Archived code for "A composite universal DNA signature for the Tree of Life"
Creators: name: de Medeiros, Bruno A. S.
name: CAI, LIMING
name: Flynn, Peter
name: Yan, Yujing
name: duan, xiaoshan
name: MARINHO, Lucas C.
name: Anderson, Christiane
name: Asprino, Renata
name: Davis, Charles C.
Publisher: name: figshare
Year: 2025
Subjects: classificationCode: 310499
subject: Evolutionary biology not elsewhere classified
classificationCode: 310201
subject: Bioinformatic methods development
classificationCode: 461104
subject: Neural networks

description: This repository includes archived code repositories used in the following publications: <br><br>de Medeiros et al (2025) A composite universal DNA signature for the Tree of Life. Nature Ecology &amp; Evolution. <br>Asprino et al (2025) A curated benchmark dataset for molecular identification based on genome skimming. Scientific Data<br><br>varKoder-main is the version of varKoder at the time of manuscript submission (see https://github.com/brunoasm/varKoder for the latest version). varKoder_development-main is a repository with code used in development and analyses.<br><br>## Abstract of related publications<br>### de Medeiros et al (2025)<br>Species identification using DNA barcodes has revolutionized biodiversity sciences. However, conventional barcoding methods may lack power and universal applicability across the Tree of Life. Alternative methods based on whole genome sequencing are hard to scale due to large data requirements. Here, we develop a novel DNA-based identification method, varKoding, using exceptionally low-coverage genome skim data to create two-dimensional images representing the genomic signature of a species. Using these representations, we train neural networks for taxonomic identification. Applying a taxonomically verified novel genomic dataset of Malpighiales plant accessions, we optimize training hyperparameters and find the highest performance by combining a transformer architecture with a new modified chaos game representation. Greater than 91% precision is achieved despite minimal input data, exceeding alternative methods tested. We illustrate the broad utility of varKoding across several focal clades of eukaryotes and prokaryotes. We also train a model capable of identifying all species in NCBI SRA using less than 10 Mbp sequencing data with 96% precision and 95% recall and robust to sequencing platforms. The varKoding approach offers enhanced computational efficiency and scalability, minimal data inputs robust to sequencing details, and modularity for further development in biodiversity science.<br><br>### Asprino et al (2025)<br>Genome skimming is an emerging tool allowing for scalable DNA barcoding efforts for numerous biodiversity science applications. Despite its growing importance, there are few standardized datasets for benchmarking genome skimming tools, making it challenging to evaluate new methods (e.g., using machine learning), and comparing to existing ones (e.g., conventional barcoding loci). As part of the development of varKoder, a new tool for DNA-based identification, we curated four datasets designed for comparing molecular identification tools using low-coverage genomes. These datasets comprise vast phylogenetic and taxonomic diversity from closely related species to all taxa currently represented on NCBI SRA. One of them consists of novel sequences from taxonomically verified samples in the plant clade Malpighiales, while the other three datasets compile publicly available data. All include raw genome skim sequences to enable comprehensive testing and validation of a variety molecular species identification methods. We also provide the two-dimensional graphical representations of genomic data (chaos game representations and varKodes) that have been used to develop and test varKoder. These datasets represent a reliable resource for researchers to assess the accuracy, efficiency, and robustness of new tools to varKoder and other methods in a consistent and reproducible manner.