Abstract
The broadening utilisation of ancient DNA to address archaeological, palaeontological, and biological questions is resulting in a rising diversity in the size of laboratories and scale of analyses being performed. In the context of this heterogeneous landscape, we present an advanced, and entirely redesigned and extended version of the EAGER pipeline for the analysis of ancient genomic data. This Nextflow pipeline aims to address three main themes: accessibility and adaptability to different computing configurations, reproducibility to ensure robust analytical standards, and updating the pipeline to the latest routine ancient genomic practices. The new version of EAGER has been developed within the nf-core initiative to ensure high-quality software development and maintenance support; contributing to a long-term life-cycle for the pipeline. nf-core/eager will assist in ensuring that a wider range of ancient DNA analyses can be applied by a diverse range of research groups and fields.
Highlights
Ancient DNA has become a widely accepted source of biological data, helping to provide new perspectives for a range of fields including archaeology, cultural heritage, evolutionary biology, ecology, and palaeontology
We have developed a corresponding MultiQC module for every tool used by nf-core/eager, where possible, to enable comprehensive evaluation of all stages of the pipeline
We have further extended the functionality of the original EAGER pipeline by adding ancient metagenomic analysis (Fig. 3); allowing reconstruction of the wider taxonomic content of a sample
Summary
Ancient DNA (aDNA) has become a widely accepted source of biological data, helping to provide new perspectives for a range of fields including archaeology, cultural heritage, evolutionary biology, ecology, and palaeontology. Improvement in laboratory protocols to increase yields of otherwise trace amounts of DNA has at the same time led to studies that can total hundreds of ancient individuals (Olalde et al, 2018; Mathieson et al, 2018), spanning single (Bos et al, 2011) to thousands of organisms (Warinner et al, 2014). These differences of disciplines have led to a heterogeneous landscape in terms of the types of analyses undertaken, and their computational resource requirements (Tastan Bishop et al, 2015; Bah et al, 2018). Taking into consideration the unequal distribution of resources (and infrastructure such as internet connection), easy-to-deploy, streamlined and efficient pipelines can help increase accessibility to high-quality analyses
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