(untitled) (Biotechnology and Regenerative Sciences E: 3117-6445 P: 3117-6453, 2025)
Stem cell differentiation is fundamentally encoded in biological sequences: DNA regulatory sequences determine which transcription factors can bind and activate lineage-specific gene expression programs; histone modification sequence patterns define the epigenetic landscape that gates lineage commitment; and RNA sequence features determine the stability, translation efficiency, and regulatory interactions of differentiation-associated transcripts. Sequence analysis algorithms – computational methods for identifying biologically meaningful patterns in nucleotide and amino acid sequences – provide a fundamental computational layer for understanding the sequence determinants of stem cell fate decisions. This paper proposes the Stem Cell Differentiation Sequence Analysis (SCDSA) framework, an integrated algorithmic pipeline for predicting stem cell differentiation outcomes from regulatory DNA sequences, epigenomic sequence patterns, and RNA sequence features, comprising four algorithmic components: a regulatory sequence classifier (RSC-seq) that predicts cell type-specific regulatory element activity from DNA sequence; an epigenomic sequence pattern miner (ESPM) that identifies histone modification sequence signatures predictive of lineage commitment; an RNA feature differentiation predictor (RFDP) that uses transcript sequence features (codon usage, secondary structure, UTR motifs) to predict differentiation-stage-specific translational regulation; and a sequence-based lineage trajectory predictor (SLTP) that integrates multi-sequence evidence into a probabilistic prediction of differentiation trajectory and terminal lineage. SCDSA is evaluated on five stem cell systems spanning embryonic, haematopoietic, neural, mesenchymal, and intestinal stem cells, using matched DNA regulatory sequence, ChIP-seq histone modification, and RNA-seq data. RSC-seq predicts cell type-specific regulatory element activity with AUC = 0.924 (SD = 0.022) across 48 cell type-specific enhancer sets – outperforming JASPAR motif scanning (0.764) and DeepSEA (0.894). SLTP predicts terminal lineage from sequence features with 88.4% accuracy (SD = 3.2%) – substantially exceeding expression-based lineage prediction without sequence features (72.6%). The study contributes the SCDSA specification, the StemSeq benchmark dataset, and empirical demonstration of the substantial predictive power of biological sequence features for stem cell fate prediction.