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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2022.15
URN: urn:nbn:de:0030-drops-170494
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/17049/
Wei, Wei ;
Koslicki, David
WGSUniFrac: Applying UniFrac Metric to Whole Genome Shotgun Data
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.
BibTeX - Entry
@InProceedings{wei_et_al:LIPIcs.WABI.2022.15,
author = {Wei, Wei and Koslicki, David},
title = {{WGSUniFrac: Applying UniFrac Metric to Whole Genome Shotgun Data}},
booktitle = {22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
pages = {15:1--15:22},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-243-3},
ISSN = {1868-8969},
year = {2022},
volume = {242},
editor = {Boucher, Christina and Rahmann, Sven},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/17049},
URN = {urn:nbn:de:0030-drops-170494},
doi = {10.4230/LIPIcs.WABI.2022.15},
annote = {Keywords: UniFrac, beta-diversity, Whole Genome Shotgun, microbial community similarity}
}
