Animal and Plant Whole Genome Sequencing（WGS） is an instrumental technique that is commonly employed to sequence the entire genomes of animals and plants, respectively, and aims at identifying genomic variations such as SNP, InDel, CNV, and SV. Whole Genome Sequencing is an ideal approach to determine the entirety of genetic information at a single nucleotide level.
Novogene offers Animal and Plant Whole Genome Sequencing services with ultra-fast turnaround time, high-quality sequencing data, and reliable results. Animal and Plant Whole genome sequencing has found its applications in several fields including population genetics research, genome-wide association studies (GWAS), and agricultural breeding programs.
Specifications: DNA Sample Requirements
|Platform Type||Sample Type||Amount (Qubit®)||Purity|
NovaSeq X Plus /NovaSeq6000
|Genomic DNA||≥ 200 ng||A260/280=1.8-2.0;
(PCR free non-350bp)
|≥ 5 μg|
(PCR free -350bp)
|≥ 1.2 μg|
|PacBio Sequel II
DNA CLR library
|HMW Genomic DNA||≥ 8 μg||A260/280=1.8-2.0;
Fragments should be ≥ 40 kb
|PacBio Revio/sequel II/sequel IIe DNA HiFi library||HMW Genomic DNA||≥ 5 μg||A260/280=1.8-2.0;
Fragments should be ≥ 30 kb
|Nanopore PromethION||HMW Genomic DNA||≥ 8 μg||A260/280=1.8-2.0;
Fragments should be ≥ 30 kb
*NC/QC: NanoDrop concetration/Qubit concentration
Specifications: Sequencing and Analysis
|Platform Type||Illumina NovaSeq X Plus /NovaSeq6000||PacBio Revio/sequel II/sequel IIe||Nanopore PromethION|
|Read Length||Paired-end 150 bp||Average > 15 kb||Average > 17 kb|
|For SNP/InDel detection: ≥ 10×||For SV detection: ≥ 20×|
|For SV/CNV detection: ≥ 20×|
From sample and library preparation, short and long-read sequencing, and data quality control, to bioinformatics analysis, Novogene provides high-quality products and professional services. Each step is performed in agreement with a high scientific standard and meticulous design to ensure high-quality research results.
Featured Publications of Animal and Plant Whole Genome Sequencing
Resequencing of 1,143 indica rice accessions reveals important genetic variations and different heterosis patterns
Journal: Nature CommunicationsDate: 22 September, 2020IF: 12.121DOI:https://www.nature.com/articles/s41467-020-18608-0
Journal:Plant Biotechnology JournalIssue date: 18 September, 2020IF: 8.154DOI: https://onlinelibrary.wiley.com/doi/10.1111/pbi.13480
Journal: Molecular Plant date: 5 September, 2020IF:12.084DOI:
Journal:Nature GeneticsIssue date: 2018IF: 27.959DOI: https://www.nature.com/articles/s41588-018-0119-7
Journal: Nature CommunicationsIssue date: 2018IF:12.353DOI:https://www.nature.com/articles/s41467-018-07744-3
Sequencing Quality Distribution
Sequencing quality distribution is examined over the entire length of all sequences, to detect regions where incorrect bases are incorporated at abnormally high levels. The detailed sequencing quality distribution is as follows.
Distribution of sequencing quality
Note： The x-axis shows the base position within a sequencing read, and the y-axis shows the average phred score of all reads at each position.
(Pair-end sequencing data are plotted together, with the first PE150 bp representing read 1 and the following PE150 bp for read 2.)
Sequencing Error Rate
The sequencing error rate is the major confounding factor of precise detection of low-frequency variations by deep sequencing. It determines the quality of the sequencing data. The sequencing error rate is highly associated with the sequencing cycle, escalating towards the end of each read because of the consumption of chemical reagents, which is a common feature of the Illumina high throughput sequencing platform.
Distribution of sequencing error rate
Note: The x-axis shows the base position along each sequencing read and the y-axis shows the base error rate.
(Pair-end sequencing data are plotted together, with the first PE150 bp representing read 1 and the following PE150 bp describes read 2.)
Filtering reads containing adapter or with low quality
The sequencingraw reads often contain low-quality reads or reads with adaptors, which influences the quality of the downstream analysis. To avoid this, it is necessary to filter out the raw reads and obtain clean reads. Raw reads filtering should be done under the following conditions:
(1) Remove the paired reads when either read contains adapter contamination;
(2) Remove the paired reads when uncertain nucleotides (N) constitute more than 10 percent of either read;
(3) Remove the paired reads when low quality nucleotides (base quality less than 5, Q ≤ 5) constitute more than 50 percent of either read.
Classification of the sequenced reads
(1) Adapter related: (reads containing adapter) / (total raw reads).
(2) Containing N: (reads with more than 10% N) / (total raw reads).
(3) Low quality: (reads of low quality) / (total raw reads).
(4) Clean reads: (clean reads) / (total raw reads).
Alignment with reference genome
Mapping Statistics with Reference Genome
The mean depth of each chromosome.
Note: The x-axis shows the chromosome and the y-axis shows the mean depth
SNP Detection & Annotation
ANNOVAR is a software that efficiently annotates genetic variants spotted from the genome utilizing contemporary information. Provided an index of variants with reference nucleotide, start position, end position, observed nucleotides, and chromosome, ANNOVAR can perform gene-based annotation, region-based annotation, filter-based annotation as well as other functionalities.
Note: The figure demonstrates the distribution of (A) The number of SNPs in different regions of the genome (left) and (B) the number of SNPs of different types in the coding region (right)
SNP Quality Distribution
To assess the credibility of detected SNPs, we checked the distribution of support reads number, SNP quality, as well as the distance between adjacent SNPs. The results as follows.
Cumulative distribution of SNP quality
Note: These figures demonstrate the quality distribution of SNPs, distribution of SNP support reads number, distribution of distances between adjacent SNPs, and the cumulative distribution of SNP quality.
SNP Mutation Frequency
Take the T:A>C:G mutations as an example, this category includes mutations from T to C and A to G. When T>C mutation appears on either of the double-strand, the A>G mutation will be found in the same position of the other chain. Therefore the T>C and A>G mutations are classified into one category. Accordingly, the whole-genome SNP mutations could be classified into six categories.
Frequency of SNP mutations
Note:The x-axis represents the number of the SNPs, and y-axis indicates the mutation types.
CNV Detection & Annotation
Copy-number variation (CNV) is a type of structural variation that happens when a DNA fragment is present in variable copy number in comparison to a reference genome. It pinpoints the deletions and duplications in the genome. Based on the reads depth of the reference genome, CNVnator can be used to detect CNVs of potential deletions and duplications with the following parameter ‘-call 100’. The detected CNVs are then further annotated by ANNOVAR.
Ann Variation type statistics distribution of CNVs
Note:The x-axis represents samples, and the y-axis indicates the proportion of each type of CNVs.
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