miRvestigator

miRvestigator identifies microRNAs (miRNAs) that mediate co-regulation of gene expression by detecting over-represented sequence motifs in co-expressed gene 3' untranslated regions (3' UTRs) and matching them to known miRNA seed sequences to infer candidate regulatory miRNAs.

Key Features:

• Input Flexibility: Accepts lists of co-expressed genes from Caenorhabditis elegans, Drosophila melanogaster, Gallus gallus, Homo sapiens, Mus musculus, and Rattus norvegicus.
• Sequence Motif Identification: Searches for over-represented sequence motifs in 3' UTRs using the Weeder algorithm.
• miRNA Seed Sequence Comparison: Compares identified motifs against all unique miRNA seed sequences in miRBase using a custom hidden Markov model (HMM) algorithm.
• Similarity P-value Computation: Uses the HMM algorithm to compute a similarity P-value for each unique miRNA seed sequence.
• Putative miRNA Binding Sites: Reports putative miRNA binding sites within the 3' UTRs of query transcripts.

Scientific Applications:

• Regulatory network inference: Identifies candidate miRNAs underlying observed gene co-regulation signatures to support reconstruction of post-transcriptional regulatory networks.
• Functional studies in biology and disease: Supports investigations of miRNA roles in developmental biology, disease mechanisms, and comparative/evolutionary genomics.
• Novel miRNA-target discovery: Facilitates de novo discovery of miRNA seed sequence matches to uncover novel miRNA–target interactions.

Methodology:

Search 3' UTRs for over-represented motifs with Weeder; compare motifs to all unique miRNA seed sequences from miRBase using a custom HMM; compute similarity P-values for each seed and report putative 3' UTR miRNA binding sites.