A damage-aware NGS workflow for conservative species identification from ultra-degraded DNA
Stefania Morelli1, Sara Romano1, Giulia Cosenza1
1IRIS (Infrastruttura per la Ricerca e l'Identificazione degli Scheletri senza nome) Dipartimento di Biologia, Università degli Studi di Firenze, Florence, Italy.
Analytical and Bioanalytical Chemistry
|June 12, 2026
Summary
This study introduces a new DNA analysis workflow to accurately identify species from tiny, degraded DNA samples. The method minimizes errors, improving conservation genetics and forensic science applications.
Area of Science:
- Genetics
- Molecular Biology
- Conservation Science
Background:
- Species identification from degraded DNA is challenging in ecology, conservation, forensics, and museum science.
- Standard methods often yield false positives with fragmented DNA and conserved genomic regions.
Purpose of the Study:
- To develop a damage-aware next-generation sequencing (NGS) workflow for accurate species identification from minute, degraded DNA samples.
- To minimize misclassification in low-input and damage-rich datasets.
Main Methods:
- Integrates micro-sampling and half-uracil-DNA-glycosylase (half-UDG) library preparation.
- Includes PCR duplicate removal, multi-genome mapping, and a post-mapping read-ubiquity classifier.
- Utilizes a curated reference panel to distinguish species-specific from conserved reads.
Main Results:
- Accurate species identification from samples as small as 1 mm², including mixtures and mineralized matrices.
- Reliably identifies dominant biological sources and reduces false positives from conserved regions.
- Demonstrates robustness to physical and chemical treatments like swelling, heating, and plaster addition.
Conclusions:
- Presents a proof-of-concept for conservative species identification in challenging degraded DNA contexts.
- The workflow is adaptable for wildlife monitoring, forensics, and analysis of processed biological materials.
- Further validation across diverse matrices is needed for broader application.
