MUSA

MUSA identifies complex, non-contiguous nucleotide motifs in biological sequences using an unsupervised approach (Motif Finding using an UnSupervised Approach) to support motif discovery and modeling of regulatory elements.

Key Features:

• Unsupervised Motif Detection: MUSA autonomously identifies over-represented complex, non-contiguous nucleotide motifs without requiring prior assumptions about motif structures.
• Parameter Estimation: MUSA estimates search parameters for modern motif finders, reducing dependence on manually specified parameters.

Scientific Applications:

• Sigma(54)-Dependent Promoter Sequences: Analysis of 70 promoter sequences from Pseudomonas putida KT2440 to identify motifs relevant to Sigma(54)-dependent transcriptional regulation.
• Phenol-Induced Gene Upregulation: Analysis of 54 promoter sequences from Pseudomonas putida KT2440 corresponding to genes up-regulated in response to phenol as indicated by quantitative proteomics.

Methodology:

MUSA employs a biclustering approach on a matrix of co-occurrences of small motifs, focusing on co-occurrence patterns to identify complex motifs without assuming composite motif structure.