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. 2025 Jul 5;12:1147. doi: 10.1038/s41597-025-05477-9

Chromosome-level genome assembly of Ceroplastes pseudoceriferus Green, 1935 (Hemiptera: Coccidae)

Yaoguang Qin 1,✉,#, Zhennan Wang 2,#, Jiangni Li 1,✉
PMCID: PMC12227621  PMID: 40615450

Abstract

Soft scales (Hemiptera: Coccidae) are significant polyphagous pests and majority of which are invasive species. The 364.14 Mb chromosome-level genome of Ceroplastes pseudoceriferus was assembled in this work, with a contig N50 length of 6.16 Mb and scafold N50 length of 21.24 Mb. Approximately 99.89% of assembled sequences were anchored into 18 chromosomes with the assistance of Hi-C reads. Furthermore, approximately 53.98% of the genome was composed of repetitive elements. In total, 10,475 protein-coding genes were predicted, of which 9503 (90.72%) genes were functionally annotated. The BUSCO analysis demonstrated the completeness of the genome annotation is 92.54%. This genome represents first high-quality chromosome level assembly of Coccidae, thereby advancing our knowledge of Coccidae insects and developing effective management strategies that protect crops, forests, and natural ecosystems.

Subject terms: Genome, Sequencing

Background & Summary

Soft scales (Coccidae) is the third largest family within Subfamily Coccoidea behind Diaspididae (armoured scales) and Pseudococcidae (mealybugs)1–3. The majority of Coccidae species constitute significant polyphagous pests, posing a threat to economically vital agricultural and horticultural crops, as well as ornamental plants4. The inherent polyphagous characteristics and broad host plants of Coccidae species facilitate their widespread invasion globally. Furthermore, Coccidae insects play a pivotal role in ecosystem dynamics through complex interaction with predatory insects, parasitoids and entomopathogens5. Understanding the biology, behavior, and ecology of Coccidae insects is crucial for developing effective management strategies that protect crops, forests, and natural ecosystems. Until now, six genomes of Coccidae species are available from GenBank (Table 1), and only one scaffold-level genome has been published as research paper6. The scarcity of genomic resources poses a significant obstacle to intensive research endeavors aimed at advancing our knowledge of Coccidae insects.

Table 1.

Assembly features for genomes of Ceroplastes pseudoceriferus and other Coccidae species.

Organism Name Assembly Stats Total Sequence Length Assembly Level Assembly Stats Total Number of Chromosomes Assembly Stats Contig N50 Assembly Stats Scaffold N50 Assembly Stats Number of Scaffolds
Ceroplastes pseudoceriferus 364,527,212 Chromosome 18 6,164,612 21,238,502 78
Coccus hesperidum 405,400,678 Chromosome 7 8,466,229 54,289,485 41
Toumeyella liriodendri 536,180,826 Chromosome 17 1,303,606 30,207,406 366
Ericerus pela 654,859,808 Scaffold 410,234 1,243,548 2396
Coccus hesperidum 484,566,906 Scaffold 399,496 44,350,661 6006
Parthenolecanium corni 231,203,063 Contig 14,265,238
Ericerus pela 660,374,735 Contig 660,240
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Ceroplastes pseudoceriferus, first described by Green in 1935, is a highly polyphagous species of Coccidae. This species could infest a diverse array of plant species, encompassing 91 genera belonging to 56 plant families1. This broad host range underscores potential of C. pseudoceriferus as an invasive species. A high-quality reference genome of C. pseudoceriferus would be invaluable in enhancing our comprehension of ecological implications of Coccidae and facilitating the development of effective management strategies. In this study, we generated 35.11 Gb of MGI short-read sequencing and 102.21 Gb of Oxford Nanopore Technologies long-read sequencing, 77.20 Gb of high-throughput chromosome conformation capture (Hi-C). The final genome assembly size was 364.52 Mb with an N50 of 6.25 Mb. The karyotype of Ceroplastes pseudoceriferus is characterized by 18 chromosomes at the chromosome level. This conclusion is directly supported by the Hi-C scaffolding results in the genome assembly, where 99.89% of the assembled sequences (364.14 Mb) were anchored into 18 distinct chromosomes with an N50 of 21.24 Mb (Fig. 1).

Fig. 1.

Fig. 1

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Left: Overview of assembled C. pseudoceriferus genome.The outer layer of coloured blocks is a circular representation of the 18 chromosomes and circos demonstration of gene count (histogram), repeat density (heatmap) and GC content (line) from the outer to the inner circle, respectively. Right: The heatmap represents 18 chromosomes of the C. pseudoceriferus genome.

The assembly achieved an L50 value of 9, indicating that 50% of the total genome length (excluding ambiguous bases) is contained within the 9 longest scaffolds. This demonstrates high continuity in the assembly. The calculated auN value of 20,385,303.53 bp reflects a robust representation of long contiguous sequences, suggesting that the majority of the genome is assembled into large, uninterrupted segments. A total of 60 gaps were identified in the assembly, corresponding to regions of undetermined sequence (represented by runs of ‘N’ bases). This low gap count underscores the assembly’s high completeness. The assembly contains 6,000 ambiguous bases (N), accounting for <0.01% of the total genome length. Such minimal unresolved regions further validate the assembly’s reliability for downstream analyses.

We totally identified 998,369 repeat elements and the total length is 196,755,237 bp, accounting for 53.98% of whole sequence (Table 2). The total length of 23,595 tandem repeats (TR) is 1,374,491 bp, of which length of 10,466 simple sequence repeats (SSRs) is 123,925 bp. The annotation of transposable elements (TE) identified 873,421 repeat elements and the length is 179,535,462 bp, comprising 49.25% of the total. The annotation of non-coding RNAs (ncRNAs) totally resulted in 31 rRNA, 75 small RNA, 109 regulatory and 204 tRNA (Table 3). The gene prediction yielded a total of 10,475 genes, with an average gene length of 20,039 bp, an average CDS length of 1,701 bp, an average exons number per gene at 9, an average exon length of 199 bp, and an average intron length of 2,431 bp (Table 4).

Table 2.

Repetitive elements sequence statistics of the assembled genome.

Type Number of elements Length of sequence (bp) Percentage of sequence (%)
TEs Class I LINE L2 4,919 746,213 0.20
Unknown 43,018 7,569,999 2.08
I-Jockey 3,377 529,766 0.15
RTE-RTE 3,253 859,132 0.24
R1 1,414 733,018 0.20
Other 4,310 459,257 0.13
LTR Unknown 42,629 7,267,293 1.99
Gypsy 8,329 1,639,811 0.45
Pao 3,007 1,311,568 0.36
Copia 7,480 2,085,659 0.57
Other 3,372 385,307 0.11
SINE Unknown 8,093 666,156 0.18
tRNA-Core-RTE 1,795 429,843 0.12
Other 2,191 621,833 0.17
Class II DNA Kolobok-T2 5,320 428,734 0.12
TcMar-Tc1 5,396 982,730 0.27
Unknown 571,953 118,603,002 32.54
Maverick 6,470 1,382,554 0.38
hAT-Ac 29,650 4,379,726 1.20
Crypton-I 6,606 949,425 0.26
CMC-Chapaev-3 1,962 813,420 0.22
CMC-EnSpm 2,602 2,788,889 0.77
TcMar-Fot1 1,893 514,523 0.14
Academ-1 1,404 469,109 0.13
TcMar-Mariner 6,415 1,991,663 0.55
Other 19,541 2,482,897 0.68
MITE Unknown 57,951 14,793,825 4.06
RC Helitron 19,071 3,650,110 1.00
Total TEs 873,421 179,535,462 49.25
Tandem Repeats tandem_repeat 13,129 1,250,566 0.34
SSR 10,466 123,925 0.03
Total 23,595 1,374,491 0.38
Simple repeats 2,243 340,996 0.09
Unknown 97,246 15,186,412 4.17
Other 1,759 303,452 0.08
Low complexity 105 14,424 0.00
Total Repeats 998,369 196,755,237 53.98
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Table 3.

Statistics of annotated non-coding RNAs.

Type Copy Number Average Length(bp) Total Length(bp) Percentage of sequence(%)
rRNA (31) 18S 6 2,669.17 16,015 0.0044
28S 6 6,343.00 38,058 0.0104
5.8S 6 158 948 0.0003
5S 13 116.85 1,519 0.0004
small RNA (75) snRNA 19 113.11 2,149 0.0006
miRNA 23 80.65 1,855 0.0005
spliceosomal 25 140.12 3,503 0.001
other 8 247 1,976 0.0005
Regulatory cis-regulatory elements 109 48.61 5,298 0.0015
tRNA tRNA 204 75.35 15,372 0.0042
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Table 4.

Statistical results of gene structure prediction.

Gene set Total number of genes Average gene length(bp) Average CDS length(bp) Average exons number per gene Average exon length(bp) Average intron length(bp)
Transcriptome NGS RNA seq 15,964 30,114.73 3,481.19 9.79 355.63 3,030.40
PASA 15,637 29,593.23 3,477.10 9.75 356.7 2,985.39
Homoloy C. lectularius 15,795 52,434.27 1,417.84 7.72 183.62 7,589.76
A. pisum 25,118 35,660.83 1,328.27 5.84 227.47 7,094.62
N. lugens 27,001 38,850.77 1,323.46 5.67 233.35 8,033.07
H. halys 20,824 44,447.92 1,351.53 6.63 203.79 7,651.91
D. melanogaster 17,764 28,949.25 1,156.09 5.13 225.37 6,729.98
GeMoMa 11,353 29,039.92 1,443.02 7.13 202.33 4,500.38
De novo AUGUSTUS 12,124 19,075.14 1,665.97 8.42 197.75 2,344.77
Final EVM 10,475 20,038.57 1,700.51 8.54 199.03 2,430.89
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The completeness of the predicted genes was assessed using Benchmarking Universal Single-Copy Orthologs (BUSCO)7, resulting in a high score of 92.54% (n = 1,265) (Fig. 2). This encompassed 85.15% (1164) single-copy, 7.39% (101) duplicated, 0.73% (10) fragmented, and 6.73% (92) missing BUSCOs, indicating a high integrity of gene prediction. Functional annotation indicated that a total of 90.72% (9503) of genes were annotated to five public databases including SwissProt8, the NCBI non-reduntant protein database (NR), Kyoto Encyclopedia of Gene and Genomes (KEGG)9, Eukaryotic Orthologous Groups of protein (KOG)10 and Gene Ontology (GO)11 (Fig. 3).

Fig. 2.

Fig. 2

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BUSCO evaluation on the genome assembly completeness of the assembled genome.

Fig. 3.

Fig. 3

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Venn diagram of function annotations from various databases. The Venn diagram displays the overlap and uniqueness of functional gene annotations derived from five databases: SwissProt, Non-Reduntant Protein Database (NR), Kyoto Encyclopedia of Gene and Genomes (KEGG), Eukaryotic Orthologous Groups of protein (KOG) and Gene Ontology (GO).

Method

Sample collection, library construction and sequencing

First-instar larva of C. pseudoceriferus were collected from a single Camphor tree in Kunming, Yunnan Province, China (25°02′11″N, 102°42′31″E). Specimens were flash-frozen in liquid nitrogen and stored at −80 °C. Thousands of whole-body samples were used for DNA and RNA extraction to ensure sufficient biological material for multi-omics sequencing, including MGI short-read sequencing, Nanopore long-read sequencing, Hi-C sequencing and RNA sequencing. Genomic DNA (gDNA) was extracted using a QIAGEN® Genomic DNA Kit (Cat#13323). Total RNA was extracted using TRIzol® (TIANGEN) with DNase I treatment. All DNA and RNA extractions were conducted by Grandomics Biosciences (Wuhan, China) following manufacturer recommended protocols, meeting the quality specifications for library preparation and sequencing requirements.

For MGI sequencing, gDNA was randomly fragmented and libraries were prepared using standard protocols. After quality checks, the libraries were sequenced on the MGI DNBSEQ-T7RS platform, producing 35.11 Gb of raw data with 100 × average coverage.

For Nanopore sequencing12, size-selected DNA fragments were processed using the PippinHT system (Sage Science). Following end-repair and adapter ligation (SQK-LSK114 kit), library concentrations were quantified with a Qubit fluorometer. The prepared library was sequenced on a PromethION platform (Oxford Nanopore Technologies), yielding 102.21 Gb of long-read data at 287 × coverage for genome assembly.

For Hi-C sequencing, whole body samples were formaldehyde-fixed (2%) to crosslink DNA-protein complexes. Libraries were constructed through chromatin digestion, proximity ligation, and biotin pulldown following the protocol13. Sequencing on the MGISEQ-2000 platform generated 77.20 Gb of 150 bp paired-end reads (217 × coverage), enabling 3D chromatin architecture analysis.

For RNA sequencing, the Poly A mRNA was enriched via Dynabeads, fragmented, and reverse-transcribed using MGIEasy RNA Library Prep Kit V3.1 (Cat# 1000005276, MGI). Libraries were circularized and sequenced on DNBSEQ-T7RS, generating 13.45 Gb data for gene annotation.

Genome assembly

The raw Nanopore reads underwent de novo genome assembly using NextDenovo (v2.5.2)14 with default parameters. Four iterative correction rounds of Nextpolish (v1.2.4)15 polishing were subsequently applied using MGI short reads to enhance base accuracy. Assembly quality was verified through mapping all MGI reads to the assembled genome using BWA (Burrows-Wheeler Aligner, v0.7.12-r1039)16 and coverage assessment using Winnowmap217 with parameters of “-x map-ont”. Base-level accuracy was calculated using samtools (v1.4)18 and BCFtools (v1.8.0)19 with default parameters. Mitochondrial sequence exclusion was performed via BLAST (v2.9)20 alignment against the NT database, followed by contaminant removal.

Chromosome anchoring

The 370 million paired-end reads underwent quality control through the standardized Hi-C data processing workflow21. Low-quality sequences (quality scores <20), adaptor sequences and sequences shorter than 30 bp were filtered out using fastp (v0.21.0)22. Clean reads were mapped using bowtie2 (v2.3.2)23 (-end-to-end–very-sensitive -L 30). HiC-Pro (v2.8.1)21 retained valid chromatin contacts while filtering invalid read pairs, including dangling-end, self-cycle, re-ligation, and dumped products. Scaffolds were organized into chromosomes using LACHESIS24, with key 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. Final chromosomal orientations were manually verified and corrected based on chromatin interaction patterns.

Repeat annotation

Tandem repeats were systematically annotated through complementary approaches: simple sequence repeats (SSRs) were detected using GMATA (v2.2)25 with default parameters, while genome-wide tandem repeats were identified via Tandem Repeats Finder (TRF V4.07b)26 using sensitivity thresholds of “2 7 7 80 10 50 500 -f -d -h -r”. Transposable elements (TE) annotation was combinig ab initio and homology-based methods. ab initio repeat library was predicted using MITE-hunter27 with parameters of “-n 20 -P 0.2 -c 3” and RepeatModeler version open-2.0.428 with parameters of “-engine wublast”, in which long terminal repeats (LTRs) were characterized using LTR_FINDER29, LTRharvest30 and LTR_retriever31. The obtained library underwent cross referencing against TEclass Repbase32 (http://www.girinst.org/repbase) for TE classification. Genome-wide TE masking was subsequently performed using RepeatMasker33 which simultaneously screened sequences against both the de novo library and RepBase reference. Overlapping TE annotations were resolved through BEDTools-based merging followed by manual curation to validate complex repeat architectures.

Annotation of non-coding RNAs (ncRNAs)

Non-coding RNA identification employed complementary database alignment and model-based prediction approaches. Transfer RNA genes were systematically predicted through tRNAscan-SE (v2.0)34 using eukaryotic configuration parameters. For comprehensive detection of small regulatory RNAs (miRNAs, snRNAs, snoRNAs) and ribosomal RNAs, a dual-strategy framework was implemented: Infernal (v1.1.2)35 facilitated covariance model searches against the Rfam Database36, while RNAmmer (v1.2)37 provided specialized prediction of rRNA subunits through hidden Markov model profiling. This integrated annotation pipeline ensured cross-validated identification of non-coding RNA elements across multiple functional categories.

Gene prediction

Gene annotation integrated three complementary methodologies applied to the repeat-masked genome. Homology-based predictions were generated through GeMoMa (v1.6.1)38, which mapped conserved protein sequences from five phylogenetically relevant species (Acyrthosiphon pisum39, Cimex lectularius40, Drosophila melanogaster41, Halyomorpha halys42 and Nilaparvata lugens43) onto the assembly. Concurrently, transcriptomic evidence was incorporated through RNA-seq alignment using STAR-2.7.3a44, followed by transcript assembly with StringTie245 and ORF prediction via PASA (v2.3.3)46. For the ab initio prediction, RNA-seq-derived training sets processed through StringTie and PASA informed Augustus (v3.3.1)47 predictions. The three prediction streams were reconciled through EVidenceModeler (EVM)46, creating a unified gene set subsequently refined through TransposonPSI48 filtration to remove transposon associated genes. Structural annotation completeness was enhanced through PASA-based identification of untranslated regions (UTRs) and alternative splicing variants, with canonical isoforms selected based on maximum ORF length. Final gene models retained only protein-coding sequences validated through evolutionary conservation, transcriptional evidence, and ab initio support, ensuring both structural accuracy and biological relevance.

Functional annotation of gene

Gene functional characterization was conducted through systematic interrogation of five biological databases using complementary bioinformatics approaches. Protein domain architecture and Gene Ontology (GO) term assignments were determined via InterProScan49 analysis with default parameterization. Parallel BLASTp searches (E-value threshold ≤ 1e–5) against four major databases - SwissProt (manually curated proteins), NR (non-redundant sequences), KEGG (metabolic pathways), and KOG (eukaryotic ortholog clusters) - were performed on the EvidenceModeler-derived proteome, retaining only top-scoring alignments for each query sequence. The consensus functional annotations derived from these five independent evidence streams (InterProScan domain predictions and four BLAST-based database matches) were subsequently integrated through hierarchical evidence weighting, prioritizing experimentally validated annotations from SwissProt while incorporating complementary functional predictions from other resources. This multi-tiered annotation framework ensured comprehensive functional insights spanning molecular interactions, biological processes, and evolutionary relationships.

Data Records

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive50 in National Genomics Data Center51, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA01941552) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. The whole genome sequence data and the annotation files for genome, ncRNA and repeat content are available from the Figshare repository53–56. The whole genome sequence are also deposited in the National Center for Biotechnology Information (NCBI) GenBank, with accession number GCA_050872025.157, under BioProject PRJNA1273070.

Technical Validation

High mapping quality were confirmed in our results. After assembling ONT reads, BWA was used to align MGI short-reads with the assembled genome, resulted in mapping rate of 98.70%. The coverage of the genome by MGI short-reads is 99.72%. Winnowmap2 was used to align ONT reads with the assembled genome, resulted in mapping rate of 98.65%, and achieving a genome coverage of 99.99%. The combination of parthenogenetic tendencies in Coccidae, single-host sampling, and high assembly quality strongly suggests that these first-instar larvae had low genetic diversity, making them suitable for producing a coherent reference genome.

Acknowledgements

This research was supported by National Natural Science Foundation of China (32100363), the Fundamental Research Funds for the Central public welfare research institutes (L2024008) and China Academy of Chinese Medical Sciences (Z2024032).

Author contributions

Yaoguang Qin conceived and designed the study; Yaoguang Qin and Zhennan Wang conducted molecular works; Yaoguang Qin, Zhennan Wang and Jiangni Li discussed the results; Yaoguang Qin and Zhennan Wang analyzed the data and wrote the manuscript.

Code availability

All software used in this study is in the public domain and its parameters are clearly described in the methodology. If the detailed parameters of the software are not mentioned, the default parameters are used as recommended by the developer. No custom code or scripts were used for the curation and validation of the dataset.

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: Yaoguang Qin, Zhennan Wang.

Contributor Information

Yaoguang Qin, Email: qinyaoguang@lfnu.edu.cn.

Jiangni Li, Email: lijiangni@lfnu.edu.cn.

References

  • 1.Morales, M. G. et al. ScaleNet: a literature-based model of scale insect biology and systematics. Database2016, bav118 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Wang, X. B. et al. DNA barcoding of common soft scales (Hemiptera: Coccoidea: Coccidae) in China. Bull. Entomol. Res.105(5), 545–554, 10.1017/S0007485315000413 (2015). [DOI] [PubMed] [Google Scholar]
  • 3.Choi, J., Soysouvanh, P., Lee, S. & Hong, K. J. Review of the family Coccidae (Hemiptera: Coccomorpha) in Laos. Zootaxa4460(1), 1–62, 10.11646/zootaxa (2018). [DOI] [PubMed] [Google Scholar]
  • 4.Bendov, Y. A systematic catalogue of the soft scale insects of the world (Homoptera: Coccoidea: Coccidae) with data on geographical distribution, host plants, biology and economic importance. Annals of the Entomological Society of America86(6), 841–841(1) (1993). [Google Scholar]
  • 5.Becchimanzi, A., Nicoletti, R., Di Lelio, I. & Russo, E. Immune Gene Repertoire of Soft Scale Insects (Hemiptera: Coccidae). Int. J. Mol. Sci.25, 4922, 10.3390/ijms25094922 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Yang, P. et al. Genome sequence of the Chinese white wax scale insect Ericerus pela: the first draft genome for the Coccidae family of scale insects. Gigascience.8(9), giz113, 10.1093/gigascience/giz113 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Simão, F. A. et al. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics.31(19), 3210–3212, 10.1093/bioinformatics/btv351 (2015). [DOI] [PubMed] [Google Scholar]
  • 8.Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic Acids Res.27, 49–54, 10.1093/nar/27.1.49 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res.27, 29–34, 10.1093/nar/27.1.29 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Galperin, M. Y. et al. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res.43, D261–D269, 10.1093/nar/gku1223 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet.25, 25–29, 10.1038/75556 (2000). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Shang, L. et al. A super pan-genomic landscape of rice. Cell Res.32, 878–896, 10.1038/s41422-022-00685-z (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Rao, S. S. P. et al. A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell159, 1665–1680, 10.1016/j.cell.2014.11.021 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hu, J. et al. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biol.25, 107, 10.1186/s13059-024-03252-4 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Hu, J. et al. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics36, 2253–2255, 10.1093/bioinformatics/btz891 (2020). [DOI] [PubMed] [Google Scholar]
  • 16.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics26, 589–595, 10.1093/bioinformatics/btp698 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Jain, C. et al. Long-read mapping to repetitive reference sequences using Winnowmap2. Nat Methods19, 705–710, 10.1038/s41592-022-01457-8 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics25, 2078–2079, 10.1093/bioinformatics/btp352 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Danecek, P. & McCarthy, S. A. BCFtools/csq: haplotype-aware variant consequences. Bioinformatics33, 2037–2039, 10.1093/bioinformatics/btx100 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chaisson, M. J. & Tesler, G. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinformatics13, 238, 10.1186/1471-2105-13-238 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol.16, 259, 10.1186/s13059-015-0831-x (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chen, S. et al. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics34, i884–i890, 10.1093/bioinformatics/bty560 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods9, 357–359, 10.1038/nmeth.1923 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol.31, 1119–1125, 10.1038/nbt.2727 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Wang, X. & Wang, L. GMATA: An Integrated Software Package for Genome-Scale SSR Mining, Marker Development and Viewing. Front. Plant Sci.7, 1350, 10.3389/fpls.2016.01350 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res.27, 573–580, 10.1093/nar/27.2.573 (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Han, Y. & Wessler, S. R. MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Res.38, e199–e199, 10.1093/nar/gkq862 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proceedings of the National Academy of Sciences117, 9451–9457, 10.1073/pnas.1921046117 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res.35, W265–W268, 10.1093/nar/gkm286 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Ellinghaus, D., Kurtz, S. & Willhoef, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinformatics9, 18, 10.1186/1471-2105-9-18 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ou, S. & Jiang, N. LTR_retriever: A Highly Accurate and Sensitive Program for Identification of Long Terminal Repeat Retrotransposons. Plant Physiol.176, 1410–1422, 10.1104/pp.17.01310 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Abrusán, G., Grundmann, N., DeMester, L. & Makalowski, W. TEclass—a tool for automated classification of unknown eukaryotic transposable elements. Bioinformatics25, 1329–1330, 10.1093/bioinformatics/btp084 (2009). [DOI] [PubMed] [Google Scholar]
  • 33.Bedell, J. A., Korf, I. & Gish, W. MaskerAid: a performance enhancement to RepeatMasker. Bioinformatics16, 1040–1041, 10.1093/bioinformatics/16.11.1040 (2000). [DOI] [PubMed] [Google Scholar]
  • 34.Chan, P. P. et al. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res.49, 9077–9096, 10.1093/nar/gkab688 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics29, 2933–2935, 10.1093/bioinformatics/btt509 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Grifths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res.33, D121–D124, 10.1093/nar/gki081 (2005). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res.35, 3100–3108, 10.1093/nar/gkm160 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Keilwagen, J. et al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res.44, e89–e89, 10.1093/nar/gkw092 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.International Aphid Genomics Consortium. Genome sequence of the pea aphid Acyrthosiphon pisum. PLoS biology.8(2), e1000313, 10.1371/journal.pbio.1000313 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Rosenfeld, J. et al. Genome assembly and geospatial phylogenomics of the bed bug Cimex lectularius. Nat. Commun.7, 10164, 10.1038/ncomms10164 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Adams, M. D. et al. The genome sequence of Drosophila melanogaster. Science.287(5461), 2185–2195, 10.1126/science.287.5461.2185 (2000). [DOI] [PubMed] [Google Scholar]
  • 42.Sparks, M. E. et al. Brown marmorated stink bug, Halyomorpha halys (Stål), genome: putative underpinnings of polyphagy, insecticide resistance potential and biology of a top worldwide pest. BMC Genomics.21(1), 227, 10.1186/s12864-020-6510-7 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ye, Y. X. et al. Chromosome-level assembly of the brown planthopper genome with a characterized Y chromosome. Mol. Ecol. Resour.21(4), 1287–1298, 10.1111/1755-0998.13328 (2021). [DOI] [PubMed] [Google Scholar]
  • 44.Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics29, 15–21, 10.1093/bioinformatics/bts635 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Bio.20, 278, 10.1186/s13059-019-1910-1 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol.9, R7, 10.1186/gb-2008-9-1-r7 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Stanke, M. et al. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics24, 637–644, 10.1093/bioinformatics/btn013 (2008). [DOI] [PubMed] [Google Scholar]
  • 48.Urasaki, N. et al. Draft genome sequence of bitter gourd (Momordica charantia), a vegetable and medicinal plant in tropical and subtropical regions. DNA Res.24(1), 51–58, 10.1093/dnares/dsw047 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Zdobnov, E. M. & Apweiler, R. InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics17(9), 847–848, 10.1093/bioinformatics/17.9.847 (2001). [DOI] [PubMed] [Google Scholar]
  • 50.The Genome Sequence Archive Family. Toward Explosive Data Growth and Diverse Data Types. Genomics, Proteomics & Bioinformatics19(4), 578–583, 10.1016/j.gpb.2021.08.001 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Database Resources of the National Genomics Data Center. China National Center for Bioinformation in 2022. Nucleic Acids Res.50(D1), D27–D38, 10.1093/nar/gkab951 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.NGDC Genome Sequence Archive.https://ngdc.cncb.ac.cn/gsa/browse/CRA019415 (2025).
  • 53.Qin, Y. G. Ceroplastes pseudoceriferus genome. Figshare10.6084/m9.figshare.27193908.v1 (2024).
  • 54.Qin, Y. G. Ceroplastes pseudoceriferus genome annotation. Figshare10.6084/m9.figshare.27193896.v1 (2024).
  • 55.Qin, Y. G. Ceroplastes pseudoceriferus noncoding annotation. Figshare10.6084/m9.figshare.27195201.v1 (2024).
  • 56.Qin, Y. G. Ceroplastes pseudoceriferus repeat content annotation. Figshare10.6084/m9.figshare.27193911.v1 (2024).
  • 57.NCBI GenBankhttps://identifiers.org/ncbi/insdc.gca:GCA_050872025.1 (2025).

Associated Data

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

Data Citations

  1. NGDC Genome Sequence Archive.https://ngdc.cncb.ac.cn/gsa/browse/CRA019415 (2025).
  2. Qin, Y. G. Ceroplastes pseudoceriferus genome. Figshare10.6084/m9.figshare.27193908.v1 (2024).
  3. Qin, Y. G. Ceroplastes pseudoceriferus genome annotation. Figshare10.6084/m9.figshare.27193896.v1 (2024).
  4. Qin, Y. G. Ceroplastes pseudoceriferus noncoding annotation. Figshare10.6084/m9.figshare.27195201.v1 (2024).
  5. Qin, Y. G. Ceroplastes pseudoceriferus repeat content annotation. Figshare10.6084/m9.figshare.27193911.v1 (2024).
  6. NCBI GenBankhttps://identifiers.org/ncbi/insdc.gca:GCA_050872025.1 (2025).

Data Availability Statement

All software used in this study is in the public domain and its parameters are clearly described in the methodology. If the detailed parameters of the software are not mentioned, the default parameters are used as recommended by the developer. No custom code or scripts were used for the curation and validation of the dataset.

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

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