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Filtering out the noise: metagenomic classifiers optimize ancient DNA mapping.

Shyamsundar Ravishankar¹, Vilma Perez^1,2, Roberta Davidson¹

¹Australian Centre for Ancient DNA (ACAD) and The Environment Institute, The School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia.

Briefings in Bioinformatics

|December 14, 2024

Summary

This study introduces a computational workflow using metagenomic classification to effectively reduce exogenous DNA contamination in ancient DNA (aDNA) studies. The method improves accuracy and efficiency, especially for samples with low endogenous DNA content.

Keywords:

Kraken2 ancient DNA contamination filtering, metagenomic classifiers

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Area of Science:

Ancient DNA Research
Bioinformatics
Genomics

Background:

Exogenous DNA contamination is a major challenge in ancient DNA (aDNA) studies, potentially compromising results.
Existing methods for contamination control have limitations in accurately distinguishing endogenous from exogenous DNA computationally.
There is a need for efficient computational tools to filter contaminating DNA in aDNA analyses.

Purpose of the Study:

To develop and validate a computational workflow for reducing exogenous DNA contamination in aDNA studies.
To improve the accuracy and efficiency of distinguishing endogenous DNA from contaminants.
To provide a resource-efficient method applicable to various computational environments.

Main Methods:

Proposed a workflow integrating Kraken2-based metagenomic classification prior to reference genome mapping.
Utilized both simulated and empirical shotgun aDNA data for testing the workflow.
Developed strategies for building specific databases tailored to computational resources and known taxa.

Main Results:

The workflow significantly reduced computational resources and runtime by up to ~94%, particularly in low endogenous DNA samples.
Effectively filtered contaminants that could otherwise lead to false positive alignments.
Demonstrated negligible loss of endogenous DNA, with no measurable impact on downstream population genetics analyses.

Conclusions:

The proposed Kraken2-based workflow offers a simple, efficient, and resource-conscious method for mitigating exogenous DNA contamination in aDNA studies.
This approach enhances the reliability of aDNA analyses by accurately filtering contaminants.
The method is suitable for diverse computational settings and preserves valuable endogenous genetic information.