Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.

Area of Science:

• Population Genetics
• Molecular Ecology
• Bioinformatics

Background:

• Analyzing multilocus genotype data is crucial for understanding population genetic structure.
• Model-based clustering programs require complex, multi-step user interventions for accurate results.
• Comparing results across different model assumptions and cluster numbers (K) is challenging.

Purpose of the Study:

• To introduce Clumpak, a novel method for automating the postprocessing of model-based population structure analyses.
• To streamline the interpretation of results from population genetic clustering.
• To facilitate comparisons across different analyses and parameters.

Main Methods:

• Clumpak utilizes a Markov clustering algorithm on similarity matrices (generated by Clumpp) to group similar runs at a fixed K.
• It identifies consensus solutions for distinct clustering modes.
• The method aligns inferred clusters across different K values for simplified comparison.

Main Results:

• Clumpak automates the identification of distinct clustering solutions from multiple runs.
• It provides an optimal alignment of clusters across varying K values.
• The software integrates methods for selecting K and comparing results from different analyses.

Conclusions:

• Clumpak significantly simplifies the use of model-based analyses in population genetics and molecular ecology.
• The tool enhances the efficiency and reliability of population structure identification.
• It offers a comprehensive solution for postprocessing and comparing clustering outputs.