SpideR

SpideR provides functions for DNA barcoding analyses and species delimitation in R, supporting summary statistics, specimen identification efficacy assessment, divergence-threshold testing, diagnostic nucleotide analysis, probability of reciprocal monophyly testing, and sliding-window marker design for degraded DNA and environmental DNA (eDNA) studies.

Key Features:

• Summary statistics generation: Generates summary statistics from DNA barcode data to characterize genetic diversity and structure within and between taxa.
• Specimen identification efficacy assessment: Evaluates specimen identification performance to quantify classification accuracy based on barcode data.
• Divergence threshold optimization: Tests and optimizes divergence threshold limits for delimiting closely related species.
• Diagnostic nucleotide analysis: Identifies and assesses diagnostic nucleotides that distinguish taxa.
• Probability of reciprocal monophyly testing: Calculates the probability of reciprocal monophyly to evaluate evolutionary relationships and species boundaries.
• Sliding-window analysis and marker design: Performs sliding-window analyses across genes to inform marker design, including applications to degraded DNA and eDNA samples.

Scientific Applications:

• Species delimitation and speciation studies: Supports delimitation analyses and investigations of speciation processes using DNA barcodes.
• Taxonomy and phylogenetic relationships: Aids taxonomic research and the exploration of species relationships through monophyly and divergence assessments.
• Marker development for degraded DNA and eDNA: Informs design of short or region-specific markers suitable for degraded samples and environmental DNA studies.
• Integrated nucleotide and morphological analyses: Facilitates combined analyses of nucleotide and morphological data for comprehensive taxonomic assessment.
• Assessment of species boundaries using DNA barcoding: Provides tools to evaluate and validate species boundaries inferred from barcode data.

Methodology:

Implements analyses within the R environment, integrating with other bioinformatics tools and datasets and leveraging R's statistical functions.