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Release Notes

All notable changes to SeroGen are documented on this page. This project adheres to Semantic Versioning and the Keep a Changelog specification.

[Unreleased]

Added

  • Structure-of-Arrays (serogen.core):
  • BatchedContainer and RaggedArrayContainer zero-copy memory protocols (serogen.core.collections).
  • 2-bit DNA/RNA (Alphabet.DNA) and MMSEQS12 reduced amino-acid alphabets with ragged translation across NCBI translation tables (serogen.core.seq.alphabet).
  • RandstrobeIndex (\(O(1)\) strobemer seeding) and FracMinHashSketch k-mer indices (serogen.core.seq.kmers).
  • Vectorized unbanded/banded Smith-Waterman-Gotoh local and semi-global alignment (PairwiseAligner in serogen.core.seq.align).
  • Hybrid sequence + active-site CNN motif-rescued protein clustering (Clusterer supporting greedy incremental and sparse Markov Clustering MCL modes in serogen.core.seq.clustering).
  • Packed CSR directed multigraphs (GraphModel, GraphBatch), Consensus Pan-Graph compression (GraphBatch.as_pan_graph), Hungarian + L2-QP GraphAligner, branch-and-bound maximum_spanning_arborescence, RelationalMPNN, and Fruchterman-Reingold LayoutEngine (serogen.core.graph).
  • Single-Pass ORF & CNN Role Engines (serogen.genes):
  • 29-state frameshift- and operon-overlap-aware profile-HMM Viterbi OrfPredictor with sliding 4-kb GC adaptation and single-pass viral/prophage gap rescue (serogen.genes.orf.predictor).
  • 30-class GeneRole ontology (29 functional polysaccharide biosynthesis, transport, modification, regulatory, and MGE roles + open-set UNKNOWN) and pure NumPy/Numba multi-scale 1D-CNN + 20-D biophysical GeneRoleClassifier (serogen.genes.cnn).
  • De Novo Polysaccharide Locus Discovery (serogen.loci): -Exact All-Contiguous-Partitions Dynamic Programming (ACP-DP) + multi-locus Viterbi LocusSegmenter (serogen.loci.segmenter).
  • LocusPredictor with TRANSPOSASE/INTEGRASE MGE insertion bridging, optional coordinate healing (heal_mobile_elements), and cross-contig boundary stitching (stitch_contig_splits).
  • LocusMatcher for de novo orthogroup database construction and MatchType structural variant bitflag classification (serogen.loci.matcher).
  • Interactive Benchmarks & Documentation:
  • Reproducible 16:9 Marimo slide decks (benchmark_prophage_orfs.py, train_gene_classifier.py, benchmark_spneumo_db.py) and unified SeroVerse Zensical documentation.

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