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. 2024 Jul 11;11:762. doi: 10.1038/s41597-024-03581-w

A near complete genome assembly of the East Friesian sheep genome

Xiaoxiao You 1,#, Qinyuan Fang 1,#, Chunhai Chen 2,#, Junwei Cao 3, Shaoyin Fu 4, Teng Zhang 1, Shenyuan Wang 3, Xiaolong He 4, Jiangfeng He 4, Yang Zhou 1, Biao Wang 4, Liwei Wang 4, Zheng Wang 1, Tianhao Sun 1, Xukui Yang 2, Rigele Te 4, Jianbo Jian 2, Huanmin Zhou 1,3,✉, Yanfeng Dai 1,✉, Yongbin Liu 1,4,✉
PMCID: PMC11239650  PMID: 38992134

Abstract

Advancements in sequencing have enabled the assembly of numerous sheep genomes, significantly advancing our understanding of the link between genetic variation and phenotypic traits. However, the genome of East Friesian sheep (Ostfriesisches Milchschaf), a key high-yield milk breed, remains to be fully assembled. Here, we constructed a near-complete and gap-free East Friesian genome assembly using PacBio HiFi, ultra-long ONT and Hi-C sequencing. The resulting genome assembly spans approximately 2.96 Gb, with a contig N50 length of 104.1 Mb and only 164 unplaced sequences. Remarkably, our assembly has captured 41 telomeres and 24 centromeres. The assembled sequence is of high quality on completeness (BUSCO score: 97.1%) and correctness (QV: 69.1). In addition, a total of 24,580 protein-coding genes were predicted, of which 97.2% (23,891) carried at least one conserved functional domain. Collectively, this assembly provides not only a near T2T gap-free genome, but also provides a valuable genetic resource for comparative genome studies of sheep and will serve as an important tool for the sheep research community.

Subject terms: Structural variation, Zoology

Background & Summary

Selective breeding for different agricultural purposes, such as meat, wool, and milk, have established many sheep breeds with unique characteristics worldwide1. The East Friesian sheep (Ostfriesisches Milchschaf) is a highly specialized breed. The breed originates from the Frisia region of both the Netherlands and Germany, and is considered to be the world’s highest producing dairy sheep2,3. In a single lactation, the East Friesian sheep can produce 500–700 kg of milk over a period of approximately 230 days4. Additionally, East Friesian sheep have a relatively high average number of lambs per ewe, 2.25 lambs/litter, but the carcass of lambs is very lean5. In physical appearance, East Friesian sheep have many unique features. They have a relatively large body, head, face, legs, ears all clean of wool. Their most distinctive physical feature is a “rat-tail” which is thin and devoid of wool. The East Friesian sheep, renowned for its adaptability, has been successfully crossbred with breeds known for their robust ketone body composition, such as Suffolk, Dorset, and Texel. This strategic crossbreeding not only enhances the meat quality of the East Friesian sheep but also ameliorates the traits of breeds that exhibit lower milk yields and suboptimal reproductive and lambing capabilities. Hailing from the northern regions of Germany and the Friesland area in the Netherlands, the East Friesian breed has garnered international attention and has been integrated into the livestock industries of various countries, including China, the United Kingdom, and South Africa. The exploration of the breed’s genetic makeup at the molecular level presents a compelling opportunity to deepen our comprehension of the genetic underpinnings of economically significant traits in sheep, thereby contributing to the advancement of the field.

De novo genome assembly is a fundamental and powerful tool employed in the realm of molecular research. Several genomes of sheep genomes have been made publicly available in databases, including East Friesian sheep6, Tibetan sheep7, Rambouillet sheep8, and Texel sheep9. Despite the achievement of chromosome-level assembly in these sheep genomes, there still exist unidentified regions containing gaps that require further investigation and determination. A number of assemblers have been developed for long reads assembly, such as Falcon10, Flye11, Canu12, wtdbg213, NextDenovo14 and Hifiasm15. The Hifiasm method stands out for its utilization of string-overlap graphs to represent genomes, encode information for algorithmic analysis, and visually present both primary and alternative paths along a DNA sequence16. New developments in long-read sequencing technologies, such as Pacific Biosciences (PacBio) circular consensus (CCS) long-read sequencing and ultra-long ONT sequencing, has revolutionized our ability to acquire comprehensive chromosome sequences spanning from one telomere to another. With the availability of a complete genome sequence, researchers would have the opportunity to thoroughly investigate and gain a deeper understanding of genome function, regulation, and evolution17,18.

In this study, we present the first near T2T gap-free genome assembly for East Friesian sheep using a combination of PacBio high-fidelity (HiFi) long-read, Oxford Nanopore (ONT) ultra long-read, and high-throughput chromosome conformation capture (Hi-C) sequencing data. In total, we generated 321 Gb (~107X coverage) ONT reads with a N50 of 63.5 kb, 148 Gb PacBio HiFi CCS reads with a N50 of 22.1 kb (~49X coverage), and 396 Gb Hi-C data (MGISEQ paired-end reads, ~132X coverage) (Table 1). The final genome assembly of East Friesian sheep, termed as EFS v2.0, is about 2.96 Gb with a scaffold N50 of 104.10 Mb, comprising 27 chromosomes without any gaps (Table 2; Fig. 1) and 164 unplaced sequences. We observed that 94.53% of these unplaced sequences consist of repetitive elements, among which satellite sequences constitute 84.64%. Further research and refinement are needed to determine their precise genomic location and functional relevance. The EFS v2.0 assembly captured 41 telomeres and 24 centromeres (Table 3). Notably, the EFS v2.0 assembly closed 35 gaps in total compared to the previously published East Friesian genome6 (Fig. 2).

Table 1.

Summary of sequencing data of East Friesian sheep genome.

Flow cell number Reads Number Total length (GB) Genome depth N50 length of reads (bp)
Raw ultra-long ONT data 3 8,180,779 321 107 63,509
Clean ultra-long ONT data (>=80 kb) 3 1,017,473 108 36 103,117
Error corrected ONT data (>=80 kb) 3 1,004,276 105 35 101,717
PacBio subreads data 5 113,316,791 2,207 736 22,376
PacBio CCS data 5 6,914,516 148.3 49 22,086
Raw Hi-C data — 2,641,764,362 396 132 150
Clean Hi-C data — 2,631,885,258 395 132 150
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Table 2.

Comparison of four sheep genomes.

East Friesian (EFS v2.0) East Friesian (GCA_018804185.1) Tibetan sheep (GCA_017524585.1) Rambouillet (GCA_016772045.1) Texel (GCA_000298735.2)
Genome size (Gb) 2.96 2.90 2.65 2.63 2.62
Number of scaffolds 191 937 58 142 5,466
N50 of scaffolds (bp) 104,103,076 96,203,338 105,184,753 101,274,418 100,009,711
Chromosome-scale scaffolds (bp) 2,726,314,016 (92.2%) 2,664,530,299 (91.8%) 2,649,881,505 2,615,649,360 2,584,815,894
Number of contigs 191 972 168 226 49,782
N50 of contigs (bp) 104,103,076 85,264,699 74,601,179 43,178,051 144,057
Number of Gap 0 35 110 84 44,566
Protein-coding number 24,580 not available 20,688 21,257 20,545
GC content of the genome 43.9% 43.6% 41.9% 42.0% 41.5%
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Fig. 1.

Fig. 1

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Circos plot of the EFS v2.0 genome. From inside to outside, I: GC content in nonoverlapping 1 Mb windows (histograms); II: percent coverage of repetitive sequences in nonoverlapping 1 Mb windows (heat maps); III: gene density calculated based on the number of genes in nonoverlapping 1 Mb windows (heat maps); IV: 27 super-scaffolds. Lengths are shown in Mb.

Table 3.

Centromere positions of East Friesian sheep genome.

Chromosomes ID Chromosome length Centromere start Centromere end
Chr01 287,587,234 123,580,000 128,830,000
Chr02 258,028,803 113,470,000 120,150,000
Chr03 230,046,731 107,030,000 108,840,000
Chr04 125,767,300 1 2,700,000
Chr05 110,109,836 1 1,340,000
Chr06 123,671,404 1 3,560,000
Chr07 106,996,842 20,000 3,430,000
Chr08 94,674,297 170,000 2,690,000
Chr09 104,103,076 1 5,560,000
Chr10 88,865,759 1 530,000
Chr11 63,898,494 340,000 770,000
Chr12 81,322,787 — —
Chr13 91,530,625 980,000 7,160,000
Chr14 67,246,060 — —
Chr15 85,768,580 1,170,000 2,590,000
Chr16 73,547,930 30,000 1,270,000
Chr17 73,961,413 140,000 620,000
Chr18 79,180,959 720,000 6,400,000
Chr19 62,167,297 670,000 1,590,000
Chr20 55,174,739 440,000 2,400,000
Chr21 51,749,166 100,000 670,000
Chr22 56,726,796 280,000 3,260,000
Chr23 64,680,290 1 1,430,000
Chr24 48,344,355 630,000 3,410,000
Chr25 45,498,135 — —
Chr26 48,284,804 1 3,170,000
ChrX 147,380,304 7,220,000 9,310,000
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Fig. 2.

Fig. 2

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Overview of the near T2T and gap-free EFS v2.0 reference genome. The box represents the 35 closed gaps identified from GCA_018804185.1. The triangle represents the telomere region, and the circle represents the centromere region.

In the EFS v2.0 genome, repeat sequences accounted for 1.60 Gb, representing 53.98% of the assembly (Table 4). Long interspersed nuclear elements (LINE) retrotransposons (41.46%) were the most abundant component among repetitive elements, which was consistent with a previous study19 (Table 5). Gene annotation identified 24,580 protein-coding genes. Of which, 24,536 genes (99.8%) were anchored to 27 chromosomes (Fig. 1), while 44 genes anchored to unplaced scaffolds. The length and number of exons were similar to those of three other sheep breeds (Fig. 3a,b). Furthermore, the predicted proteins achieved a complete BUSCO score of approximately 98%, indicating high quality annotation (Fig. 3c). 23,891 (97.2%) protein-coding genes were successfully annotated in diverse databases, including Gene Ontology (GO), KOG, Interpro, SwissProt20, Kyoto Encyclopedia of Genes and Genomes (KEGG)21, NCBI nonredundant database (NR), and Translation of European Molecular Biology Laboratory (Trembl) (Table 6). Moreover, 17,328 (~70.5%) genes were supported by all five databases (Fig. 3d). Based on transcriptomic deep-sequencing data, we investigated gene expression level in five different tissues (Table 7). A total of 15,263 (62.2%) genes showed detectable expression levels (transcripts per million ≥ 1) in one or more of these tissues. Through structural variants analysis with the previously published East Friesian sheep6, we identified 232 newly assembled genes, among which 151 were expressed in 5 different transcriptome samples (Table 8; Fig. 4).

Table 4.

General statistics of repeats in the EFS v2.0 assembly.

Type Repeat Size % of genome
Tandem repeats 121,015,125 4.09
Interspersed repeats
Repeatmasker 965,987,732 32.67
Proteinmask 555,689,821 18.80
De novo 1,460,536,811 49.40
Total 1,596,071,511 53.98
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Note: Some elements may partially overlap with another element domain.

Table 5.

Transposable elements (TEs) in the assembled EFS v2.0 assembly.

Type Repbase TEs TE protiens De novo Combined TEs
Length (Bp) % in genome Length (Bp) % in genome Length (Bp) % in genome Length (Bp) % in genome
DNA 26,247,775 0.89 2,137,907 0.07 271,885 0.01 26,477,799 0.90
LINE 748,843,603 25.33 546,714,112 18.49 1,125,752,351 38.08 1,225,790,468 41.46
SINE 133,041,949 4.50 0 0.00 563,878 0.02 133,585,032 4.52
LTR 76,620,857 2.59 6,905,477 0.23 127,898,467 4.33 196,780,336 6.66
Other 233 0.00 0 0.00 0 0.00 233 0.00
Unknown 0 0.00 0 0.00 700,079 0.02 700,079 0.02
Total 965,987,732 32.67 555,689,821 18.80 1,199,035,229 40.56 1,300,301,311 43.98
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Note: This statistical table does not contain Tandem Repeats, some elements may partly include another element domain.

*Combined: the non-redundant consensus of all repeat prediction/classification methods employed.

†Unknown: the predicted repeats that cannot be classified by RepeatMasker;

LINE, long interspersed nuclear elements; SINE, short interspersed nuclear elements; LTR, long terminal repeat.

Fig. 3.

Fig. 3

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Quality assessment of the protein-coding genes in the EFS v2.0 assembly. (a) Comparison of exon length among four sheep gene sets. Window refers to the length of every point. (b) Comparison of exon number among four sheep gene sets. No obvious unexpected differences exist among these four organisms, indicating the high quality of gene structure annotation. (c) BUSCO assessment results of protein-coding genes in the EFS v2.0 assembly. (d) Gene function annotation results in a statistics Venn diagram using five public databases: NR, InterPro, KEGG, SwissProt and KOG.

Table 6.

Number of functional annotations for predicted genes in the EFS v2.0 assembly.

Type Gene number Percentage
Total 24,580 100%
Nr 23,664 96.27%
Swissprot 21,954 89.32%
KEGG 20,577 83.71%
KOG 18,046 73.42%
TrEMBL 23,755 96.64%
Interpro All 22,002 89.51%
GO 16,578 67.45%
Annotated 23,891 97.20%
Unannotated 689 2.80%
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Table 7.

Summary of RNA-seq sequencing data of East Friesian sheep genome.

Sample Raw reads Raw bases Clean reads Clean bases Q20 (%) Q30 (%)
Lun_g 65,206,128 9,780,919,200 61,666,246 9,249,936,900 97.86 92.96
Rum_n 74,652,518 11,197,877,700 70,628,290 10,594,243,500 98.02 93.47
Sub_t 77,938,068 11,690,710,200 63,021,986 9,453,297,900 98.04 93.57
Per_t 69,699,584 10,454,937,600 66,091,478 9,913,721,700 97.95 93.25
Hea_t 66,270,030 9,940,504,500 62,984,582 9,447,687,300 97.94 93.19
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Note: “Hea_t” represents heart, “Rum_n” represents rumen, “Sub_t” represents subcutaneous fat, “Lun_g” represents lung, and “Per_t” represents perirenal fat.

Table 8.

The improvement of EFS v2.0 assembly.

Chromosomes EFS v2.0 Length (bp) GCA_018804185.1 Length (bp) EFS v2.0 gap numbers GCA_018804185.1 gap numbers EFS v2.0 Gene number EFS v2.0 New assembled genes
1 287,587,234 284,533,122 0 5 2,411 19
2 258,028,803 254,299,457 0 2 1,792 15
3 230,046,731 229,824,859 0 1 2,400 24
4 125,767,300 121,425,072 0 0 847 9
5 110,109,836 108,581,918 0 0 1,380 5
6 123,671,404 118,750,793 0 1 705 16
7 106,996,842 104,409,019 0 3 1,034 11
8 94,674,297 91,893,355 0 0 552 8
9 104,103,076 96,203,338 0 0 564 8
10 88,865,759 88,811,564 0 5 438 6
11 63,898,494 63,197,725 0 1 1,304 7
12 81,322,787 81,831,065 0 0 727 7
13 91,530,625 85,264,699 0 0 852 5
14 67,246,060 67,917,403 0 1 1,379 13
15 85,768,580 85,099,227 0 0 1,036 12
16 73,547,930 73,688,838 0 5 383 5
17 73,961,413 74,056,358 0 2 624 5
18 79,180,959 73,405,259 0 0 624 6
19 62,167,297 60,536,646 0 0 589 4
20 55,174,739 52,767,035 0 0 800 6
21 51,749,166 52,149,183 0 2 714 4
22 56,726,796 51,710,764 0 0 434 4
23 64,680,290 62,896,507 0 0 359 4
24 48,344,355 45,761,874 0 0 798 4
25 45,498,135 45,186,384 0 2 334 3
26 48,284,804 45,990,704 0 0 264 6
X 147,380,304 144,338,131 0 5 1,192 16
Total 2,726,314,016 2,664,530,299 0 35 24,536 232
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Fig. 4.

Fig. 4

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Heatmap representation of new assembled genes. Rows represent new assembled genes, and columns represent 5 different samples. The bar in the upper right corner represents log 2 transformed TPM values. Blue and red boxes represent genes showing lower and higher expression levels, respectively. “Hea_t” represents heart, “Rum_n” represents rumen, “Sub_t” represents subcutaneous fat, “Lun_g” represents lung, and “Per_t” represents perirenal fat.

Methods

Sample collection, DNA preparation and RNA extractions

A 1-year-old female East Friesian sheep from Inner Mongolia key Lab of Bio-manufacture in Inner Mongolia autonomous region of China was chosen for DNA and RNA sequencing. The assembled sequence does not include the Y chromosome due to sampling from females. The animal was healthy, and no genetic defects were observed in it or its parents.

DNA was extracted from fresh blood specimen using the QIAGEN Blood & Cell Culture DNA Midi Kit according to the manufacturer’s instruction (QIAGEN, Germany). TRIzol (Invitrogen, Carlsbad, CA, United States) was used to extract total RNA from heart, rumen, subcutaneous fat, lung and perirenal fat tissues. The concentration of total RNA was determined using the Nano 6000 spectrophotometer Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, United States). The RNA purity was determined using the Qubit® RNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, Camarillo, CA, United States).

Long insert libraries preparation and sequencing

The library construction and sequencing of RNA-seq full-length transcripts were conducted using a method similar to that described in Yuan, Ge et al.22, resulting in 437,807 full-length non-chimeric reads with mean length of 1,388 bp.

For the DNA PacBio long inserts libraries, the preparation was carried out in accordance with the “Using SMRTbell Express Template Prep Kit 2.0 With Low DNA Input” protocol23 provided by PacBio (Pacific Biosciences, USA). This resulted in libraries with an insert size of approximately 20 kb. Subsequently, the libraries were subjected to sequencing using PacBio Sequel II platforms operating in CCS mode. The subreads were processed through the CCS algorithm of SMRTLink (v8.0.0)24 with specific parameters: “-minPasses 3 -minPredictedAccuracy 0.99 -minLength 500”, yielding 148 Gb of PacBio’s long high-fidelity (HiFi) reads in total.

Furthermore, ultra-long DNA ONT libraries were created following the protocols detailed by Shafin et al.25. These libraries were then sequenced on the PromethION sequencer platform (Oxford Nanopore Technologies, UK). The sequencing effort resulted in the production of 8,180,779 reads, with an N50 value of 63,509 bp.

Short insert libraries preparation and sequencing

RNA-seq libraries were prepared uisng the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, Ipswich, MA, USA) following the manufacturer’s protocol. Subsequently, the RNA libraries were sequenced on a MGISEQ-2000 platform, producing 150 bp paired-end reads.

The Hi-C library was prepared using the same method described in Yin, Chen et al.26 with the same blood specimen and sequenced on a MGISEQ-2000 instrument. A total of 395 Gb of clean data were obtained from 396 Gb of sequencing data using software SOAPnuke (v2.0)27 with parameters “-n 0.01 -l 20 -q 0.1 -i -Q 2 -G 2 -M 2 -A 0.5”.

Genome assembly

With the HiFi reads, the primary contigs were assembled using Hifiasm (v 0.16.1)15 with default parameters. The Hi-C valid reads were employed to anchor contigs onto chromosomes through Juicer28 and 3d-dna pipeline29. The chromosome nomenclature was adopted for the chromosome numbering on the basis of their collinearity with 27 chromosomes of Texel sheep genome30. To achieve a near T2T gap-free reference genome assembly, gaps in the assembly genome were filled using LR_Gapcloser31 with error-corrected ONT long reads produced by NECAT32.

Annotation of repetitive sequences and identification of telomeres and centromeres

Two strategies, de novo and homolog methods, were applied to annotate repetitive sequences. RepeatModeler (v1.0.4)33 was used to identify de novo repeats, and LTR-FINDER (v1.0.7)34 was utilized to annotate long terminal repeats. DNA and protein transposable elements (TEs) were detected by RepeatMasker (v4.0.7)35 and RepeatProteinMasker (v4.0.7), respectively, based on Repbase database36. At last, tandem repeats were identified using Tandem Repeat Finder (v4.10.0)37. Referencing the methods described in sweet tea38, wild blueberry39, and rapeseed studies40, the telomeric sequences in the EFS v2.0 genome assembly were identified using quarTeT (v1.0.3)41 with the “-c animal” option. The quarTeT program comprises four modules: AssemblyMapper, GapFiller, TeloExplorer, and CentroMiner, of which, TeloExplorer is specifically utilized for identifying candidate telomeres. The telomere repeat monomer identified by quarTeT was “TTAGGG/CCCTAA”. The Centromics software (https://github.com/ShuaiNIEgithub/Centromics) was employed for centromere identification. Furthermore, we aligned the human rRNA sequences against the EFS v2.0 genome by using BLASTN with an E value of 1 × 10−5 to investigate the ribosomal DNA (rDNA) in EFS v2.0 genome. In total, we identified 1329 ribosomal RNAs (rRNAs), including 122 18S rRNA, 586 28S RNA, 59 5.8S rRNA, and 562 5S RNA.

Protein-coding genes prediction and functional annotation

Protein-coding genes in the EFS v2.0 assembly were predicted using a similar method to Fang, Mou et al.42. For the RNA-seq-based prediction approach, clean RNA-seq reads were aligned to the EFS v2.0 assembly via Hisat2 (v2.1.0)43 with parameters including–sensitive–no-discordant–no-mixed -I 1 -X 1000–max-intronlen 1000000. Additionally, 437,807 full-length transcripts were matched against the EFS v2.0 assembly using GMAP (v 2017-11-15)44. The aligned reads were then assembled using Stringtie (v1.3.5)45 using the parameters: -f 0.3 -j 3 -c 5 -g 100 -s 10000. Subsequently, TransDecoder (v5.5.0) (https://github.com/TransDecoder/TransDecoder) was invoked to identify the coding sequence with default settings. In the homolog prediction method, GeMoMa (v1.9)46 was used to detect homologous peptides across four mammalian genomes, including human (GCA_000001405.29)47, Texel sheep (GCA_000298735.2)9, Tibetan sheep (GCA_017524585.1)7, and Rambouillet sheep (GCA_016772045.1)8. Genes that had RNA-seq-based prediction support with correct structure, but were not identified via homology-based prediction, were incorporated into the gene set. Ultimately, untranslated regions and alternative splicing regions were determined using Program to Assemble Spliced Alignment48. The integrated gene set was translated into amino-acid sequences and functionally annotated by mapping against KEGG49, Swiss-Prot20, TrEMBL20, KOG50, InterPro51 and NR (NCBI Non-redundant protein) databases using BLAST (v2.2.26)52 with an E-value threshold of 1E-5. Protein domains and motifs were annotated using InterProScan53, from which GO Ontology (GO)54 was derived.

Gene expression analysis

Quality control of raw RNA-seq reads was conducted using SOAPnuke (v2.0)15. Afterwards, the clean reads were aligned to the EFS v2.0 genome using Hisat2 (v2.1.0)43, with the following parameters: ‘--phred33 -p 5 --sensitive --no-discordant --no-mixed -I 1 -X 1000’. A read count matrix was generated using featureCounts55. Gene expression levels were calculated using the transcripts per million (TPM) method.

Identification of new assembled genes

The software Syri (v1.6.3)56 was employed to detect structural variations between the EFS v2.0 genome assembly and the previously published East Friesian sheep6. A gene was classified as newly assembled if the previously published East Friesian sheep6 exhibited a deletion of at least 50 bp and the gene region had a minimum overlap of 30% with that region.

Reads coverage analysis of genome assembly

We assessed whether the long sequencing reads extended across the regions that required gap filling. Prior to this process, the genome contained eight gaps. We employed minimap257 (v 2.24) to map both the ONT and HiFi reads to the EFS v2.0 genome. Utilizing SAMtools58 (v 1.10) with the ‘-q 20’ option, we filtered out low-quality and multi-mapping reads. Subsequently, we utilized the IGV software for visualizing the high-quality alignment results.

Quality value (QV) calculations

In the realm of whole-genome sequencing, the Quality Value (QV) emerges as an essential metric for gauging the precision of nucleotide identification. The QV is derived from the Phred quality score, a measure that captures the negative logarithm of the likelihood that a given base call is erroneous. The QV is precisely calculated through the equation QV = −10 × log10 (error probability). For instance, an error probability of 0.001 equates to a QV of 30, indicating a high confidence in the correctness of the base call. Throughout the sequencing process, each nucleotide is appraised with a Phred score that is contingent upon the signal-to-noise ratio; this score is subsequently converted to a QV, thereby providing an index of the sequencing data’s fidelity. In this study, we have employed the Merqury59 software to meticulously compute the QV, ensuring robust data quality assessment.

Data Records

The DNA sequence reads of East Friesian sheep (Experiment of DNA sequencing data from ultra-long ONT library: SRR2627375660; Experiments of DNA sequencing data from Hi-C library: SRR2627376360; Experiments of DNA sequencing data from PacBio HiFi library: SRR2627376260) and RNA sequence reads of East Friesian sheep (Experiment of 5 transcriptome libraries: SRR26273757-SRR2627376160) have been deposited in the Sequence Read Archive (SRA). The genome assembly have been deposited in the GenBank database under the accession number JAWMPZ00000000061. The files of the gene structure annotation, repeat predictions and gene functional annotation have been deposited at Figshare database62.

Technical Validation

Multiple methods were employed to validate the accuracy and completeness of EFS v2.0 assembly.

Firstly, we utilized long sequencing reads to ascertain their extension across the eight gap regions (Table 9). The resulting plots confirmed comprehensive coverage of the targeted regions (Fig. 5). Secondly, the Hi-C heatmap displayed high consistency across all chromosomes, demonstrating the correct ordering and orientation of contigs in the EFS v2.0 assembly (Fig. 6). Thirdly, the EFS v2.0 assembly exhibited high collinearity with Rambouillet sheep (GCA_016772045.1)8, Tibetan sheep (GCA_017524585.1)7 and the previously published East Friesian sheep (GCA_018804185.1)6 (Fig. 7). Fourthly, the accuracy was confirmed by the high mapping rates of two type sequences on the EFS v2.0 assembly, with 99.93% of ONT reads and 100% of HiFi reads aligning to the EFS v2.0 assembly. Notably, the sequencing assembly attained a remarkable quality value (QV) score of 69.1, signifying an exceptionally low error rate of approximately 1.26 errors per 100 million bases. This level of sequencing accuracy and reliability is highly commendable and will undoubtedly facilitate subsequent genetic analysis and research. Lastly, the Benchmarking Universal Single-Copy Orthologs (BUSCO) test revealed that EFS v2.0 assembly successfully identified 97.1% of 9,226 mammalia gene sets, which exhibiting the highest level of BUSCO completeness among the four commonly used genomes (Fig. 8).

Table 9.

The location of the gap to be filled.

Chromosome ID Start End
Chr01 269,427 269,926
Chr01 391,274 391,773
Chr11 43,820,237 43,846,318
Chr11 63,558,411 63,558,809
Chr11 63,647,361 63,647,859
Chr20 33,593,787 33,599,607
Chr24 41,820,199 41,820,522
Chr25 2,393,086 2,394,781
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Fig. 5.

Fig. 5

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Using IGV to demonstrate the coverage of ONT and PacBio reads in the gap 1 region. The IGV images for Gap 1 through Gap 8 are available through the Figshare database62.

Fig. 6.

Fig. 6

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The accuracy and completeness of the EFS v2.0 genome assembly. Whole-genome Hi-C heatmap of EFS v2.0 within and between 27 chromosomes.

Fig. 7.

Fig. 7

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The identification of syntenic regions for EFS v2.0, Rambouillet sheep and Tibetan sheep was based on conducting homology searches using MCScan (Python version)63, with a minimum requirement of 30 genes per block. Macrosynteny connecting blocks of >30 one-to-one gene pairs are shown.

Fig. 8.

Fig. 8

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BUSCO plot of the several sheep genomes. C: Complete BUSCOs; S: Complete and single-copy BUSCOs; D: Complete and duplicated BUSCOs; F: Fragmented BUSCOs; M: Missing BUSCOs; n: Total BUSCO groups searched. East Friesian sheepa: GCA_018804185.1; East Friesian sheepb: EFS v2.0.

Acknowledgements

This work was funded by the Inner Mongolia Autonomous Region Open Competition Projects (2022JBGS0024), China Agriculture Research System (CARS-38) and National Key Research and Development Program Projects (2021YFD1300903).

Author contributions

Y.F.D. and H.M.Z. conceived the study. Y.B.L. was responsible for the implemented the study. Q.Y.F., S.Y.W., X.L.H., J.F.H., B.W., L.W.W and R.T. collected the samples. Q.Y.F., J.W.C., S.Y.W., Z.W, X.K.Y., T.H.S. and J.B.J. processed the samples. X.X.Y., Q.Y.F. and C.H.C. processed bioinformatics data. X.X.Y., S.Y.F., T.Z. and Y.Z. performed bioinformatics analysis. X.X.Y. and C.H.C. wrote the manuscript with significant contributions. All authors read and approved the final manuscript.

Code availability

No specific code was developed for this study. The data analyses were conducted following the manuals and protocols provided by the developers of the relevant bioinformatics tools, which are described in the Methods section along with the versions used.

Competing interests

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: Xiaoxiao You, Qinyuan Fang, Chunhai Chen.

Change history

8/29/2024

A Correction to this paper has been published: 10.1038/s41597-024-03755-6

Contributor Information

Huanmin Zhou, Email: huanminzhou@263.net.

Yanfeng Dai, Email: daiyf@imu.edu.cn.

Yongbin Liu, Email: ybliu117@126.com.

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Associated Data

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

Data Citations

  1. NCBI Genbank.https://identifiers.org/ncbi/insdc.gca:GCA_000298735.2 (2015).
  2. NCBI Genbank.https://identifiers.org/ncbi/insdc.gca:GCA_000001405.29 (2022).
  3. NCBI Sequence Read Archivehttps://identifiers.org/ncbi/insdc.sra:SRP464403 (2023).
  4. Liu, Y. Ovis aries breed East Friesian sheep isolate LY-2023, whole genome shotgun sequencing project. GenBankhttps://identifiers.org/ncbi/insdc.gca:GCA_033439445.1 (2023).
  5. Liu, Y. B. Annotation Files of East Friesian sheep. figshare10.6084/m9.figshare.24208689.v3 (2023). 10.6084/m9.figshare.24208689.v3 [DOI]

Data Availability Statement

No specific code was developed for this study. The data analyses were conducted following the manuals and protocols provided by the developers of the relevant bioinformatics tools, which are described in the Methods section along with the versions used.

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

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