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Animal & Plant Whole Genome Sequencing


Whole genome sequencing (WGS) provides the most comprehensive collection of the genetic variations in individuals of the same species or between related species. The variation information such as Single Nucleotide Polymorphism (SNP), Insertion and Deletion (InDel), Copy Number Variation (CNV), and structural variation (SV) obtained through WGS is used in population genetics research and genome-wide association studies (GWAS) to investigate the causes of diseases, to select plants and animals for agricultural breeding programs, and to identify common genetic variations among populations.

Service Specifications


  • Evolutionary and demographic history
  • Biodiversity
  • Crop development 
  • Conservation
  • Animal Health and Breeding
  • Natural selection


  • Extensive experience: We have completed numerous re-sequencing projects, and our data has been published in many noteworthy journals.
  • Unsurpassed data quality: We guarantee a Q30 score ≥ 80%, exceeding Illumina’s official guarantee of ≥ 75%. See our data example.
  • High verification rate: We promise that the verification rate of SNPs is higher than 95%.

Sample Requirements

Platform Type Sample Type Amount (Qubit®) Purity
Illumina Novaseq 6000
Genomic DNA ≥ 200 ng
Genomic DNA (PCR free) ≥ 1.5 μg
Genomic DNA from FFPE ≥ 0.8 μg
PacBio Sequel I/II HMW Genomic DNA ≥ 10 μg (for Sequel I)
≥30 μg (for Sequel II)
Fragments should be ≥ 30 Kb for Sequel I, ≥ 60 Kb for Sequel II
Nanopore PromethION HMW Genomic DNA ≥ 10 μg OD260/280=1.8-2.0;
Fragments should be ≥ 30 Kb

Sequencing Parameters and Analysis Contents

Platform Type Illumina Novaseq 6000 PacBio Sequel I/II Nanopore PromethION
Read Length Paired-end 150 bp average > 10 Kb for Sequel I
average > 15 Kb for Sequel II
average > 17 Kb
Recommended Sequencing Depth
For SNP/InDel detection: ≥ 10×
For SV detection: ≥ 20×
For SV detection: ≥ 20×
For SV/CNV detection: ≥ 20×
Standard Data Analysis
  • Standard Analysis
  • Data quality control: filtering reads containing adapter or with low quality
  • Alignment with reference genome, statistics of sequencing depth and coverage
  • Variant (SNP, InDel) calling, annotation and statistics
  • Advanced Analysis
  • SV calling, annotation and statistics
  • Advanced Analysis
  • SV calling, annotation and statistics
  • SNV calling, annotation and statistics
  • Note: For detailed information, please refer to the Service Specifications and contact us for customized requests.

    Project Workflow

    Sampling & Sequencing Strategy:

    • Sampling —— 58 samples of cultivated peaches and closely related relatives
    • Library Preparation —— ~350bp insert DNA library
    • Sequencing Strategy —— Illumina platforms, PE 150bp

    Results & Conclusion

    A. peach originated about 2.47 Mya in southwest China in glacial refugia generated by the uplift of the Tibetan plateau (Fig.1).


    Fig. 1 Speciation and demographic history of peach species.

    B. The copy number of four fruit texture or taste associated candidate genes(EXPA16, Pectin lyase-like, CAD9, and PMT5) increased in P. persica in the course of domestication and/or subsequent improvement (Fig.2).

    Fig. 2 CNVs involved in fruit texture and taste during peach domestication and improvement.

    C. The significant haplotype differentiation patterns were observed for several SNPs within these several candidate genes related to fruit size (CNR9 and CNR10) and skin color (NAC078 and TTG1) (Fig.3).

    Fig. 3 Stage-wise selection for fruit size and skin color during peach domestication and improvement.

    Conclusion: This study dramatically increases the amount of genomic data available for peach, provides valuable information for facilitating marker-assisted selection, and clarifies the evolutionary history of specific fruit traits in peach, offering a new evolutionary model that help us to understand the evolution of perennial fruit tree crops.

    Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield


    Upland cotton is the most widely cultivated species, and more than 90% of the global cotton production reflects characteristics of wide adaptability and high yield. Because fiber properties are much more imperative for the quality of spinning yarn, the ability to genetically manipulate fiber-related traits has become a pivotal goal for traditional breeding and biotechnology-assisted improvement. The genomic variation of diverse germplasms and alleles underpinning fiber quality and yield should be extensively explored. However, few studies on core collections together with phenotyping under multiple environments have been conducted on cotton.

    Sampling & Sequencing Strategy:

    • Sampling: 419 upland cotton accessions
    • Library Preparation: 350bp insert DNA library
    • Sequencing Strategy: Illumina platforms, PE 150bp

    Results & Conclusion

    A. A total 3,665,030 population SNPs were identified from 419 accessions. The 419 accessions were classified into three genetic groups, G1, G2, and G3. The decay rate of linkage disequilibrium (LD) was calculated as the pairwise correlation coefficient (r2) from the maximum value (0.46) to the half maximum at 742.7 kb, for all the accessions with variation among different genetic groups.

    B. A total of 11,026 (7,383 excluding the repeated among traits) SNP signals (P<10−6) were significantly associated with the 13 traits. Among these, 3,806 SNP signals for the traits were repeatedly observed in at least three types of data.

    C. 7,383 unique SNPs and 4,820 candidate genes were significantly associated with fiber quality and yield traits (Fig. 2).
    The results should credibly provide targets for molecular-marker selection and genetic manipulation of cotton improvement to meet the growing demand for renewable fiber. Further work will be necessary to validate more genes underlying the traits.

    Fig. 4 Phylogenetic tree, Pca, genetic structure and LD decay of the 419 accessions.

    Fig. 5 Comprehensive diagram illustrating the relationships among chromosomes, associated SNPs and genes, traits, fiber developmental stages and transcriptome analysis.

    Genetic variation in PTPN1 contributes to metabolic adaptation to high-altitude hypoxia in Tibetan migratory locusts


    Animal and human highlanders have evolved distinct traits to enhance tissue oxygen delivery and utilization. Revealing the mechanisms underlying organismal adaptation to high-altitude hypoxia is attracting considerable attention and can contribute to our understanding of hypoxia-featured human diseases, such as heart failure and various cancers. Unlike vertebrates, insects use their tracheal system for efficient oxygen delivery. However, the genetic basis of insect adaptation to high-altitude hypoxia remains unexplored.

    Sampling & Sequencing Strategy:

    • Sample ——24 migratory locusts from 8 localities
    • Library Preparation —— 150bp insert DNA library
    • Sequencing Strategy —— Illumina platforms, PE 150bp

    Results & Conclusion

    A. The 22 individuals represents two geographically distinct migratory locust populations in China. The Tibetan locusts were approximately 20% smaller in body size than lowland locusts, although they were taxonomically the same species (Fig.6).

    Fig. 6 Phylogenetics of migratory locust based on whole-genome SNPs.

    B. 484 genes in 113.8 Mb genomic regions were annotated positively selected gene (PSG) candidates. The expression analysis of PSGs results indicate that energy metabolism in lowland locusts is highly repressed by hypoxia, whereas Tibetan locusts evolved metabolic robustness against hypoxic stress(Fig.7).

    Fig. 7 Selective sweep and expression analysis of hypoxia adaptation.

    C. PTPN1 variants in Tibetan locusts were separated from the locusts in both South and North China lowlands. The PTPN1 point mutant in Tibetan locusts at the altitudes of >3700 m showed higher homozygosity than all the other populations (Fig. 8).
    This research reveals a specific mechanism for metabolic adaptation to high-altitude hypoxia by insects and improve the understanding of the complex biological features of high-altitude adaptation in animals.

    Fig. 8 Genetic differentiation of PTPN1.

    SNP detection

    SV detection

    CNV annotation


    Note: Novogene shows Circos only when CNV analysis was carried out.