CFSP

CFSP discovers frequent sequence patterns in nucleic acid datasets to identify conserved motifs associated with transcriptional regulation and functional genomic elements.

Key Features:

• Frequent Sequence Pattern Discovery: Identifies conserved subsequences in nucleic acid datasets using a Teiresias-like feature extraction approach.
• Gap-Tolerant Pattern Detection: Detects sequence motifs with long-distance correlations by allowing large gaps between subsequences.
• Mutation-Aware Pattern Matching: Incorporates single nucleotide variants and insertion–deletion mutations (indels) to enable fuzzy matching of sequence patterns.
• Predictive Feature Extraction: Extracts sequence patterns used for predictive modeling of functional genomic elements.

Scientific Applications:

• Regulatory Motif Discovery: Identifies conserved sequence motifs involved in transcriptional regulation and protein–DNA interactions.
• Non-Coding RNA Identification: Detects sequence features associated with miRNA, piRNA, and Sigma 54 promoter regions.

Methodology:

CFSP constructs arrays of frequent subsequences from ordered nucleic acid sequences and encodes mutation information, including single nucleotide variants and indels, to identify conserved sequence patterns with fuzzy matching.