iPromoter-2L

iPromoter-2L predicts and classifies DNA promoter sequences using a two-layer model and a multi-window-based pseudo K-tuple nucleotide composition (PseKNC) approach to identify promoters and assign sigma-factor types.

Key Features:

• Two-Layer Prediction Model: The first layer discriminates promoter versus non-promoter sequences and the second layer classifies promoters into σ24, σ28, σ32, σ38, σ54, or σ70.
• Multi-Window-Based PseKNC: Uses pseudo K-tuple nucleotide composition across short-, middle-, and long-range windows to capture sequence patterns and discriminate between similar consensus sequences and within-type variability.

Scientific Applications:

• Promoter identification: Detects promoter regions within genomic DNA sequences for downstream regulatory analysis.
• Sigma-factor assignment: Classifies promoters into σ24, σ28, σ32, σ38, σ54, or σ70 types to inform studies of bacterial transcription regulation.
• Functional genomics: Supports analyses of promoter diversity and its implications for gene expression and regulatory mechanism studies.

Methodology:

Applies a two-layer classification scheme using a multi-window-based pseudo K-tuple nucleotide composition (PseKNC) representation that captures short-, middle-, and long-range sequence information; the first layer distinguishes promoter versus non-promoter sequences and the second layer assigns promoters to σ24, σ28, σ32, σ38, σ54, or σ70.