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Updated: Jun 11, 2025

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

Quantifying microbial DNA in metagenomes improves microbial trait estimation.

Raphael Eisenhofer¹, Antton Alberdi¹, Ben J Woodcroft²

¹Centre for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.

ISME Communications

|September 30, 2024

Summary

This summary is machine-generated.

Shotgun metagenomics can overestimate microbial genome size due to non-microbial DNA. A new tool corrects this bias, improving ecological insights and revealing a stronger link between bacterial genome size and soil pH.

Keywords:

bioinformatics metagenomics microbial ecology microbiology microbiome soil

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Last Updated: Jun 11, 2025

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Published on: December 22, 2014

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

Area of Science:

Microbial ecology
Bioinformatics
Genomics

Background:

Shotgun metagenomics analyzes microbial communities but can be biased by non-bacterial/archaeal DNA.
Existing tools often assume all reads are microbial, potentially skewing genomic trait estimations.
Eukaryotic and viral DNA proportions can significantly impact accuracy.

Purpose of the Study:

To investigate the influence of eukaryotic DNA on average genome size estimation in soil metagenomes.
To introduce and validate a new bioinformatic tool for correcting non-microbial DNA bias.
To re-evaluate the relationship between bacterial genome size and soil pH using corrected data.

Main Methods:

Reanalysis of a global soil sample dataset using a novel bioinformatic tool.
Quantification and correction for eukaryotic and viral DNA proportions in metagenomic data.
Statistical analysis of corrected average bacterial genome size in relation to soil pH.

Main Results:

Soil metagenomes contain substantial non-microbial DNA, inflating original genome size estimates.
Correction for non-microbial DNA significantly reduced inflated genome size estimations.
The negative relationship between average bacterial genome size and soil pH was strengthened after bias correction.

Conclusions:

Metagenomic datasets frequently include significant amounts of non-microbial DNA.
New bioinformatic methods are crucial for accurately estimating microbial traits from metagenomic data.
Correcting for DNA source bias enhances ecological and evolutionary interpretations of microbial communities.