Fast and accurate average genome size and 16S rRNA gene average copy number 
computation in metagenomic data

Pereira-Flores, Emiliano; Gloeckner, Frank Oliver; Fernandez-Guerra, Antonio

doi:10.1186/s12859-019-3031-y

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Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data

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Pereira-Flores, Emiliano
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Gloeckner, Frank Oliver
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Fernandez-Guerra, Antonio
Microbial Genomics Group, Department of Molecular Ecology, Max Planck Institute for Marine Microbiology, Max Planck Society;

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Citation

Pereira-Flores, E., Gloeckner, F. O., & Fernandez-Guerra, A. (2019). Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data. BMC Bioinformatics, 20(1): 453. doi:10.1186/s12859-019-3031-y.

Cite as: https://hdl.handle.net/21.11116/0000-0005-BA48-9

Abstract

Background: Metagenomics caused a quantum leap in microbial ecology.
However, the inherent size and complexity of metagenomic data limit its
interpretation. The quantification of metagenomic traits in metagenomic
analysis workflows has the potential to improve the exploitation of
metagenomic data. Metagenomic traits are organisms' characteristics
linked to their performance. They are measured at the genomic level
taking a random sample of individuals in a community. As such, these
traits provide valuable information to uncover microorganisms'
ecological patterns. The Average Genome Size (AGS) and the 16S rRNA gene
Average Copy Number (ACN) are two highly informative metagenomic traits
that reflect microorganisms' ecological strategies as well as the
environmental conditions they inhabit.
Results: Here, we present the ags.sh and acn.sh tools, which
analytically derive the AGS and ACN metagenomic traits. These tools
represent an advance on previous approaches to compute the AGS and ACN
traits. Benchmarking shows that ags.sh is up to 11 times faster than
state-of-the-art tools dedicated to the estimation AGS. Both ags.sh and
acn.sh show comparable or higher accuracy than existing tools used to
estimate these traits. To exemplify the applicability of both tools, we
analyzed the 139 prokaryotic metagenomes of TARA Oceans and revealed the
ecological strategies associated with different water layers.
Conclusion: We took advantage of recent advances in gene annotation to
develop the ags.sh and acn.sh tools to combine easy tool usage with fast
and accurate performance. Our tools compute the AGS and ACN metagenomic
traits on unassembled metagenomes and allow researchers to improve their
metagenomic data analysis to gain deeper insights into microorganisms'
ecology. The ags.sh and acn.sh tools are publicly available using Docker
container technology at
https://github.com/pereiramemo/AGS-and-ACN-tools.