Ontologizer

Fast and safe implementation of the Ontologizer — a tool for Gene Ontology (GO) enrichment analysis using Frequentist ( hypergeometric test) and Bayesian (inference) methods.

Gene Symbols

Gene symbols in the study and population gene sets must match the gene symbols in the .gaf annotation file — specifically column 2 (DB_Object_Symbol).

Project Layout

ontologizer/
├── examples/                    # Example inputs (tracked in git)
│   ├── yeast/
│   │   ├── study_genes.txt      # 493 yeast study genes
│   │   ├── population_genes.txt # 6010 yeast background genes
│   │   └── config.json          # Bayesian analysis config
│   └── go0090717/
│       ├── study_genes.txt      # Study genes for GO:0090717
│       ├── population_genes.txt # Background genes
│       └── config.json          # Frequentist analysis config
│
├── data/                        # Downloaded at runtime (gitignored)
│   ├── go-basic.json            # Auto-downloaded from GO
│   └── goa_yeast.gaf            # Provide your own annotation file
│
└── output/                      # Analysis results (gitignored)
    └── enrichment_result.csv

Quick Start

1. Provide a GO annotation file

Download a .gaf file for your organism from the GO Annotation Database and place it in data/. For example:

• Yeast: data/goa_yeast.gaf
• Human: data/goa_human.gaf

The GO ontology (data/go-basic.json) is downloaded automatically on first run.

2. Run an example

Run the yeast Bayesian example (uses config.json in the project root by default):

cargo run --release --features cli

Or point to a specific example config:

cargo run --release --features cli -- examples/yeast/config.json
cargo run --release --features cli -- examples/go0090717/config.json

Results are written to output/enrichment_result.csv.

3. Build the binary

cargo build --release --features cli
./target/release/ontologizer examples/yeast/config.json

Configuration

Each example ships with a ready-to-use config.json. The schema is:

{
  "study_genes_path": "examples/yeast/study_genes.txt",
  "population_genes_path": "examples/yeast/population_genes.txt",
  "go_path": "data/go-basic.json",
  "goa_path": "data/goa_yeast.gaf",
  "method": {
    "method": "bayesian"
  }
}

For frequentist analysis:

{
  "method": {
    "method": "frequentist",
    "topology": "standard",
    "correction": "bonferroni"
  }
}

Topology options: standard, parentUnion, parentIntersection

Correction options: bonferroni, bonferroniHolm, benjaminiHochberg, none

Architecture

graph TD
    %% Semantic, high-contrast class definitions
    classDef file fill:#f8f9fa,stroke:#6c757d,stroke-width:2px,stroke-dasharray: 5 5,color:#212529;
    classDef struct fill:#e3f2fd,stroke:#0d6efd,stroke-width:2px,color:#052c65;
    classDef process fill:#e8f5e9,stroke:#198754,stroke-width:2px,color:#0a3622;
    classDef decision fill:#fff3cd,stroke:#ffc107,stroke-width:2px,color:#664d03;
    classDef enumType fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px,color:#4a148c;

    ProblemFile[("<div style='width: 120px; text-align: center;'>Config File</div>")]:::file
    ProblemFile --> Data_Loading

    subgraph Data_Loading ["Load"]
        direction TB
  SF[("<div style='width: 120px; text-align: center;'>Study Genes</div>")]:::file
        PF[("<div style='width: 120px; text-align: center;'>Population Genes</div>")]:::file
        GOF[("<div style='width: 120px; text-align: center;'>Gene Ontology</div>")]:::file
        GAF[("<div style='width: 120px; text-align: center;'>GO Annotation</div>")]:::file

        AnnotationIndex["AnnotationIndex"]:::struct

        SF & PF & GOF & GAF --> AnnotationIndex
    end

    subgraph Analysis ["Enrichment Analysis"]
        direction TB

        subgraph Frequentist_Module ["Frequentist Approach"]
            direction TB
            subgraph FreqConfig ["Test Configuration"]
                Background["Background"]:::enumType
                Correction["Correction"]:::enumType                    
            end
            FreqTest["Test: Hypergeometric"]:::process
            FreqMeasure["Measure: P-Value"]:::struct
            
            Background & Correction --> FreqTest
            FreqTest --> FreqMeasure
        end

        subgraph Bayesian_Module ["Bayesian Approach"]
            direction TB
            subgraph BayesianData ["Data Structures"]
                Model["Model: OrModel"]:::struct
                Cache["Cache: OrCache"]:::struct
                State["State: MgsaState"]:::struct
            end

            Algorithm["Algorithm: Metropolis-Hastings"]:::process
            BayesMeasure["Measure: Posterior Probability"]:::struct

            Model & Cache & State --> Algorithm
            Algorithm --> BayesMeasure
        end
    end

    BranchDec{"Method Selection"}:::decision

    AnnotationIndex --> BranchDec

    BranchDec --> FreqConfig
    BranchDec --> BayesianData

    subgraph Output_Phase ["Output"]
        direction TB
        Result["AnalysisResult"]:::struct
        Writer["CSV Writer"]:::process
        OutputFile[("result.csv")]:::file

        FreqMeasure --> Result
        BayesMeasure --> Result

        Result --> Writer
        Writer --> OutputFile
    end

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