Evolutionary Genetics
Platform Overview
Evolutionary genetics is the study of how genetic variations leads to evolutionary change. Evolutionary studies at biomolecular level empowers: 1. Estimation of rate and time of speciation based on structural variation of amino acids and nucleotides.
2. Revealing phylogenetic relations between species with minimized influence of convergent evolution and co-evolution.
3. Estimation of genetic diversity and revealing evolution potential.
4. Building connections between genotypes and phenotypes and revealing genes controlling particular traits.
Research area
Convergent evolution of Chinese cabbage and Cabbage
Phylogenetic tree of Chinese cabbage and Cabbage
Service Workflow
Experimental design
DNA extraction
Library construction and sequencing
Data analysis
After-sale service
Resequencing-Evolutionary Genetics
Introduction: By processing whole genome resequencing on representative material (note: at least two sub-species and at least 10 individuals or mixed samples in each sub-species), Variation information such as SNPs and InDels can be identified for population genetic analysis, including Linkage Disequilibrium analysis, Phylogeny analysis, population structures, effective population size analysis, gene flow analysis, etc.
Applications:
Population genetics study on species with a reference genome, including population adaptation studies, population domestication studies, demographic history, variation database among sub-species.
Bioinformatic analysis
Standard analysis: 1. Raw data quality control
2. Alignment against reference genome
3. Genome-wide variation identification
4. Phylogenetic tree construction
5. Genetic structure analysis
6. Group selection analysis
Advanced analysis: 1.Speciation time 2. Gene flow 3. Demographic history 4. Selective sweep analysis 5. Other evolutionary models 6. Other evolutionary models+ ML tree
Phylogenetic tree
SLAF-Evolutionary Genetics
Introduction:
SLAF is a self-developed simplified genome sequencing strategy, which discovers genome-wide distributed markers, SNP. These SNPs, as molecular genetic markers, can be processed for evolutionary genetics studies including analysis on genetic structures, selective pressure, etc. and reveal evolutionary mechanisms.
Applications:
Population genetic studies on species with or without reference genome, including selection of core germplasm, population adaptation and population domestication studies.
Bioinformatic analysis
Standard analysis: 1. Raw data quality control
2. SNP identification and annotation
3. Phylogenetic tree construction
4. Genetic structure analysis
5. Group selection analysis (with reference genome)
Advanced analysis: 1.Speciation time
2. Gene flow
3. Other evolutionary models
4. Other evolutionary models+ ML tree
5. Other evolutionary models+Bayes tree
Transcriptome-Evolutionary Genetics
Introduction:
Taking advantage of high-throughput readout of transcriptome information, gene expression level evolution relationships between different species and sub-species can be extensively studied.
Applications:
Population genetic studies on species with or without reference genome, including population adaptation and population domestication studies.
Taking advantage of high-throughput readout of transcriptome information, gene expression level evolution relationships between different species and sub-species can be extensively studied.
Applications:
Population genetic studies on species with or without reference genome, including population adaptation and population domestication studies.
Bioinformatic analysis
Standard analysis: (with reference genome)
1. Raw data quality control
2. Phylogenetic tree construction
3. Genetic structure analysis
4. Group selection analysis
5. Gene expression quantification
6. Differential expression analysis
7. Expression based PCA analysis
8. DEG annotation and functional enrichment
9. Protein-protein Interaction network
10. WGCNA (at least 15 individuals)
Standard analysis: (with reference genome)
1. Raw data quality control
2. Phylogenetic tree construction
3. Genetic structure analysis
4. Group selection analysis
5. Gene expression quantification
6. Differential expression analysis
7. Expression based PCA analysis
8. DEG annotation and functional enrichment
9. Protein-protein Interaction network
10. WGCNA (at least 15 individuals)
Standard analysis: (without reference genome)
1. Raw data quality control
2. Unigene assembly
3. CDS prediction
4. SNP analysis
5. Phylogenetic tree construction
6. Genetic structure analysis
7. Group selection analysis
8. Gene expression quantification
9. Differential expression analysis
10. Expression based PCA analysis
11. DEG annotation and functional enrichment
12. Protein-protein Interaction network
13. WGCNA (at least 15 individuals)
Evolutionary Genetics with Biomarker Technologies
Biomarker Technologies has accumulated massive experience in population genetics, covering hundreds of species. Our technical group has contributed to over 60 publications in high impact journals, such as Nature Communication, Molecular Plant, Plant Biotechnology Journal, The Plant Journal, etc., accumulated IF over 220.
50+ skilled experts in analysis
Diverse successful cases
Highly-experienced technical team
3 distributed computer cluster servers
In-depth data interpretation
Optimized reports delivery
Professional after-sale support
Rapid analysis
Results Demo
Phylogenetic tree
Phylogenetic tree (or evolutionary tree) is a branched diagram describing evolutionary relationships among organisms. Based on similarities and differences in genetic characters, evolutionary processes can be speculated and displayed as phylogenetic tree.
PCA analysis
SNP-based Principal components analysis (PCA) can be processed by cluster software in order to cluster samples that are relatively more similar to each other.
Fst Analysis
Population fixation index (Fst) is a measure of alleles heterozygosity, which is frequently estimated from SNP data. Fst is a typical parameter in defining population differentiation. A higher Fst value may indicate that one of an allele genes is more adaptive to particular environmental condition, whose frequency can be significantly increased after adaptive selection.
Nucleotide diveristy (π) analysis
Nucleotide diversity (π) is a measure of nucleotide polymorphism degree within a population. It is a typical index in selective sweeping, which is independent of sample numbers. A smaller π indicates a lower nucleotide diversity. As shown in the figure on the right, X-axis stands for positions on genome and Y-axis stands for π value.
FAQ
1What are the sample requirements for Evolutionary genetics studies?
Research goal：
Demographic history
Adaptation studies
Selective domestication
Sub-species precise variation database construction
Material：
Materials from center of origin
Select typical materials under different environmental conditions
Select typical wild and domestic materials
Select most typical material in each sub-species
Demographic history
Adaptation studies
Selective domestication
Sub-species precise variation database construction
Material：
Materials from center of origin
Select typical materials under different environmental conditions
Select typical wild and domestic materials
Select most typical material in each sub-species
2What's the recommended tag numbers and depth for SLAF-Evolutionary genetics study?
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