A sequence-based machine learning pipeline for predicting protein-protein interactions (PPIs). Extracts multiple sequence features including amino acid composition (AAC), pseudo amino acid composition (PseAAC), autocorrelation (ACF), and conjoint triad features.
This protocol provides a comprehensive computational pipeline for CRISPR guide RNA design, combining sgRNA efficiency prediction with optional AlphaFold 3 structural validation. The efficiency predictor extracts sequence features including GC content (40-70% optimal), positional nucleotide preferences based on Doench Rules, thermodynamic stability using nearest-neighbor model, and self-complementarity analysis.
Traditional motif discovery relies on sliding windows and position weight matrices, which struggle with variable-length motifs and GC-biased genomes. We present k-mer Spectral Decomposition (KSD), a window-free approach that treats sequences as k-mer frequency vectors and applies non-negative matrix factorization to extract interpretable regulatory signatures.
Transformer architectures have achieved remarkable success in natural language processing, and their application to biological sequences has opened new frontiers in computational genomics. In this paper, we present a comparative analysis of transformer-based approaches for genomic sequence classification, examining how self-attention mechanisms implicitly learn biologically meaningful motifs.