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Comparative Study
. 2025 Feb 19;15(1):6013.
doi: 10.1038/s41598-025-88443-0.

Facilitating high throughput collections-based genomics: a comparison of DNA extraction and library building methods

Affiliations
Comparative Study

Facilitating high throughput collections-based genomics: a comparison of DNA extraction and library building methods

William A Marsh et al. Sci Rep. .

Erratum in

Abstract

While DNA barcoding methods are an increasingly important tool in biological conservation, the resource requirements of constructing reference libraries frequently reduce their efficacy. One efficient way of sourcing taxonomically validated DNA for reference libraries is to use museum collections. However, DNA degradation intrinsic to historical museum specimens can, if not addressed in the wet lab, lead to low quality data generation and severely limit scientific output. Several DNA extraction and library build methods that are designed to work with degraded DNA have been developed, although the ability to implement these methods at scale and at low cost has yet to be formally addressed. Here, the performance of widely used DNA extraction and library build methods are compared using museum specimens. We find that while our selected DNA extraction methods do not significantly differ in DNA yield, the Santa Cruz Reaction (SCR) library build method is not only the most effective at retrieving degraded DNA from museum specimens but also easily implemented at high throughput for low cost. Results highlight the importance of lab protocol on data yield. An optimised "sample to sequencing" high-throughput protocol which incorporates SCR is included to allow for easy uptake by the wider scientific community.

Keywords: Biodiversity; Historical DNA; Museomics; Museum collections.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The effect of DNA extraction and library building protocols on (a) endogenous content; (b) collapsed read proportion; (c) library complexity (as calculated using preseq); and (d) aligned q30 unique read length distribution. See Supplementary Table 1 for detailed alignment statistics.

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