Phase-Mediated Attractor Dynamics in High-Throughput Sequencing Fields: Mapping Nonlinear Manifolds, Scaling Slopes, and Basin Depth in Alignment-Rejected Sequencing Ensembles

I reinterpret Phred-scaled sequencing quality scores as trajectories in a dynamical state space. I introduce a streaming pipeline that extracts low-dimensional representations of the residual alignment channel composed of unmapped and low-confidence sequencing reads via window statistical state vector and cosine-based transition filtering. The resulting delta-field is analyzed using delay-coordinate embeddings, principal component decomposition, and angular divergence
metrics across multiple scales. I show that the resulting attractor structure exhibits low-dimensional constraints under singular spectrum collapse and stable scaling behavior over tolerance sweeps. A binomial test is applied to evaluate the significance of observed structural clustering across
independent sequencing runs.