Selection of appropriate metagenome taxonomic classifiers for ancient microbiome research release_esd42xruvbaujhijwgolgbdtxa

by Irina Marie Velsko, Laurent A. F. Frantz, Alexander Herbig, Greger Larson, Christina Warinner

Released as a post by Cold Spring Harbor Laboratory.

2018  

Abstract

Metagenomics enables the study of complex microbial communities from myriad sources, including the remains of oral and gut microbiota preserved in archaeological dental calculus and paleofeces, respectively. While accurate taxonomic assignment is essential to this process, DNA damage, characteristic to ancient samples (e.g. reduction in fragment size), may reduce the accuracy of read taxonomic assignment. Using a set of in silico-generated metagenomic datasets we investigated how the addition of ancient DNA (aDNA) damage patterns influences microbial taxonomic assignment by five widely-used profilers: QIIME/UCLUST, MetaPhlAn2, MIDAS, CLARK-S, and MALT (BLAST-X-mode). In silico-generated datasets were designed to mimic dental plaque, consisting of 40, 100, and 200 microbial species/strains, both with and without simulated aDNA damage patterns. Following taxonomic assignment, the profiles were evaluated for species presence/absence, relative abundance, alpha-diversity, beta-diversity, and specific taxonomic assignment biases. Unifrac metrics indicated that both MIDAS and MetaPhlAn2 provided the most accurate community structure reconstruction. QIIME/UCLUST, CLARK-S, and MALT had the highest number of inaccurate taxonomic assignments; however, filtering out species present at <0.1% abundance greatly increased the accuracy of CLARK-S and MALT. All programs except CLARK-S failed to detect some species from the input file that were in their databases. Ancient DNA damage resulted in minimal differences in species detection and relative abundance between simulated ancient and modern datasets for most programs. In conclusion, taxonomic profiling biases are program-specific rather than damage-dependent, and the choice of taxonomic classification program to use should be tailored to the research question.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.4 MB
file_sk6kaewhbfbepp2exqqaccvv2u
www.biorxiv.org (repository)
web.archive.org (webarchive)
application/pdf  2.4 MB
file_fjknvhltendhzotnz2ahpjw3hi
www.biorxiv.org (repository)
web.archive.org (webarchive)
application/pdf  2.4 MB
file_msyaruwxuvgidlt5mhkombtwl4
www.biorxiv.org (web)
web.archive.org (webarchive)
application/pdf  2.3 MB
file_fhx3joncyjcbppjdnahxqgzyxi
pubman.mpdl.mpg.de (web)
web.archive.org (webarchive)
application/pdf  4.2 MB
file_lsnq574yqvhlxlgbstgckbyole
ora.ox.ac.uk (web)
web.archive.org (webarchive)
application/pdf  2.4 MB
file_qv6doqsbpjeppf2xa6hnhk3q3y
web.archive.org (webarchive)
www.biorxiv.org (web)
application/pdf  4.2 MB
file_uhxlxhjupzblflusy26usrzb4i
web.archive.org (webarchive)
qmro.qmul.ac.uk (web)
Read Archived PDF
Preserved and Accessible
Type  post
Stage   unknown
Date   2018-02-05
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 99fd8f22-d6f6-40db-ab26-44fbca42d39a
API URL: JSON