Sylamer

Sylamer identifies significantly over- or under-represented words in nucleotide sequences across ranked gene lists to detect enrichment or depletion of microRNA (miRNA) and small interfering RNA (siRNA) seed sequences.

Key Features:

• Word-level enrichment analysis: Identifies over- and under-represented words across a sorted gene list to detect sequence motif biases.
• Hypergeometric scoring: Uses hypergeometric P-values to quantify the significance of word occurrence biases.
• Significance landscape plots: Produces significance landscape plots that represent significance profiles of each word across the sorted gene list.
• Genome-scale suitability: Suitable for analysis of large, genome-wide datasets.
• 3′ untranslated region (UTR) analysis: Applied to 3′ untranslated regions to study miRNA targets and siRNA off-target signals.
• Expression-ranked input: Operates on gene lists ranked by expression changes such as from miRNA perturbations, RNA interference experiments, or microarray data.

Scientific Applications:

• miRNA target analysis: Detects enrichment or depletion of miRNA seed sequences in 3′ UTRs from ranked expression data.
• siRNA off-target detection: Identifies off-target seed signals arising from RNA interference or siRNA experiments.
• Interpretation of expression-derived rankings: Reveals occurrence biases across gene lists ranked by microarray or other expression assays.

Methodology:

Computes hypergeometric P-values for word occurrences across the sorted gene list and generates significance landscape plots showing enrichment and depletion profiles for each word.