Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons
Ponsero, Alise J
Hartman, John H
Hurwitz, Bonnie L
AffiliationUniv Arizona, Dept Comp Sci
Univ Arizona, Dept Biosyst Engn
Univ Arizona, BIO5 Inst
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationIllyoung Choi, Alise J Ponsero, Matthew Bomhoff, Ken Youens-Clark, John H Hartman, Bonnie L Hurwitz, Libra: scalable k-mer–based tool for massive all-vs-all metagenome comparisons, GigaScience, Volume 8, Issue 2, February 2019, giy165, https://doi.org/10.1093/gigascience/giy165
Rights© The Author(s) 2018. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractBackground Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. Results We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. Conclusions A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.
NoteOpen access journal
VersionFinal published version
SponsorsNational Science Foundation