基于宏基因组鸟枪测序的中国典型城市灰尘地域推断研究

doi:10.16288/j.yczz.25-009

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Hereditas(Beijing) ›› 2025, Vol. 47 ›› Issue (10): 1156-1168.doi: 10.16288/j.yczz.25-009

• Research Article • Previous Articles Next Articles

Geographical inference of dust from typical Chinese cities based on metagenomic shotgun sequencing

Qi Yang¹^,²(), Kelai Kang², Bo Zhao³^,⁴, Kai Feng³^,⁴, Yaosen Feng², Jian Ye¹^,²(), Ye Deng³^,⁴(), Le Wang²()

1. People’s Public Security University of China, Beijing 100038, China
2. Key Laboratory of Forensic Genetics, Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
3. Key Laboratory of Environmental Biotechnology of CAS, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2025-02-12 Revised:2025-03-20 Online:2025-10-20 Published:2025-03-21
Contact: Jian Ye, Ye Deng, Le Wang E-mail:458425932@qq.com;yejian77@126.com;yedeng@rcees.ac.cn;wangle_02@163.com
Supported by:
Institute of Forensic Science, Ministry of Public Security of China(2022JB022);Ministry of Public Security of China(2023JC14)

Abstract

Abstract:

Microbial profiles in dust are closely correlated with geographical locations and provide valuable clues for criminal investigation, demonstrating significant potential in forensic use. However, the feasibility of using microbial profiles from metagenomics datasets to infer the geographical locations remains underexplored. In this study, we collect 170 dust samples from resident communities in four cities across northern, eastern, southwestern, and northwestern China. All samples are subjected to shotgun metagenomic sequencing to reveal variations in microbial composition. In total, 41,029 species are annotated, including 93.39% bacteria, 6.37% eukaryotes, 0.21% viruses, and 0.03% archaea. Clear clustering patterns are observed among the four cities (R²=0.870, P<0.001). Further filtering of species with detection rates below 10% across all samples strengthens city-level clustering (R²=0.948, P<0.001). Additionally, 127 biomarkers are identified using linear discriminant analysis effect size (LEfSe) to distinguish between the cities. Each city harbors a distinct microbial community, with unique species and relatively abundant taxa that contribute to its differentiated microbial profile. All samples are randomly split into training and testing sets in a 7:3 ratio. Five machine learning models including SourceTracker, FEAST, LightGBM, Random Forest and Support Vector Machine are applied to 51 randomly sample data and achieve average accuracies of 88.89%, 92.16%, 98.04%, 99.35% and 69.28%, respectively. These results constitute a microbial genetic map of four cities in China that highlights distinct microbial taxonomic signatures and provides an approach for city-scale source tracking of dust samples.

Key words: Dust, metagenomic shotgun sequencing, microbial composition, geographical inference

Qi Yang, Kelai Kang, Bo Zhao, Kai Feng, Yaosen Feng, Jian Ye, Ye Deng, Le Wang. Geographical inference of dust from typical Chinese cities based on metagenomic shotgun sequencing[J]. Hereditas(Beijing), 2025, 47(10): 1156-1168.

Figures/Tables 18

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Table 1

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采样点	纬度	经度	采样点	纬度	经度
BJ01-A	40.009444	116.286111	BJ15-C	39.850833	116.428333
BJ02-C	39.970833	116.252500	BJ17-A	39.841389	116.304444
BJ03-A	39.920278	116.287222	BJ17-B	39.841111	116.303889
BJ03-C	39.920833	116.287222	BJ18-A	39.889722	116.296111
BJ04-A	39.986667	116.311944	BJ18-C	39.888889	116.295556
BJ04-C	39.986944	116.314167	BJ19-A	39.907222	116.185556
BJ05-C	39.855833	116.502500	BJ20-B	39.941944	116.203056
BJ06-A	39.891111	116.513333	BJ20-C	39.908889	116.185833
BJ09-B	39.897778	116.331389	BJ21-B	39.880000	116.373889
BJ09-C	39.897778	116.333056	BJ21-C	39.880833	116.374167
BJ10-A	39.892778	116.410556	BJ22-C	39.913889	116.361111
BJ10-B	39.893611	116.410833	BJ23-A	39.943611	116.360556
BJ10-C	39.893889	116.409722	BJ23-C	39.942778	116.359167
BJ11-A	39.920833	116.406944	BJ24-C	39.919444	116.601389
BJ12-A	39.949444	116.393889	BJ25-A	39.947500	116.450278
BJ12-B	39.948889	116.394167	BJ25-B	39.947778	116.450000
BJ12-C	39.949444	116.393889	BJ25-C	39.948333	116.450833
BJ14-A	39.978333	116.412500	BJ27-A	39.957778	116.277222
BJ15-A	39.851111	116.427778	BJ27-B	39.958333	116.276389
BJ15-B	39.850833	116.428056	BJ28-A	40.032222	116.282500

采样点	纬度	经度	采样点	纬度	经度
FZ01-A	25.953333	119.542778	FZ11-A	26.070556	119.331667
FZ02-B	25.940833	119.490833	FZ11-B	26.070556	119.331944
FZ02-C	25.941389	119.491111	FZ11-D	26.070278	119.331667
FZ03-A	25.997778	119.456389	FZ12-B	26.088333	119.281944
FZ03-D	25.997778	119.456667	FZ12-C	26.088611	119.281944
FZ04-A	26.026944	119.394722	FZ12-D	26.087500	119.281667
FZ04-B	26.026389	119.394722	FZ13-B	26.065000	119.280000
FZ04-C	26.026389	119.394444	FZ13-D	26.065000	119.279444
FZ05-C	25.984167	119.252778	FZ15-C	26.011389	119.278333
FZ05-D	25.984167	119.252500	FZ16-A	26.048333	119.318611
FZ06-A	25.903611	119.371667	FZ16-B	26.048333	119.318611
FZ06-B	25.903611	119.371389	FZ16-D	26.048333	119.318611
FZ06-C	25.903889	119.371389	FZ17-B	36.053056	119.343889
FZ07-A	25.990278	119.353611	FZ17-C	36.053056	119.344167
FZ07-B	25.990278	119.370556	FZ19-A	26.090000	119.335278
FZ07-C	25.990000	119.371111	FZ19-B	26.090556	119.335000
FZ09-A	26.018056	119.320833	FZ20-B	26.146944	119.333889
FZ09-D	26.018056	119.321389	FZ20-D	26.146667	119.334167
FZ10-A	26.048056	119.365556	FZ21-A	26.147778	119.311667
FZ10-C	26.048333	119.364167	FZ22-B	26.133611	119.283889
FZ10-D	26.047778	119.365000	FZ23-B	26.106389	119.288333

采样点	纬度	经度	采样点	纬度	经度
KM02-A	24.850556	102.823889	KM16-C	25.026111	102.874167
KM02-B	24.850556	102.823889	KM17-A	25.054167	102.796667
KM02-C	24.857778	102.863056	KM17-C	25.053333	102.795278
KM03-A	24.843611	102.845556	KM18-A	25.046389	102.741944
KM03-D	24.844167	102.846667	KM18-B	25.046111	102.742500
KM04-A	24.761667	102.803056	KM18-C	25.045833	102.741944
KM05-B	25.010833	102.782500	KM19-A	25.085556	102.727778
KM06-B	24.980278	102.822778	KM19-B	25.085278	102.727500
KM08-C	24.996667	102.735556	KM20-B	25.041667	102.650556
KM09-A	25.030556	102.752222	KM20-C	25.041667	102.650556
KM09-B	25.030278	102.753333	KM20-D	25.042500	102.650000
KM09-C	25.030833	102.751944	KM21-A	25.075556	102.697222
KM09-D	25.031111	102.751944	KM21-C	25.072500	102.699444
KM10-A	25.010278	102.624444	KM22-B	25.119722	102.745278
KM11-A	25.042778	102.637500	KM22-C	25.119167	102.746667
KM11-C	25.010278	102.624444	KM22-D	25.118611	102.745556
KM12-A	25.054722	102.653333	KM23-A	25.953889	102.779444
KM13-A	25.110833	102.719167	KM23-C	25.953889	102.779444
KM13-B	25.110833	102.719167	KM24-B	25.988333	102.817222
KM13-C	25.110833	102.719167	KM24-C	25.988611	102.817500
KM14-A	25.095278	102.665556	KM25-B	25.003056	102.781111
KM15-B	25.006667	102.822778	KM26-B	25.998056	102.782778
KM15-C	25.006389	102.822500	KM27-A	25.856111	102.861111
KM15-D	25.006111	102.823056

采样点	纬度	经度	采样点	纬度	经度
WQ01-A	43.812222	87.634444	WQ11-C	43.842778	87.401389
WQ01-B	43.812222	87.634722	WQ12-A	43.869167	87.426111
WQ02-A	43.795278	87.610833	WQ12-C	43.868611	87.426667
WQ02-B	43.795000	87.610833	WQ13-B	43.840833	87.497222
WQ03-A	43.813611	87.608333	WQ13-C	43.840833	87.496944
WQ03-B	43.813611	87.608889	WQ14-C	43.883611	87.651111
WQ03-C	43.813611	87.608889	WQ16-A	43.818611	87.620000
WQ03-D	43.813889	87.608611	WQ16-C	43.818889	87.619722
WQ04-A	43.744722	87.559722	WQ19-C	43.965833	87.660556
WQ05-A	43.764722	87.647778	WQ20-B	43.953611	87.690278
WQ05-B	43.764722	87.647778	WQ22-A	43.890556	87.492778
WQ05-C	43.765000	87.647500	WQ22-C	43.890833	87.493056
WQ05-D	43.765000	87.647500	WQ23-A	43.906944	87.575556
WQ06-A	43.815278	87.542500	WQ23-B	43.907222	87.575556
WQ06-B	43.815278	87.542778	WQ23-C	43.906944	87.575833
WQ06-C	43.815556	87.543056	WQ24-A	43.863056	87.598056
WQ07-B	43.798889	87.524722	WQ24-B	43.863056	87.597778
WQ08-A	43.792500	87.553056	WQ24-C	43.863333	87.598056
WQ09-C	43.790278	87.656944	WQ25-A	43.857500	87.540000
WQ10-C	43.830278	87.678056	WQ25-C	43.857222	87.539444
WQ11-A	43.843611	87.400556

模型	加权精确度	加权召回率	加权 F1分数	平均准确率
SourceTracker	96.06%	88.89%	91.94%	88.89%
FEAST	95.50%	92.16%	93.59%	92.16%
LightGBM	98.10%	98.04%	98.05%	98.04%
随机森林	99.39%	99.35%	99.35%	99.35%
支持向量机	76.74%	69.28%	66.46%	69.28%

Geographical inference of dust from typical Chinese cities based on metagenomic shotgun sequencing

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