Perfect Match Genomic Landscape strategy: Refinement and customization of reference genomes

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Accurate reference genomes (RGs) are crucial for genomic research.
• Existing algorithms for RG refinement often rely on statistical methods.
• The Perfect Match Genomic Landscape (PMGL) offers a non-statistical approach to detect genomic variations.

Purpose of the Study:

• To demonstrate the efficacy of the PMGL strategy for refining and customizing reference genomes.
• To showcase PMGL's applicability across diverse life forms and viral genomes.
• To validate PMGL's capability in detecting specific mutations in experimental settings.

Main Methods:

• Application of the automatic PMGL pipeline to various microbial genomes (archaea, bacteria, eukarya).
• Analysis of viral genomes, including SARS-CoV-2 and patient-derived COVID-19 samples.
• Utilizing a mutation-accumulation experiment in *E. coli* to assess PMGL's mutation detection sensitivity.

Main Results:

• Successful refinement of reference genomes for multiple microorganisms across the three domains of life.
• Demonstrated PMGL's ability to analyze viral genomes, including clinical isolates.
• Confirmed PMGL's sensitivity in detecting experimentally introduced mutations at specific stages.

Conclusions:

• The PMGL strategy is a powerful, non-statistical tool for refining and customizing reference genomes.
• PMGL is versatile and applicable to haploid genomes from diverse biological sources.
• PMGL can serve as a final refinement step, independent of prior assembly methods.