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. 2025 Jan 11;12:57. doi: 10.1038/s41597-025-04400-6

Chromosome-level genome assembly, annotation, and population genomic resource of argali (Ovis ammon)

Mu-Yang Wang 1,2,3,#, Bao-Lin Zhang 4,5,6,#, Qi-Qi Liang 7,#, Xin-Ming Lian 8,9,10, Ke Zhang 8,9,10, Qi-En Yang 8,9,10,✉, Wei-Kang Yang 1,2,3,✉
PMCID: PMC11724849  PMID: 39799149

Abstract

Argali stands as the largest species among wild sheep in Central and East Asia, with a concerning rate of decline estimated at 30%. The intraspecific taxonomy of argali remains contentious due to limited genomic data and unclear geographic separation. In this study, we constructed a chromosome-level genome assembly and annotation for the Tibetan argali (O. a. hodgsoni), together with population genomic resequencing of 32 individuals representing four subspecies. The contig-level genome was 2.64 Gb in size, with a contig N50 length of 71.69 Mb and an estimated genomic completeness of 96.01%. Using Hi-C sequencing data scaffolding, 99.90% of initially assembled sequences were mapped and oriented onto 28 pseudo-chromosomes except the Y chromosome. Annotation uncovered 21,564 protein-coding genes and 46.38% repeat sequences. The average coverage of the population resequencing data was 23.74 with mean mapping ratio up to of 97.19%. The high-quality genome assembly and annotation of the Tibetan argali, coupled with the high-depth population genomic data, will serve as a valuable genetic resource for studies on the taxonomy and conservation of argali.

Subject terms: Zoology, Evolution

Background & Summary

The argali sheep (Ovis ammon) is the largest wild species of genus Ovis that native to the mountainous regions of Central and East Asia, with current distribution spanning from the Irtysh River and Altai Mountains in the north to the Himalayas (China, Nepal) in the south, and from the Oxus River (Uzbekistan) in the west to the Mongolian Plateau in the east1. Despite their wide geographic distribution, argali face numerous threats, including over-hunting and poaching for meat and horns, competition with domestic livestock, habitat loss, and potential disease transmission from domestic animals2,3. Consequently, this species has undergone significant decline over the past two centuries, no longer present in areas such as northeastern China, southern Siberia, and parts of Mongolia4. Currently, the argali sheep is classified as Near Threatened (NT) species by the International Union for Conservation of Nature and Natural Resources (IUCN) Red List and Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Despite varying chromosome numbers, the argali sheep is capable of generating fertile offspring when crossbred with domestic sheep, and recent research has leveraged this capability to expedite the genetic improvement of domestic sheep5.

Argali exhibit genetic diversity across their geographic range, yet their intraspecific taxonomy remains disputed. Various subspecies have been recognized, ranging from 4 to 166–9, including the Altai (O. a. ammon), Gobi (O. a. darwini), Tian Shan (O.a. karelini), Pamir (O. a. polii), Kyzylkum (O. a. severtzovi), and Tibetan (O. a. hodgsoni). These subspecies are distinguished by differences in pelage, body size and horn characters. However, these variations are often insufficient and may be obscured by unclear geographic separation or seasonal and age-related morphological variations10,11. The conservation status of these subspecies also varies12. Therefore, genomic information from the wild populations across their range could help understand the taxonomy, ecology, and genetic status of the argali. However, there is currently a limited amount of genomic data available for the wild argali sheep, with all existing genomic information exclusively pertaining to the Pamir subspecies5,13,14.

In this study, we assembled a new chromosome-level genome of argali sheep, the Tibetan argali, using long-read sequencing platform of Oxford Nanopore Technologies (ONT) with a combination of short-read sequencing and Hi-C scaffolding. The resulting assembly has a total genomic size of 2.64 Gb, organized into 28 chromosomes, with a contig N50 of 71.69 Mb. The complete BUSCO value of the new genome was 96.01%, and with a high base accuracy of 99.9995%. Furthermore, we present high-coverage genomic sequences of 32 wild argali samples from different localities, representing four distinct subspecies. The high-quality Tibetan argali genome, coupled with population-level genomic data, will serve as a valuable resource for future research into the taxonomy, ecology, and conservation of argali sheep.

Methods

Reference genome sequencing

Fresh tissue from a dead male argali in Yeniugou (Golmud, Qinghai Province, China), which had been just killed by packs of wolves, was collected and quickly stored at −80 °C during the field survey in 2020. The genomic DNA was extracted and purified with QIAGEN® Genomic kit (Cat#13343, QIAGEN) according to the standard operating procedure provided by the manufacturer. The DNA degradation and contamination was monitored on 1% agarose gels. After assessing DNA quality, size-select of long DNA fragments were prepared for the ONT library. Sequencing was performed on Nanopore PromethION platform, and generated 136.68 Gb of clean data (~50X).

For the Illumina short-reads sequencing, genomic DNA was randomly fragmented and a library with an average insert size of 350 bp was constructed according to Illumina’s standard protocol. The library was sequenced on Illumina novaseq6000 platform in paired-end reads (150 bp) program. After filtering the low quality and short reads, and the adapter sequences by fastp v0.12.43115, we generated a total of 407.56 Gb (~150X) clean data for downstream analyses.

For the Hi-C library, we used 2% formaldehyde for crosslinking cells. Crosslinking was stopped by adding glycine and additional vacuum infiltration. Fixed tissue was then grounded to powder before re-suspending in nuclei isolation buffer to obtain a suspension of nuclei. The purified nuclei were digested with 100 units of DpnII and marked by incubating with biotin-14-dCTP. The ligated DNA was sheared into 300−600 bp fragments, and then was blunt-end repaired and A-tailed, followed by purification through biotin-streptavidin-mediated pull down. Finally, the Hi-C libraries were quantified and sequenced using the Illumina novaseq6000 platform, generating a total of 309.40 Gb (~115X) data.

Chromosome-level genome assembly

The initial assembly of argali genome was constructed by using the software of NextDenovo16 (reads_cutoff:1k, seed_cutoff:15622). Considering the high error rate of ONT raw reads, the original subreads were first self-corrected using NextCorrect under default parameter, then the NextGraph module was used to capture the correlations of consistent sequences (CNS). The preliminary genome was assembled based on the correlation of CNS. To improve the accuracy of the assembly, the contigs were refined with Racon (https://github.com/isovic/racon) using ONT long reads and was further calibrated with Illumina short-read sequences using NextPolish with default parameters. These steps yielded a polished assembly of 2.64 Gb, containing 214 contigs with a contig N50 length of 71.69 Mb. The maximum length of contig was 152,065,544 bp (Table 1).

Table 1.

Summary statics of the polished assemble genome.

Stat Type Contig Length (bp) Contig Number
N50 71686117 14
N60 60700258 18
N70 44835672 23
N80 36200085 30
N90 18335753 40
Longest 152065544 1
Total 2642822877 214
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Chromosome-level assembly of argali genome was conducted using Hi-C technology. The filtered Hi-C reads were aligned to the contig assembly using bowtie2 (v2.3.2)17 (-end-to-end–very-sensitive -L 30). Only uniquely mapped and valid paired-end reads were used for the scaffolding. The contigs were further clustered, ordered, and oriented onto chromosomes by LACHESIS18 with following parameters: CLUSTER_MIN_RE_SITES = 100, CLUSTER_MAX_LINK_DENSITY = 2.5, CLUSTER NONINFORMATIVE RATIO = 1.4, ORDER MIN N RES IN TRUNK = 60, ORDER MIN N RES IN SHREDS = 60. The placement and orientation errors exhibiting obvious discrete chromatin interaction patterns were manually adjusted in Juicebox (v1.11.08)19. Finally, a total of 2,639.94 Mb of the contig-level assembled sequences (99.90%) were anchored and orientated onto 28 pseudo-chromosomes, which was consistent with the karyotype of previous study (2n  = 56)20. The chromosome length ranging from 43.50 to 279.70 Mb (Table 2) and the contact maps were visualized using HiCExplorer v0.6.63621 (Fig. 1).

Table 2.

Summary of the assembled chromosomes of the Tibetan argali.

Chromosome Size (bp) Contig Number
LG01 279695706 13
LG02 227251094 9
LG03 143049907 36
LG04 137765606 1
LG05 121784806 2
LG06 118988766 1
LG07 113360613 7
LG08 108759658 5
LG09 101212747 3
LG10 95369036 1
LG11 92263955 4
LG12 88277149 8
LG13 83637784 4
LG14 82747032 3
LG15 80442138 3
LG16 73471412 4
LG17 72268625 1
LG18 70418982 6
LG19 66207289 11
LG20 62529082 1
LG21 62444233 3
LG22 60700258 1
LG23 52241053 3
LG24 51706622 1
LG25 51152309 5
LG26 44924779 3
LG27 44835672 1
LG28 43499549 4
Total 2631005862 144
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Fig. 1.

Fig. 1

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Heat map of intra-chromosome interactions analyzed by Hi-C data. (a) A photograph of Tibetan argali taken from Amubalebaxikan Mountain Pass in Altun Mountain National Nature Reserve. Photo credit: Jun-sheng Gong. (b) interaction density was quantified based on the number of supporting Hi-C reads and represented by a color bar, where dark red indicates high density and light yellow indicates low density. LG01 to LG28 means the chromosome number with length decreasing.

Quality assessment of the genome assembly

The quality of the final chromosome-level genome assembly was assessed using three distinct approaches. Firstly, we assessed the completeness and quality of the assembly by leveraging conserved genes from the benchmarking universal single-copy orthologs (BUSCO v4.0.522) database (-l mammalia_odb10 -g genome). This analysis revealed that 96.01% of BUSCO groups (8,858 out of 9,226) were identified as complete, with 93.19% being complete and single-copy (8,598), and 2.82% being complete and duplicated (260) (Table 3). The high percentage of complete BUSCOs underscores the remarkable completeness of our assembled genome.

Table 3.

BUSCO assessment of gemome assembly and annotation.

Type Assembly Annotation
Number Percent Number Percent
Complete BUSCOs 8858 96.01 8742 94.75
Complete and single-copy BUSCOs 8598 93.19 8573 92.92
Complete and duplicated BUSCOs 260 2.82 169 1.83
Fragmented BUSCOs 107 1.16 79 0.86
Missing BUSCOs 261 2.83 405 4.39
Total BUSCO groups searched 9226 100 9226 100
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Secondly, sequencing data could be analyzed for GC bias and sample contamination23,24. To evaluate the GC contents and sequencing depth with 10 Kb windows, we mapped the ONT long reads onto the assembled genome using minimap225 software (-x map-ont). The result showed that almost all GC points were consistently around 40% (Fig. 2), indicating no exogenous species pollution was found.

Fig. 2.

Fig. 2

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GC content and depth distribution. The horizontal axis represents the percentage of GC content, and the vertical axis represents the average sequencing depth.

Finally, to evaluate the base accuracy of the assembled sequences, we aligned the Illumina DNA short reads of same individual to the assembled genome using BWA-MEM (v0.7.15)26 with default parameters. The number of homozygous single-nucleotide variants (SNVs) and the number of insertions and deletions (Indels) that exhibited distinct nucleotide differences from the assembled genome were counted by SAMtools v1.927 and BCFtools v1.928 with different coverage depth of data. These analyses yielded a high sequence identity exceeding 99.9995% (depth > = 5X, Table 4), demonstrating the high accuracy of the assembled genome. Taken together, these comprehensive evaluations consistently demonstrate the high completeness and accuracy of our assembled genome.

Table 4.

Base accuracy rate estimated from different depth of Illumina short-reads. SNV: single-nucleotide variants. Indel: insertions and deletions.

Depth(X) Homozygous SNV Error rate by homozygous SNP (%) Homozygous Indel Error rate by homozygous Indel (%) Error rate by homozygous variants (%) Accuracy of genome (%)
depth >  = 1x 9097 0.000344 7579 0.000287 0.000631 99.999369
depth >  = 5x 7667 0.00029 5743 0.000217 0.000507 99.999493
depth >  = 10x 6877 0.00026 4074 0.000154 0.000414 99.999586
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Repetitive and non-coding gene prediction

The repeat sequences in the argali genome were annotated by using a combination of ab initio and homology-based approaches. For ab initio prediction, we firstly employed RepeatModeler v1.0.84029 to construct a de novo repeat sequence database. Subsequently, RepeatMasker v1.3233730 was utilized to identify both known and de novo repeats by querying the established database. For homologous sequence prediction, we leveraged RepeatMasker and RepeatProteinMask, which both within the RepeatMasker package to predict homology sequences against known repeat sequences in the RepBase database31. Tandem Repeats Finder (TRF) v4.07b4132 and GMATA v2.233 with default parameters were used to find tandem repeat sequences within the genome. Ultimately, we identified approximately 1.23 Gb repeat sequences, accounting for 46.38% of the assembled genome. Among these, long terminal repeats (LTRs) (42.76%) accounted for the highest proportion of the assembly, followed by long interspersed nuclear elements (LINE) (28.86%), short interspersed nuclear elements (SINE) (8.83%), and DNA elements (2.63%) (Table 5).

Table 5.

Summary statistics of the repeat annotation.

Repeat Type Repeat Number Repeat Length (bp) Percent (%)
LTR 622013 133825708 5.06
SINE 1643498 233342367 8.83
LINE 1868483 762758727 28.86
DNA 605785 69626678 2.63
RC 18647 996461 0.04
MITE 11733 3740836 0.14
Tandem Repeats 306902 10867883 0.41
Unknown 86373 10448248 0.4
Total 5163434 1225606908 46.37
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Non-coding RNA (ncRNA) were identified through database searches and model-based predictions. Specifically, Transfer RNAs (tRNAs) were predicted using tRNAscan-SE34 with eukaryotic parameters (–thread 4 -E -I). MicroRNA, rRNA, small nuclear RNA, and small nucleolar RNA were detected using the cmscan function in Infernal35, by querying against the Rfam database36. The rRNAs and their subunits were predicted using RNAmmer37 (-S euk -m lsu,ssu,tsu -gff) (Table 6).

Table 6.

Summary statistics of non-coding RNA.

Type Number Average length (bp) Total length (bp) Percent (%)
rRNA 378 9141.73 125911 0.0049
miRNA 3082 499.34 354481 0.0134
tRNA 251576 73.22 18419618 0.697
Regulatory 543 66.62 36174 0.0014
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Gene prediction and functional annotation

Protein-coding genes were also predicted using a combination of homology- and ab initio-based strategies. For homology-based prediction, protein-coding sequences of six species were retrieved from the NCBI database for human (Homo sapiens, GCA_000001405.2838), mouse (Mus musculus, GCA_000001635.939), sheep (Ovis aries, GCA_002742125.140), goat (Capra hircus, GCA_001704415.241), pig (Sus scrofa, GCA_000003025.642), and cattle (Bos taurus, GCA_002263795.243). These protein sequences were aligned to the argali genome using TBLASTN v2.2.2644 with an E-value threshold of 1e-5. Proteins with multiple adjacent matches were linked to each other using genBlastA v1.0.445. The aligned sequences and query proteins were then filtered and processed by GeneWise v2.4.146 to identify spliced alignments. For ab initio prediction, Augustus v3.0.347 was utilized, with optimized parameters trained on 1,000 randomly selected homologous genes. Finally, all gene sets were integrated using custom Perl scripts to create a comprehensive and non-redundant gene set. These strategies predicted a total of 21,564 high-quality protein-coding genes, with an average gene length of 40,453.18 bp, an average coding length of 1,613.81 bp, and 9.04 coding exons per gene.

The functional annotation of the protein-coding genes was performed using BLAST44 by searching against various databases, including SwissProt48, KEGG (Kyoto Encyclopedia of Genes and Genomes)49, KOG (Eukaryotic Orthologous Groups of proteins)50, GO (Gene Ontology)51, and NR (Non-Redundant protein Database). The putative GO terms of genes were identified using the InterProScan program52 with default parameters. Functional annotation results were merged using the aforementioned methods. Ultimately, a total of 20,798, 14,680, 13,799, 15,064, and 21,050 genes were annotated in Swissprot, KEGG, KOG, GO, and NR, respectively, with 9,327 genes were being annotated across all five databases (Table 7).

Table 7.

Summary statistics of functional annotation by search against SwissProt, NR, KEGG, KOG and Gene Ontology (GO) database.

Database Number Percent (%)
Swissprot 20798 96.45
KEGG 14680 68.08
KOG 13799 63.99
GO 15064 69.86
NR 21050 97.62
Overlap 9327 44.10
Total 21149 98.08
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Population genomic sequencing of 32 argali samples

A total of 32 wild argali samples were collected from the corpses during the field survey conducted from 2015 to 2020 (Table 8), The detailed localities of these samples are presented in Table 8. Four subspecies were identified based on the geographic location and further confirmation from mitochondrial genome sequence data (as described in the following section). For each sample, genomic DNA was extracted using the QIAGEN® Genomic kit (Cat#13343, QIAGEN). The quality and integrity of the extracted DNA were evaluated by measuring the A260/A280 ratio and conducting agarose gel electrophoresis. Paired-end sequencing libraries with an insert size of 300 bp were constructed according to Illumina’s manufacturer’s instructions and sequenced on Illumina Novaseq6000 platform. After filtering out low-quality reads, short reads, and adapter sequences using fastp v0.12.43115, the clean data were mapped to the Tibetan argali genome using BWA-MEM v0.7.12-r103926 with default parameters. The average sequencing depth achieved was 23.74X, ranging from 9.20 to 35.70, with an average mappable ratio up to 97.19% (Table 9).

Table 8.

Localities of the wild argali samples.

Sample ID Geographic Location Longitude (East) Latitude (North) Tissue Subspeices
YP21052401 China: Xinjiang: Aketao: Bulunkou Township 74.7343 38.3114 missing Pamir
YP21052402 China: Xinjiang: Aketao: Bulunkou Township 74.5503 38.5013 missing Pamir
YP21052403 China: Xinjiang: Aketao: Bulunkou Township 74.5249 38.5015 missing Pamir
YP21052404 China: Xinjiang: Aketao: Bulunkou Township 74.5249 38.5015 missing Pamir
YP21052405 China: Xinjiang: Aketao: Bulunkou Township 74.5249 38.5015 missing Pamir
YP21052406 China: Xinjiang: Aketao: Bulunkou Township 74.5249 38.5015 missing Pamir
YP21052408 China: Xinjiang: Aketao: Bulunkou Township 74.4800 38.5096 missing Pamir
YP21052409 China: Xinjiang: Aketao: Bulunkou Township 74.4800 38.5096 missing Pamir
YP21052410 China: Xinjiang: Aketao: Bulunkou Township 74.5394 38.5325 missing Pamir
YP21052411 China: Xinjiang: Aketao: Bulunkou Township 74.6080 38.5539 missing Pamir
YP211006001 China: Xinjiang: Tacheng 86.8110 47.4373 missing Altai
YP220121001 China: Xinjiang: Altun Mountain 85.4287 37.2275 missing Tibetan
YP220121002 China: Xinjiang: Altun Mountain 85.4287 37.2275 missing Tibetan
YP220121003 China: Xinjiang: Altun Mountain 85.4287 37.2275 missing Tibetan
YP170622001 China: Xinjiang: Taxkorgan: Lubugaizi valley 74.8763 37.0371 skin Pamir
YP170703001 China: Xinjiang: Taxkorgan: Kuxibili valley 75.3883 37.3806 skin Pamir
YP170703002 China: Xinjiang: Taxkorgan: Kuxibili valley 75.3883 37.3806 skin Pamir
YP170704002 China: Xinjiang: Taxkorgan: Psililing valley 75.3109 37.2973 skin Pamir
YP170704003 China: Xinjiang: Taxkorgan: Psililing valley 75.3109 37.2973 skin Pamir
YP170704005 China: Xinjiang: Taxkorgan: Psililing valley 75.3109 37.2973 skin Pamir
YP170704006 China: Xinjiang: Taxkorgan: Psililing valley 75.3109 37.2973 ear Pamir
YP170708004 China: Xinjiang: Taxkorgan: Zankan valley 75.5100 37.2522 skin Pamir
YP170820003 China: Xinjiang: Taxkorgan: Psililing valley 75.3365 37.3229 skin Pamir
YP170820002 China: Xinjiang: Taxkorgan: Psililing valley 75.3365 37.3229 muscle Pamir
YP170821004 China: Xinjiang: Taxkorgan: Qialaqigu valley 74.9069 37.1253 ear Pamir
YP170824001 China: Xinjiang: Taxkorgan: Kuxibili valley 75.5471 37.1344 skin Pamir
YP170825001 China: Xinjiang: Taxkorgan: Qialaqigu valley 75.0205 37.1222 skin Pamir
YP170825002 China: Xinjiang: Taxkorgan: Kalajilega valley 75.3787 37.3808 skin Pamir
YP170825006 China: Xinjiang: Taxkorgan: Kuxibili valley 75.5414 37.1201 skin Pamir
YP150701001 China: Xinjiang: Mori: Mengluoke mountain 91.7167–91.8167 44.4167–44.6500 ear Gobi
YP2019101901 China: Xinjiang: Fuhai: Kekesen Mountain 86.4667–87.0000 47.4167–47.6667 muscle Altai
YP20210526097 China: Xinjiang: Fuhai: Kekesen Mountain 86.4667–87.0000 47.4167–47.6667 muscle Altai
NWIPB2019122101 (Reference genome) China: Qinghai: Haixi: Yeniugou 93.7417 35.8807 muscle Tibetan
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Table 9.

Details of the whole genomic resequencing data of the wild argali samples.

Sample ID Number of Reads Mappable Reads Mapping Ratio Number of Unmappable Reads Sequencing Depth
YP21052401 313666614 310221286 0.9890 3445328 17.5683
YP21052402 313323401 309771232 0.9887 3552169 17.5365
YP21052403 281115694 275911703 0.9815 5203991 15.6146
YP21052404 301291450 297935399 0.9889 3356051 16.8695
YP21052405 321610174 319998214 0.9950 1611960 18.1143
YP21052406 398096091 169869521 0.4267 228226570 22.7456
YP21052408 303499982 302156904 0.9956 1343078 17.1125
YP21052409 309226552 286451948 0.9263 22774604 16.1946
YP21052410 299065718 297552741 0.9949 1512977 16.8575
YP21052411 313183904 303371017 0.9687 9812887 17.1262
YP211006001 293337600 292826178 0.9983 511422 16.6494
YP220121001 246437516 230086249 0.9336 16351267 12.9921
YP220121002 304738593 301673313 0.9899 3065280 17.3413
YP220121003 295117316 293236852 0.9936 1880464 16.6090
YP170622001 550635545 549455270 0.9979 1180275 31.0855
YP170703001 582163875 580103115 0.9965 2060760 32.9496
YP170703002 567181327 566100855 0.9981 1080472 32.1440
YP170704002 565230969 563686098 0.9973 1544871 31.9132
YP170704003 543482835 542471341 0.9981 1011494 30.7123
YP170704005 625657094 624419145 0.9980 1237949 35.3886
YP170704006 497574712 496816486 0.9985 758226 28.1209
YP170708004 606078969 594588899 0.9810 11490070 33.7368
YP170820003 161044323 160510176 0.9967 534147 9.1039
YP170820002 588859018 585965477 0.9951 2893541 33.1517
YP170821004 568206233 566445281 0.9969 1760952 32.1007
YP170824001 561429602 558723183 0.9952 2706419 31.6257
YP170825001 539564868 536995101 0.9952 2569767 30.4005
YP170825002 570104442 569003208 0.9981 1101234 32.2970
YP170825006 524953030 522540962 0.9954 2412068 29.6060
YP150701001 297717278 297315999 0.9987 401279 16.9088
YP2019101901 335502108 334190774 0.9961 1311334 18.9418
YP20210526097 316745455 315835591 0.9971 909864 17.8889
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Mitochondrial genome assemblies and phylogeny

The mitochondrial genome sequence of each wild argali sample was assembled from Illumina short-reads utilizing NOVOplasty 2.453. The K-mer was set to 33, and a mitogenome of argali sheep (GenBank: KT781689.1) was served as the bait reference. To further confirm the reliability of the mitochondrial contig assemblies, BLAST searches were performed against the bait reference. The sequences were then aligned using MUSCLE v3.8.3154, implemented in MEGA v7.0.1455. Protein-coding genes were translated into amino acid sequences to ensure an open reading frame and mitigate the amplification of NUMTs (nuclear mitochondrial DNA segments), which are often characterized by multiple stop codons within their sequences.

To identify the subspecies of samples, we additionally included 55 mitochondrial genome sequence of Ovis to our mitogenome dataset. We generated the mitochondrial alignment using MEGA 7.055. Indels and poorly aligned regions were excluded using standard settings in Gblocks v0.91b56. The aligned genomes were subsequently partitioned into protein-coding genes, noncoding fragments, rRNAs, and tRNAs. PartitionFinder v2.1.6 was employed to assess the optimal partitioning scheme and determine the most suitable substitution models for each partition, utilizing the corrected AIC (AICc)57. Maximum Likelihood (ML) analysis was conducted in RAxML v.8.1.1558 with 1,000 bootstrap replications, following the best partition scheme (Stamatakis 2014). The resulting mitochondrial phylogeny was presented in Fig. 3. The monophyly of four subspecies, namely Pamir, Kyzylkum, Tian Shan, and Tibet, was supported. Interestingly, the Gobi subspecies clustered with Altai, forming three distinct clades in the mitochondrial tree, partially consistent with recent research based on mitochondrial genome sequences59.

Fig. 3.

Fig. 3

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Maximum-likelihood tree based on mitochondrial genome sequences. Bootstraps values are indicated on the branches. Color bars refer to the subspecies groups. Individuals in red represent the individual of reference genome assembly in this study.

Data Records

The raw sequence data reported in this paper has been deposited in the Genome Sequence Archive under the project accession number CRA01750360. The Tibetan argali genome assembly was deposited in Genome Warehouse under accession number GWHETSG00000000.161 and GenBank database with the accession number GCA_045269445.162. The genomic annotation data have been uploaded to Figshare63. Population genomic variants have been deposited in European Nucleotide Archive (ENA) with the accession number PRJEB8299564.

Technical Validation

We employed the BUSCO assessment (Table 3), GC-Depth analysis (Fig. 2), and sequence identity (Table 4) as metrics to comprehensively assess the quality of our genome assembly. We didn’t find clear species pollution, and the estimated genomic completeness was 96.01%, accompanied by a high base accuracy rate of 99.9995%, demonstrating a highly comprehensive and accurate assembly. The Hi-C heatmap (Fig. 1B) provided further validation, revealing a clear and organized pattern of interaction contacts along the diagonals, both within and surrounding the chromosome inversion region, thereby indirectly confirming the accuracy of our chromosome assembly. Genomic annotation was also evaluated by BUSCO assessment, yielded 8,742 complete genes out of 9,226 BUSCO groups, representing 94.75% completeness (Table 3). The identity of population samples was verified by mitogenomic sequences and phylogeny, and the accuracy of mitogenome assemblies were manually checked by translating the protein-coding genes into amino acid sequences to ensure an open reading frame and no NUMT (nuclear mitochondrial DNA segments) sequences were assembled.

Acknowledgements

This project was funded by the Western Young Scholar Program-B of the Chinese Academy of Sciences (2021-XBQNXZ-014), the Natural Science Foundation of China (No. 32101408, No. 32470548), the Third Xinjiang Scientific Expedition (2022xjkk0205), The Joint Research Project of Sanjiangyuan National Park (LHZX-2022-03), Key Project of Qinghai Science & Technology Department (2024-SF-102), Yunnan Province (202305AH340006). This work was also supported by the Animal Branch of the Germplasm Bank of Wild Species, Chinese Academy of Sciences (the Large Research Infrastructure Funding).

Author contributions

Conception and study design: W.Y. and Q.Y.; Sample collection: M.W., X.L., K.Z., Q.Y. and W.Y.; Data analysis: B.L.Z. and Q.L.; Drafting the manuscript: M.W., B.L.Z. and Q.L.

Code availability

No specific custom codes were developed in this study. All commands and pipelines used for data analyses were conducted according to the manuals or protocols provided by the corresponding software development team, which are described in detail in the Methods section. Default parameters were employed if no detailed parameters were mentioned for the software used in this study.

Competing interests

Q.L. is an employee of Beijing Bio Huaxing Gene Technology Co., LTD. The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Mu-Yang Wang, Bao-Lin Zhang, Qi-Qi Liang.

Contributor Information

Qi-En Yang, Email: yangqien@nwipb.cas.cn.

Wei-Kang Yang, Email: yangwk@ms.xjb.ac.cn.

References

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Zhang, B. L. Chromosome-level genome assembly and annotation of Tibetan argali. Figshare10.6084/m9.figshare.26143207.v4 (2024).
  2. European Nucleotide Archivehttps://www.ebi.ac.uk/ena/browser/view/PRJEB82995, https://identifiers.org/ena.embl:ERP166660 (2024).

Data Availability Statement

No specific custom codes were developed in this study. All commands and pipelines used for data analyses were conducted according to the manuals or protocols provided by the corresponding software development team, which are described in detail in the Methods section. Default parameters were employed if no detailed parameters were mentioned for the software used in this study.

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