Rose Engine — Dynamic Biological Systems Interaction Explorer

Rose Engine is a computational biology research platform investigating how synonymous codon choice, RNA modification, metabolic context, immune state, and structural biology jointly shape disease across 52 diseases. Every result is grounded in 44 published biochemical databases with complete evidence chains and no synthetic data.

Operated by Rose Engine, LLC. Live site: https://roseengine.io/.

What the platform surfaces

Maps 45 entity types across 6 biological system levels (genes, RNA modifications, enzymes, metabolites, diseases, ribosome biogenesis factors, assembly intermediates, ViennaRNA folding-constraint nodes, PURE-system translation kinetics profiles, disome-seq collision-risk nodes) with a toggleable connection map and interactive Graph Explorer.
Computes ViennaRNA RNA secondary-structure folds for every rRNA modification site (41-nt windows), reporting local minimum free energy (ΔG) and per-position unpaired probabilities.
Integrates the ribosome PDB structure (4UG0) with 3D distance annotations — per-site distances to the peptidyl-transferase center, decoding center, and exit tunnel, plus proximal protein contacts with distance measurements.
Profiles PURE-system translation kinetics per codon (dwell times, elongation rates) grounded in published in-vitro reconstitution data.
Analyzes disome-seq ribosome collision risk per mRNA, flagging P-site/A-site codon pairs at elevated collision risk from published disome-sequencing occupancy data.
Models ribosome assembly thermodynamics with a stage-ordered maturation pathway, per-step ΔG and factor/r-protein binding measurements from published biochemical studies.
Produces a coverage-confidence score for every disease so users see exactly where evidence is thick or thin.
Provides disease-specific monitoring protocols and reference materials for 52 diseases.
Traces mechanism causation from gene to phenotype across the biological hierarchy.
Analyzes RNA modification combinatorial codes (the epitranscriptome).

Data foundations — 44 published databases

MODOMICS, RMBase, snOPY, GtRNAdb, NCBI RefSeq, Ensembl, ClinVar, COSMIC, GEO, GTEx, GWAS Catalog, ClinicalTrials.gov, HPO (Human Phenotype Ontology), AlphaFold, RCSB PDB, PDBe, UniProt, STRING DB, KEGG, Reactome, IMPC, ChEMBL, PubChem, DGIdb, Open Targets, CIViC, OncoKB, ChEBI, Rhea, SABIO-RK, OmniPath, iPTMnet, DisProt, CORUM, JASPAR, ReMap, PhosphoSitePlus, SIGNOR, ENCODE, 4D Nucleome, Roadmap Epigenomics, HMDB, Human Protein Atlas, IUPHAR/GtoPdb. No simulated or synthetic data is used. Tools and methods are credited separately and are not counted among the 44 databases: ViennaRNA (RNA secondary-structure prediction) and Ribo-seq (ribosome profiling).

Main sections

Home — landing page and platform overview.
Section 1 — Network Atlas: 45 entity types across 6 biological system levels with toggleable connection map and Graph Explorer.
Section 5 — Reference & Coverage: disease coverage confidence scores, monitoring protocols, and reference materials for 52 diseases.
About: project background, methodology, contact.

Citations and provenance

Every recommendation surfaced by the platform carries an evidence chain back to one or more of the 44 source databases. PubMed IDs (PMIDs) are required for inclusion in the rRNA modification site registry; entries lacking a resolvable PMID are dropped at parse time. A machine-readable briefing for AI assistants is published at /llms.txt; the full route map is at /sitemap.xml.