What is it about?
Identification of specific species in samples that contain DNA from multiple organisms is critical, yet many taxonomic identification tools available are often prone to false positive identifications. HAYSTAC is a user-friendly and computationally scalable bioinformatic tool that can robustly identify species present in low abundances from DNA sequencing data (e.g. pathogens).
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Why is it important?
HAYSTAC is a program that is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases. Thus, it becomes the ideal tool for running highly accurate hypothesis-driven analyses (i.e., assessing the presence of a specific species) on variably sized reference databases.
Perspectives
Developing HAYSTAC and writing this article has been a great learning experience and pleasure, as the co-authors are excellent scientists with whom I have had long standing collaborations. I hope our methodological approach further encourages scientists to use metagenomics in their research to answer novel questions and trust more the resulting metagenomic identifications.
Evangelos Dimopoulos
University of Oxford
Read the Original
This page is a summary of: HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data, PLoS Computational Biology, September 2022, PLOS,
DOI: 10.1371/journal.pcbi.1010493.
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Contributors
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