AI Article Synopsis

  • A major challenge in zooarchaeology is identifying closely related species from small bone fragments, often complicated by low-quality reference genomes.
  • Researchers propose a new method called MTaxi that uses mitochondrial DNA (mtDNA) differences to effectively distinguish between closely related species like sheep/goats and horses/donkeys.
  • MTaxi has been shown to work efficiently with low mtDNA coverage and achieved 100% accuracy in tests, making it a valuable tool for ancient DNA analysis in zooarchaeology.

Article Abstract

A major challenge in zooarchaeology is to morphologically distinguish closely related species' remains, especially using small bone fragments. Shotgun sequencing aDNA from archeological remains and comparative alignment to the candidate species' reference genomes will only apply when reference nuclear genomes of comparable quality are available, and may still fail when coverages are low. Here, we propose an alternative method, MTaxi, that uses highly accessible mitochondrial DNA (mtDNA) to distinguish between pairs of closely related species from ancient DNA sequences. MTaxi utilises mtDNA transversion-type substitutions between pairs of candidate species, assigns reads to either species, and performs a binomial test to determine the sample taxon. We tested MTaxi on sheep/goat and horse/donkey data, between which zooarchaeological classification can be challenging in ways that epitomise our case. The method performed efficiently on simulated ancient genomes down to 0.3x mitochondrial coverage for both sheep/goat and horse/donkey, with no false positives. Trials on n=18 ancient sheep/goat samples and n=10 horse/donkey samples of known species identity also yielded 100% accuracy. Overall, MTaxi provides a straightforward approach to classify closely related species that are difficult to distinguish through zooarchaeological methods using low coverage aDNA data, especially when similar quality reference genomes are unavailable. MTaxi is freely available at https://github.com/goztag/MTaxi.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565424PMC
http://dx.doi.org/10.12688/openreseurope.14936.3DOI Listing

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