
Information UniFrac and ratio UniFrac exhibit reduced sensitivity to rarefaction.
The new metrics allow for greater separation of outliers compared to classic UniFrac methods.

Conclusions:

Classic unweighted UniFrac is unreliable under varying sequencing depths due to subcompositional effects.
Information UniFrac and ratio UniFrac provide more robust alternatives for microbiome analysis.
These expanded UniFrac metrics empower researchers with more versatile data interpretation tools.