GPMiner

GPMiner identifies and annotates promoter regions and regulatory features in genomic sequences to support analysis of transcriptional regulation and comparative promoter studies in human and mouse genomes.

Key Features:

• Transcriptional Start Site (TSS) Prediction: Integrates three TSS prediction tools with orthologous conserved features to enhance identification of transcription initiation sites.
• Regulatory Feature Annotation: Annotates CpG islands, tandem repeats, TATA boxes, CCAAT boxes, GC boxes, GC content, nucleotide composition, and DNA stability within promoter regions.
• Over-represented Oligonucleotides: Detects over-represented oligonucleotides, including hexamer nucleotides, in promoter sequences.
• Combinatorial Interaction Analysis: Identifies co-occurrence of cis-regulatory elements to reveal combinatorial interactions of transcription factors in co-expressed genes.
• Cross-Species Conservation Analysis: Compares conserved regulatory features between homologous promoters to support evolutionary and comparative genomics studies.
• Homogeneity Analysis: Assesses homogeneity of input sequences against experimental mammalian promoter sequences.
• Predictive Modeling: Builds a support vector machine (SVM) model using integrated features and evaluates performance by five-fold cross-validation.

Scientific Applications:

• Transcriptional regulation studies: Identification of TSSs and regulatory elements to investigate mechanisms controlling gene expression.
• Functional genomics: Annotation of promoter and regulatory features in genome-wide studies for human and mouse.
• Comparative and evolutionary genomics: Detection of conserved regulatory mechanisms across species via homologous promoter comparison.
• Regulatory network inference: Analysis of co-occurring cis-elements to inform combinatorial transcription factor interactions in co-expressed gene groups.

Methodology:

Integration of three TSS prediction tools with orthologous conserved features (CpG islands, nucleotide composition, over-represented hexamer oligonucleotides, DNA stability) and construction of a support vector machine (SVM) predictive model evaluated by five-fold cross-validation (~80% sensitivity and specificity).