deltaRpkm

deltaRpkm performs rapid differential gene presence analysis between large groups of closely related bacterial genomes using RPKM-based comparisons to identify genes associated with phenotypes.

Key Features:

• Efficiency in Large Datasets: Optimized for handling substantial genomic datasets, enabling analysis of numerous similar bacterial genomes such as in surveillance and outbreak detection.
• Standard Gene Count Table Utilization: Begins from a standard gene count table and eliminates the need for de novo genome assembly or alignment.
• RPKM-Based Differential Analysis: Calculates Reads Per Kilobase of transcript, per Million mapped reads (RPKM) for each gene in each sample, computes inter-group δRPKM values and the median δRPKM (m) for each gene.
• Statistical Identification of Differentially Present Genes: Identifies genes as differentially present using the threshold m ≥ 2 × s_m, where s_m is the global standard deviation of all median δRPKM values.
• Inclusion of Non-Coding Genes: Extends the RPKM-based presence analysis to non-coding genes alongside coding sequences.
• Direct Output of Phenotypically Relevant Genes: Produces a list of genes that meet the statistical threshold and are potentially involved in the phenotype under study.

Scientific Applications:

• Comparative genomics: Enables rapid identification of gene presence differences between related genomes to link genes to phenotypic traits.
• Surveillance and outbreak detection: Supports large-scale analyses where many closely related bacterial genomes are compared for epidemiological insights.
• Listeria monocytogenes case study: Demonstrated on a dataset of 225 Listeria monocytogenes genomes to efficiently process and analyze large genomic datasets.

Methodology:

Calculates RPKM per gene per sample, computes inter-group δRPKM values and the median δRPKM (m) per gene, determines the global standard deviation s_m of m values, and flags genes with m ≥ 2 × s_m as differentially present.