Raw sequencing data and ngsfilters for snow track eDNA samples

Continued advancements in environmental DNA (eDNA) research have made it possible to access intraspecific variation from eDNA samples, opening new opportunities to expand non-invasive genetic studies of wildlife populations. However, the use of eDNA samples for individual genotyping, as typically performed in non-invasive genetics, still remains elusive. We present the first successful individual genotyping of eDNA obtained from snow tracks of three large carnivores: brown bear (Ursus arctos), European lynx (Lynx lynx) and wolf (Canis lupus). DNA was extracted using a protocol for isolating water eDNA and genotyped using amplicon sequencing of short tandem repeats (STR) and, for brown bear, a sex marker, on a high-throughput sequencing platform. Individual genotypes were obtained for all species, but genotyping performance differed among samples and species. The proportion of samples genotyped to individuals was higher for brown bear samples (5/7) than for wolf (7/10) and lynx (4/9), bu..., Snow tracks of brown bear, lynx and wolf were collected opportunistically during winter in 2019, 2020 and 2022 in the Slovenian Alps and Dinaric Mountains (seven brown bear samples and nine lynx samples) and in the French Alps (ten wolf samples). For genotyping of each species, we used a set of STR markers designed for optimal multiplex amplification and HTS genotyping. The brown bear set includes 13 STR recently described and used for individual profiling from fecal DNA, with the addition of a sex-specific marker. For wolf and lynx, we used 13 new STRs. We performed sequencing on a NovaSeq platform (2x150 bp) (libraries with id DIVJA088, DIVJA089, DAB074) and on a Miniseq platform (2x150 bp) (library LF22). Amplicons were sequenced in multiplexes, and necessary information for demultiplexing is in .ngsfilter files., , # Raw sequencing data and ngsfilters for snow track eDNA samples

[https://doi.org/10.5061/dryad.9kd51c5q1](https://doi.org/10.5061/dryad.9kd51c5q1)

## Description of the data and file structure

The dataset contains four libraries with STR amplicons for three carnivore species: DIVJA088 (brown bear), DIVJA089 (brown bear), DAB074 (brown bear, wolf and lynx), LF22 (wolf). Besides raw reads, the ngsfilter file is attached to each library. Ngsfilter is used for further data processing and contains information about sample name, sample position on plate, primers and tags sequences. More details about mulitplexing concept can be found in De Barba et al., 2017.

**De Barba, M., Miquel, C., Lobréaux, S., Quenette, P. Y., Swenson, J. E., & Taberlet, P. (2017). High-throughput microsatellite genotyping in ecology: Improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Molecular Ecology Resources, 17(3), 492–507.**

## Code/Software

To process the da...

环境DNA（eDNA）研究的持续进步，使得从eDNA样本中获取种内变异信息成为可能，为拓展野生动物种群的非侵入式遗传研究开辟了新机遇。然而，将eDNA样本用于个体基因分型（正如非侵入式遗传学中的常规操作）仍难以实现。本研究首次成功实现了从三种大型食肉动物的雪迹样本中获取的eDNA的个体基因分型，这三种食肉动物分别为棕熊（Ursus arctos）、欧亚猞猁（Lynx lynx）与灰狼（Canis lupus）。本研究采用水体eDNA分离的实验方案提取DNA，并通过高通量测序平台对短串联重复序列（STR）的扩增子进行测序以完成基因分型；针对棕熊样本，同时加入了性别特异性标记进行分型。所有物种均成功获得了个体基因型，但不同样本与物种间的基因分型效果存在差异。棕熊样本的成功分型率（5/7）高于灰狼（7/10）与猞猁（4/9），但……

研究于2019、2020及2022年的冬季，在斯洛文尼亚阿尔卑斯山与迪纳拉山脉（采集到7份棕熊样本、9份猞猁样本）以及法国阿尔卑斯山（采集到10份灰狼样本）中，通过机会性采样方式收集了棕熊、猞猁与灰狼的雪迹样本。针对各物种的基因分型，本研究采用了一套专为优化多重扩增与高通量测序分型而设计的STR标记组合。棕熊的标记组合包含13个此前已被报道、用于粪便DNA个体识别的STR位点，并额外加入了一个性别特异性标记。针对灰狼与猞猁，本研究使用了13个全新的STR位点。本研究分别在NovaSeq平台（2×150 bp测序模式，对应文库编号DIVJA088、DIVJA089、DAB074）与Miniseq平台（2×150 bp测序模式，对应文库编号LF22）上完成了测序。扩增子以多重测序的方式完成测序，用于解复用的必要信息均存储于.ngsfilter格式的文件中。

# 雪迹eDNA样本的原始测序数据与ngsfilter文件
[https://doi.org/10.5061/dryad.9kd51c5q1](https://doi.org/10.5061/dryad.9kd51c5q1)

## 数据与文件结构说明
本数据集包含针对三种食肉动物的STR扩增子文库共4个：DIVJA088（棕熊）、DIVJA089（棕熊）、DAB074（涵盖棕熊、灰狼与猞猁）以及LF22（灰狼）。除原始测序读段外，每个文库均附带对应的ngsfilter文件。ngsfilter文件可用于后续数据处理，其中包含样本名称、样本在测序板上的位置、引物与标签序列等信息。关于多重测序策略的更多细节可参考De Barba等人2017年的研究。

**De Barba, M., Miquel, C., Lobréaux, S., Quenette, P. Y., Swenson, J. E., & Taberlet, P. (2017). 生态学中的高通量微卫星基因分型：针对低含量与降解DNA的准确性、效率、标准化与成功率提升. 《分子生态学资源》, 17(3), 492–507.**

## 代码与软件
To process the da...