skDER & CiDDER: two scalable approaches for microbial genome dereplication

Area of Science:

• Microbial genomics
• Bioinformatics
• Evolutionary biology

Background:

• The increasing number of sequenced microbial genomes presents computational challenges for comparative studies.
• Over-representation of certain lineages due to sampling bias can skew evolutionary analyses.

Purpose of the Study:

• To develop efficient tools for genomic dereplication, enabling the selection of representative genomes for comparative genomic investigations.
• To address computational burdens and biases associated with analyzing large-scale microbial genome datasets.

Main Methods:

• skDER: Utilizes advanced algorithms for fast Average Nucleotide Identity (ANI) estimation and genomic dereplication.
• CiDDER: Employs protein clustering to iteratively select representative genomes until a desired saturation of the protein space is achieved.
• Auxiliary functionalities include automated genome downloading, clustering non-representatives, and filtering mobile genetic elements (MGEs).

Main Results:

• skDER and CiDDER provide efficient solutions for selecting representative microbial genomes.
• Filtering MGEs was found to slightly decrease ANI and alignment fraction (AF) estimates between genomes in one species.
• The developed tools offer features to enhance usability and streamline comparative genomic workflows.

Conclusions:

• skDER and CiDDER facilitate more manageable and accurate comparative genomic studies.
• Understanding the impact of MGEs on genomic metrics is crucial for accurate downstream analyses.
• These tools aid researchers in navigating large microbial genomic datasets for evolutionary insights.