WGSUniFrac: Applying UniFrac Metric to Whole Genome Shotgun Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The UniFrac metric has proven useful in revealing diversity across metagenomic communities. Due to the phylogeny-based nature of this measurement, UniFrac has historically only been applied to 16S rRNA data. Simultaneously, Whole Genome Shotgun (WGS) metagenomics has been increasingly widely employed and proven to provide more information than 16S data, but a UniFrac-like diversity metric suitable for WGS data has not previously been developed. The main obstacle for UniFrac to be applied directly to WGS data is the absence of phylogenetic distances in the taxonomic relationship derived from WGS data. In this study, we demonstrate a method to overcome this intrinsic difference and compute the UniFrac metric on WGS data by assigning branch lengths to the taxonomic tree obtained from input taxonomic profiles. We conduct a series of experiments to demonstrate that this WGSUniFrac method is comparably robust to traditional 16S UniFrac and is not highly sensitive to branch lengths assignments, be they data-derived or model-prescribed.

Original languageEnglish (US)
Title of host publication22nd International Workshop on Algorithms in Bioinformatics, WABI 2022
EditorsChristina Boucher, Sven Rahmann
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772433
DOIs
StatePublished - Sep 1 2022
Event22nd International Workshop on Algorithms in Bioinformatics, WABI 2022 - Potsdam, Germany
Duration: Sep 5 2022Sep 7 2022

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume242
ISSN (Print)1868-8969

Conference

Conference22nd International Workshop on Algorithms in Bioinformatics, WABI 2022
Country/TerritoryGermany
CityPotsdam
Period9/5/229/7/22

All Science Journal Classification (ASJC) codes

  • Software

Fingerprint

Dive into the research topics of 'WGSUniFrac: Applying UniFrac Metric to Whole Genome Shotgun Data'. Together they form a unique fingerprint.

Cite this