Whole Genome Resequencing Data Analysis

What is Whole Genome Resequencing Data Analysis?

Whole Genome Resequencing makes use of high-throughput sequencing method to sequence the whole genome of individuals or groups to find sequence or structural variation and other information. Whole Genome Resequencing is only applied on the species with known reference genome sequence. The sequencing is carried out on different individuals (such as patients and normal individuals) or different tissues of an individual (such as diseased tissues and normal tissues). Differences between individuals or between tissues and cells can be found at the overall level. After the alignment of each sample’s sequenced data to the reference genome sequence, we can find a large number of variation information such as single nucleotide variants (SNV), insertion and deletion (InDel), structural variation (SV) and copy number variation (CNV), so as to find the pathogenic genes and mutations of the disease, analyze the pathogenesis of the disease, population genetic mechanism and obtain population genetic characteristics.

Fig.1 copy number variations and structure variation. (Shu Y et al., 2018)

Application of Whole Genome Resequencing Data Analysis

Whole genome resequencing analysis can be used for but is not limited to the following research:

CD Genomics Data Analysis Workflow

Sample Submission Guidelines of Sequencing

What We Offer

• Data quality control, remove joint contamination and low-quality data
• Align to reference genome sequence, make statistics of sequencing depth and coverage
• SNP / InDel / SV / CNV detection, annotation, effect prediction and statistics
• Circos diagram display of genome variation
• GO / KEGG classification and enrichment analysis of the interested genes

In addition, we can tailor the content of whole-genome biological information analysis according to your specific project needs.

Example Data Analysis Report

To showcase the quality and detail of a CD Genomics report for shotgun metagenomic sequencing data analysis, we offer a sample report upon request. You can contact us to obtain this report. Additionally, refer to a client-published article for more insights. " Identification of the genetic elements involved in biofilm formation by Salmonella enterica serovar Tennessee using mini-Tn10 mutagenesis and DNA sequencing." which includes some of the data we provided.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

CD Genomics provides general analysis and customized analysis of whole genome sequencing data analysis. Experienced teams of scientists, researchers, and technicians, we provide fast turnaround, high-quality data reports at competitive prices for worldwide customers. Customers can contact our employees directly and we will respond promptly. If you are interested in our services, please contact us for more detailed information.

Reference

1. Teixeira V H ; et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nature Medicine, 2019.

What Does Data Analysis of Whole Genome Resequencing Show?

SNP Detection and Annotation

Fig 2. Shared SNP number between samples

The SNPs Detection of a Single Sample

Fig 3. SNP mutation type distribution.

SNP Annotation

Fig 4. Statistics pie of SNP annotations.

Small InDel Detection and Annotation

Fig 5. Shared InDel number between samples

Fig 6. InDel length distribution in both the whole genome scale

InDels Annotation

Fig 7. Statistics pie of InDel annotations.

Title: Sex hormones, sex chromosomes, and microbiota: Identification of Akkermansia muciniphila as an estrogen-responsive microbiota

Publication: Microbiota Host

Main Methods: 16S rRNA gene sequencing , whole genome sequencing

Abstract: This study explored the influence of sex chromosomes on gut microbial diversity, independent of sex hormone effects, utilizing the four-core genotype mouse model. Gonadectomized mice exhibited reduced levels of estradiol, progesterone, and testosterone. In females, microbial α diversity increased post-gonadectomy, contrasting with males. β diversity varied between male XX and XY mice, with no significant differences observed among females. Akkermansia muciniphila abundance was lower in gonadectomized females, correlating with enhanced growth in response to β-estradiol, highlighting its responsiveness to female sex hormones.

Main Research Results:

Gonadectomy mediated decline in sex steroids of female and male mice:

Orchiectomy in male mice resulted in no change in estradiol but significantly decreased testosterone levels. Castration increased 11-Deoxycorticosterone in XXM but not XYM mice, with no differences in aldosterone, 11-Deoxycortisol, Corticosterone, DHEA, and Pregnenolone. Progesterone decreased in XYM mice. Tetrahydroxy 11-deoxycortisol significantly increased in both XXM and XYM mice, while Tetrahydroxy aldosterone, DHEAS, Estradiol, and Estriol remained unchanged.

Fig 8. Plasma steroid analysis of gonadal intact and gonadectomized mice. Estradiol decreased after ovariectomy (P<0.001), testosterone increased in XYF (P=0.04), and aldosterone was higher in XXF (P<0.01). Castrated males had lower testosterone (P<0.0001) but no significant changes in estradiol or aldosterone. Data were analyzed using 2-way ANOVA and Sidak's multiple comparisons test.

Gut microbiota was not significantly impacted by sex chromosomes in female mice:

The gut microbial composition of eight groups of mice (intact and gonadectomized, male and female, XX and XY) was analyzed using Bray Curtis principal coordinate analysis (PCoA). Significant differences were found between intact male (XY) and intact female (XX) groups, with a trending difference between XY and XX males but no difference between XY and XX females. The findings suggest sex chromosomes impact microbiota composition in males, while sex hormones influence females.

Fig 9. Bray Curtis PCoA analysis of β-diversity of the gut microbiota. (A) Comparison between female intact and male intact. (B) Comparison between male XY and XX ANOSIM. (C) Comparison between female XY and XX ANOSIM

Gonadectomy increased α-diversity of the gut microbiota in female mice:

The α-diversity changes in the gut microbiota of gonadectomized mice were examined. Notably, all four parameters—richness, evenness, observed taxa, and diversity—were higher in gonadectomized females compared to intact females, independent of sex chromosome complement. This suggests that female sex hormones exert a stronger influence on gut microbiota than sex chromosomes.

Fig 10. Microbial analysis of α-diversity. All groups were analyzed by 16S sequencing. Gonadectomy increased Richness (XXF: P=0.03, XYF: P<0.001), Evenness (XXF: P=0.04, XYF: P=0.02), Observed Taxon (XXF: P<0.01, XYF: P=0.0003), and Shannon Diversity (XXF: P=0.02, XYF: P=0.02). Data were analyzed using 2-way ANOVA and multiple unpaired t-tests. Each group had 6-8 samples. Error bars represent SEM. *P < 0.05, ***P < 0.001.

Gonadectomy decreased Akkermansia in female mice:

Microbial analysis in female mice showed no significant PCoA differences between intact and gonadectomized groups. Sankey diagrams revealed major shifts in bacterial composition, notably in Verrucomicrobia, Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Akkermansia was significantly reduced in gonadectomized XX and XY females, suggesting a positive association with female sex hormones. In male mice, no PCoA differences were observed, but genus-level analysis showed specific bacterial enrichment in XYM and XXM intact groups.

Fig 11. Sankey diagrams depicting phyla changes in male and female gonadal intact and gonadectomized mice.

β-estradiol promoted the growth of Akkermansia muciniphila:

To assess the impact of female sex hormones on Akkermansia growth, they cultured A. muciniphila with and without β-estradiol under anaerobic conditions. A. muciniphila, the most abundant human-relevant species, was monitored using OD600 values. Results showed that β-estradiol supplementation at 2.2 μM and 10 μM concentrations positively and dose-dependently enhanced A. muciniphila growth over 4 hours.

Fig 12. Pro-growth effect of β-estradiol on A. muciniphila.

Conclusion:

This study establishes a foundational understanding of the roles played by sex hormones and chromosomes in shaping the gut microbiome, with a particular focus on A. muciniphila as responsive to estradiol. However, limitations include factors such as the absence of bedding transfer, housing conditions, and distinctions between fecal and gut mucosal microbiomes. Future research should employ standardized methodologies to bolster reproducibility. These findings underscore the necessity of accounting for variables like age, housing environment, and circadian rhythms in gut microbiome investigations, while also suggesting potential protective functions of A. muciniphila in ovariectomized females.

1. What is the workflow of whole genome sequencing?

The workflow of whole genome resequencing involves DNA extraction, fragmentation, library preparation, sequencing using platforms like Illumina, data quality control, alignment to a reference genome, variant calling, and annotation. This process identifies genetic variations by comparing the sequenced genome to the reference genome.

2. What is the process of whole genome resequencing data analysis?

The process of whole genome resequencing data analysis includes quality control of raw sequencing data, alignment to a reference genome, variant calling to identify genetic differences, annotation of variants, and downstream analyses such as filtering, functional impact assessment, and interpretation of biological significance.

3. What are the research directions in which whole genome sequencing technology is applicable?

Whole genome sequencing is valuable for studying Mendelian and complex diseases, rare diseases, de novo mutations, drug genomes, and population evolution. It is particularly useful for complex diseases like cancer and schizophrenia, as it identifies non-coding region variations and structural variations, uncovering pathogenic mutation sites comprehensively.

4. How to choose the sequencing depth for whole genome sequencing?

Sequencing depth varies by research purpose, sample size, and expectations. While 30× depth detects most SNVs, at least 50× is recommended for identifying structural variations and low-abundance mutations in cancer tissues. Population resequencing can use lower depth (~10×) with population analysis strategies to identify relevant variations.

5. What samples are suitable for whole genome sequencing?

Suitable samples for whole genome sequencing encompass genomic DNA extracted from diverse sources including blood, saliva, tissue biopsies, and cultured cells. These specimens yield high-quality DNA essential for thorough genomic analyses and variant detection.