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E.D.O and S.G.M. conceived the study. E.D.O conducted the bioinformatic and statistical analyses. M.S.S. supervised the project. All authors contributed to the writing and editing of the manuscript.
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Extended data
Extended Data Fig. 1
Percent of metagenomic reads not classified to any taxon (a), percent of reads classified as bacteria between forested and non-forested ecosystems (b), and percentage of genome equivalents recovered in the ‘bacterial’ metagenomes that were found in the full metagenomes (c). Classification was done using kraken2 with RefSeq genomes for bacteria, archaea, viruses, fungi, and protists as reference databases. Genomes within the metagenomes were quantified by determining mean coverage of 30 single-copy genes using MicrobeCensus. The R2 value and line of best fit in (a) and (c) are from linear regression. Asterisks in (b) indicate significantly higher percentage of reads classified as bacterial in non-forested environments (p < 0.001).
Extended Data Fig. 2
Average genome size in the full metagenomes as a function of the percentage of reads classified as bacteria (a) and the ratio of eukaryotic to bacterial reads (b). R2 values and lines of best fit are from linear regression (both p < 0.001). Average genome sizes were determined using MicrobeCensus.
Extended Data Fig. 3
Metagenome GC content in the full (a) and bacterial (b) metagenomes and average 16S rRNA gene copy number in the full (c) and bacterial (d) metagenomes as a function of soil pH. R2 values and best-fit lines on (a) and (b) are from linear regression (both p < 0.001). Regression models for 16S gene copy number in (c) and (d) were not significant (both p > 0.05).
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Osburn, E.D., McBride, S.G. & Strickland, M.S. Microbial dark matter could add uncertainties to metagenomic trait estimations. Nat Microbiol (2024). https://doi.org/10.1038/s41564-024-01687-w
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DOI: https://doi.org/10.1038/s41564-024-01687-w
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