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Research Services

Animal & Plant Whole Genome Sequencing


With advancements in next-generation sequencing technology, whole genome resequencing (WGS) has become the most rapid and effective method to unravel the underlying mechanisms of species origin, development, growth, and evolution at the genomic level. Utilizing WGS, complete genomic data from one or more variants can be aligned with known genomic sequences for the species. Applications of WGS include detection of genetic differences between variants, transposon fingerprinting for assessing germplasm diversity and lineages, and mapping loci associated with specific traits such as disease resistance.

Service Specifications Demo Reports


For population genetic research:

  • Population genetic structure
  • Evolutionary and demographic history
  • Natural selection

For GWAS research:

  • Development
  • Disease resistance
  • Agronomic traits


  • Extensive experience: We have completed over 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.
  • Cost-effective service: With the largest sequencing capacity in the world including the HiSeq and NovaSeq systems, we provide greater data output, quicker turnaround time, and the lowest prices possible for plant and animal genome sequencing projects of any size.
  • High verification rate: We promise that the verification rate of SNPs is higher than 95%.

Sample Requirements

Library Type Sample Type Amount (Qubit®) Purity
≤ 500 bp Insert DNA Library
(For Illumina Platform)
Genomic DNA ≥ 0.2 μg
OD260/280 = 1.8 – 2.0
Genomic DNA
(PCR-free except 350bp)
≥ 5 μg
Genomic DNA
(PCR-free low input-350bp)
≥ 1.5 μg
Mitochondrion/Chloroplast DNA ≥ 0.8 μg

Sequencing Parameters and Analysis Contents

Platform Type Illumina Novaseq 6000
Read Length Paired-end 150 bp
Recommended Sequencing Depth SNP/InDel Detection: ≥ 10X
SV/CNV Detection: ≥ 20X
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
CNV calling, annotation and statistics

Note: For detailed information, please refer to the Service Specifications & Demo Reports and contact us for customized requests.

Project Workflow

Sample Quality Control

Library Quality Control

Data Quality Control

Sample Preparation

Library Preparation


Bioinformatic Analysis

Genome Re-sequencing Reveals the Evolutionary History of Peach Fruit Edibility


Peach ( Prunus persica ) is an economically important fruit crop and a well-characterized model for studying species. The knowledge about perennial fruit crop evolution and domestication is still limited. Previous genome studies examined many cultivated peaches, but limited number of related wild peaches. The studies identified genomic regions that have undergone artificial selection. This study examines the evolutionary history of edibility in peaches by combining genomic analyses with paleo-geographic data and archeological evidence.

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.