Analyzing genomes with cumulative skew diagrams

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Microbial chromosome structure and replication are fundamental to understanding genome dynamics.
• Identifying replication origins and termini is crucial for genomic analysis.
• Nucleotide composition biases can offer insights into DNA replication and evolution.

Purpose of the Study:

• To introduce a novel method using cumulative diagrams for analyzing nucleotide composition in microbial genomes.
• To identify and predict replication origin and terminus sites in bacterial and archaeal chromosomes.
• To investigate the relationship between base composition bias, replication, and genome rearrangements.

Main Methods:

• Application of a novel cumulative diagram method to analyze nucleotide composition patterns.
• Comparison of diagram features with known replication origin and terminus sites in bacteria.
• Prediction of replication sites in uncharacterized bacterial and archaeal genomes.
• Analysis of local diagram distortions to infer genome rearrangements.
• Examination of viral and mitochondrial genomes to correlate base composition bias with single-stranded DNA duration during replication.

Main Results:

• Cumulative diagrams reveal two distinct points of nucleotide composition change in microbial chromosomes, correlating with replication origin and terminus sites.
• A significant bias was observed, with the leading strand exhibiting a higher proportion of guanine (G) over cytosine (C) residues.
• The method successfully predicted replication origin and terminus locations in various bacterial and archaeal genomes.
• Local distortions in cumulative diagrams indicated recent genome rearrangements, exemplified in specific Escherichia coli strains.
• Analysis of viral and mitochondrial genomes suggested a correlation between base composition asymmetry and the extent of single-stranded DNA exposure during replication.

Conclusions:

• Cumulative diagrams provide a powerful tool for identifying key genomic features like replication origins and termini.
• The leading strand's G-rich bias is a conserved feature in microbial replication, useful for predictive genomics.
• Diagrammatic analysis offers a method to detect and study genome plasticity and rearrangements.
• The findings link DNA replication dynamics, specifically the time spent in a single-stranded state, to observed base composition biases.