TY - GEN
T1 - WGSUniFrac
T2 - 22nd International Workshop on Algorithms in Bioinformatics, WABI 2022
AU - Wei, Wei
AU - Koslicki, David
N1 - Funding Information:
Funding Wei Wei: NSF Grant No. 2029170 David Koslicki: NSF Grant No. 2029170
Publisher Copyright:
© Wei Wei and David Koslicki.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85137800422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137800422&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.WABI.2022.15
DO - 10.4230/LIPIcs.WABI.2022.15
M3 - Conference contribution
AN - SCOPUS:85137800422
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 22nd International Workshop on Algorithms in Bioinformatics, WABI 2022
A2 - Boucher, Christina
A2 - Rahmann, Sven
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Y2 - 5 September 2022 through 7 September 2022
ER -