Using Dendritic Heat Maps to Simultaneously Display Genotype Divergence with Phenotype Divergence.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Traditional methods like heat maps and dendrograms are used for analyzing large sequencing datasets.
• Advancements in visualization techniques are crucial for interpreting complex genomic data.

Purpose of the Study:

• Introduce dendritic heat maps, a novel visualization tool for large-scale sequencing data.
• Demonstrate the utility of dendritic heat maps in understanding genotype-phenotype divergence.

Main Methods:

• Generated artificial datasets with controlled mutation and population growth.
• Clustered DNA sequences using fractional identity thresholds (0.75-1.0) and various linkage algorithms (minimum, maximum, average, centroid-based).
• Applied dendritic heat maps to visualize clustering hierarchy and relative sequence abundance across different cutoffs.

Main Results:

• Dendritic heat maps effectively display heat map results over aligned DNA sequence clusters for varying clustering cutoffs.
• The method visualizes the impact of group differences on clustering hierarchy and sequence relative abundance.
• Phenotype divergence may not always align with genotype divergence, a relationship highlighted by dendritic heat maps.

Conclusions:

• Dendritic heat maps offer a flexible approach to scrutinize clustering levels and track phenotype inequity across specificities.
• The divisive centroid-based clustering methodology is recommended for visualizing the effects of changing factors on clustering hierarchy and abundance.
• This visualization technique enhances the understanding of genotype-phenotype relationships in population genetics and comparative genomics.