Biomarker Glycan Structure Terms (bGST) Workflow

LLM-powered pipeline for extracting & normalizing glycan structure terminology
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  Table of Contents

1. About this project
2. Getting started
3. Usage
4. Data model
5. License
6. Acknowledgements

About This Project

Biomarker Glycan Structure Terms (bGST) is a controlled vocabulary of glycan structure terms extracted from literature and databases. It captures textual representations of glycans and glycan-related structural features, including full structures, motifs, epitopes, and substructures.

Because glycan structures are described inconsistently across sources, this project uses an LLM-assisted retrieval and entity resolution workflow to map terms to existing Glycan Structure Dictionary (GSD) entries or register new ones when needed. This helps unify heterogeneous glycan terminology into a normalized, de-duplicated reference knowledgebase.

Previous Work:

Vora J, Navelkar R, Vijay-Shanker K, Edwards N, Martinez K, Ding X, Wang T, Su P, Ross K, Lisacek F, Hayes C, Kahsay R, Ranzinger R, Tiemeyer M, Mazumder R. The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms. Glycobiology. 2023 Jun 3;33(5):354-357. doi: 10.1093/glycob/cwad014. PMID: 36799723; PMCID: PMC10243773.

Local LLM inference Run the pipeline entirely locally via Ollama, with configurable model selection and hardware setup.

Structured term normalization Extract, normalize, and align glycan terminology through a state-driven workflow orchestrated by LangGraph.

Vector search + embeddings Build and query vector stores (Chroma) using embedded representations for similarity lookup.

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Getting Started

Follow these steps to get a local copy up and running.

Prerequisites

Ollama is a local LLM inference runtime and model management layer that lets you pull and serve foundation models on-device. It abstracts backend details such as model packaging and request orchestration so developers can run local models with minimal and across different setups.

• Install Ollama from https://ollama.com/download, or alternatively:

  curl -fsSL https://ollama.com/install.sh | sh

  • Ollama version >=v0.15.0 is recommended

• HPC Users Only:

  • On servers that run on environment modules (Lmod), use the following to view pre-installed modules:

    module avail

  • To display default version of Ollama:

    module -d avail ollama

Installation

1. Clone this repo:

   git clone https://github.com/glygener/glycan-structure-dictionary.git
   cd glycan-structure-dictionary

2. Pull the required Ollama models:

   A thinking model and an embedding model are required. If you chose to use other models, remember to update the model names at configs/models.yaml. This pipeline was developed using a locally hosted Ollama server where GPU acceleration is almost necessary. Otherwise, Ollama also offers cloud models with limited free usage. For accessing cloud models and obtaining a Ollama API key, refer to their documentation

   Start your local ollama service at a separate terminal window (close this window after verifying downloads):

   Non-HPC users:

   ollama serve

   HPC Users Only:

   • Load the ollama module using module load ollama every time when opening a new terminal window:

     module load ollama
     ollama serve

   Back to your main terminal window - Download your reasoning model and your embedding model (more models):

   ollama pull gpt-oss:20b
   ollama pull mxbai-embed-large:335m

   Verify the downloads:

   ollama list

   # NAME                         ID              SIZE      MODIFIED
   # mxbai-embed-large:335m       468836162de7    669 MB    7 weeks ago
   # gpt-oss:20b                  17052f91a42e    13 GB     7 weeks ago

   (You may now close the terminal window that runs the Ollama server)

3. Install Python dependencies:

   (Optional) create a virtual environment with Python 3.12:

   python3.12 -m venv .venv
   source .venv/bin/activate

   Install packages:

   python -m pip install -r requirements.txt

4. Start Ollama server:

   For Non-HPC users:

   Every python script that utilizes LLM requires the hosting of an Ollama server. You may utilize these scripts to start/stop/check a server:

   python scripts/ollama/start_server.py
   python scripts/ollama/stop_server.py
   python scripts/ollama/status_server.py

   For HPC (Slurm) users only:

   Ollama server is managed using the shell script ./main_slurm.sh. It serves as a template with resource pre-sets. To run a Python LLM script through the Slurm system, use main_slurm.sh, passing the target script path as an argument:

   sbatch main_slurm.sh <SCRIPT.PY_PATH>

   Example:

   sbatch main_slurm.sh src/gsd/part1_textbook/01_ingest.py

   On successful job submission, you will find the logs at logs/slurm-<job-id>_output.txt and logs/slurm-<job-id>_error.txt.

   More on basic Slurm commands

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Usage

Workflow

Part 1: Term extraction from EoG and relations mapping

1. Creating ChromaDB from EoG documents

   unzip data/inputs/eog/raw_chapters/unzip_me_before_running_01_ingest.py.zip -d data/inputs/eog/raw_chapters/

   python src/gsd/part1_textbook/01_ingest.py

   • Or for HPC users: sbatch main_slurm.sh src/.../TargetScript.py

2. Extract terms from EoG documents (from vectorstore)

   python src/gsd/part1_textbook/02_extract.py

Varki A, Cummings RD, Esko JD, et al., editors. Essentials of Glycobiology [Internet]. 4th edition. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2022. Available from: https://www.ncbi.nlm.nih.gov/books/NBK579918/ doi: 10.1101/9781621824213

Part 2: Incoporating heterogeneous data sources and build a deduplicated master list of terms

This part builds a master dictionary of glycan structure terms by:

• Ingesting heterogeneous source term sets (Essentials of Glycobiology, legacy GSD v0, curated publications, composition lists, curator-supplied sets, etc.).
• Normalizing and formatting raw term JSONL inputs into a canonical intermediate structure.
• Creating a semantic vector store (Chroma + OpenAI embeddings) for retrieval-augmented AI mapping.
• Running AI-assisted mapping agents to (a) map synonyms to existing concepts or (b) propose creation of new canonical terms.
• Reconciling AI action logs into term-to-UUID mappings.
• Post-processing: merging multiple sources into consolidated node (master_nodes.json) and edge (master_edges.json) registries with quality checks and backups.

1. Build embeddings

   python src/gsd/part2_enrichment/1_ai-assisted_term_matching/01_create_vectordb.py

2. Run AI mapping for a source

   python src/gsd/part2_enrichment/1_ai-assisted_term_matching/02_ai_mapping_gsdv0.py

3. Reconcile mapping decisions

   python src/gsd/part2_enrichment/1_ai-assisted_term_matching/02_match_gsdv0_ai_mapping_with_uuid.py

   (Repeat analogous steps for pubdictionaries)

   python src/gsd/part2_enrichment/1_ai-assisted_term_matching/03_ai_mapping_pubdictionaries.py
   python src/gsd/part2_enrichment/1_ai-assisted_term_matching/03_match_pubdict_ai_mapping_with_uuid.py

4. Merge into master dictionaries

   python src/gsd/part2_enrichment/2_generate_mappings/postprocessing.py

Note

An OpenAI API key enables the application to access LLM services. Where to obtain an API key?

Project Structure

.
├── README.md
├── configs                       # YAML-based configuration for models, paths, and tooling
│   ├── base.yaml
│   ├── chroma.yaml               # Persist directories + retriever params
│   ├── models.yaml               # LLM labels + params
│   ├── ollama.yaml               # Ollama configs
│   ├── paths.yaml
│   ├── schemas                       # JSON/schema definitions for bGST data model
│   └── prompts                   # Collection of system prompts in markdown format
├── data
│   ├── inputs                    # Raw/normalized source data for the pipelines
│   │   ├── _resource_template    # Folder template for integrating new resources
│   │   │   ├── metadata
│   │   │   ├── normalized
│   │   │   └── raw
│   │   └── ...                   # Source data + merging audit records, grouped by folders
│   ├── outputs                   # Mapped terms (current/previous) + vectorstore snapshots (previous)
│   │   └── releases
│   └── workspace                 # Vectorstores of current release
│       └── chroma
├── docs                          # Supplementary documentation + notes
├── requirements.txt
├── scripts
│   └── ollama                    # Ollama server helpers (env var + pid management)
│       ├── start_server.py
│       ├── status_server.py
│       └── stop_server.py
├── src                           # Python library code for the GSD pipeline
│   └── gsd
│       ├── __init__.py
│       ├── adapters              # Higher level adapter tools
│       ├── part1_textbook        # EoG term extraction pipeline
│       ├── part2_enrichment      # GSD resource enrichment pipeline
│       ├── cli.py
│       ├── config.py             # Config loaders
│       ├── models.py
│       └── utils.py
└── tests                         # Unit tests

LLM Workflows

Workflow	Description	Directory
GST Extraction	Extracts and classifies GST from a preprocessed text document, and creates sentence-level citations as supporting evidence. Identify GST entity pairs (i.e. has_abbr, has_formula). Example parses Essentials of Glycobiology 4e as a Chroma document.	src/gsd/part1_textbook/02_extract/
RAG For Term Generation	Starts with deduplicated glycan structure terms. Retrieve top-k document chunks from the Essentials of Glycobiology 4e, and synthesize a term summary in terms of definition, cellular component, molecular function, and biological process.	src/gsd/part1_textbook/04_annotate
bGST Enrichment With New Datasets	Starts with a seed GST vectorstore (persistdirectory = src/data/workspace/chroma/gsd/). Parses query GST entities one at a time - searches against existing term entries from the vectorstore, and decides to i. _link query to existing entity or ii. register new entity. The vector store is dynamically updated in the iteration, whilst a list of AI term-linking audits is generated for human review (before incorporating into the production GST datasets).	src/gsd/part2_enrichment/02_link/

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Data

Data Source

Resource	URL	Entities	Notes
GlycoMotif	https://glycomotif.glyomics.org/	701	Secondary: Glydin, UniCarbKB, GlyTouCan, CCRC, GlyGen
Glydin	https://glycoproteome.expasy.org/epitopes/		Secondary: SugarbindDB, GlycoEpitope, Cummings, BioOligo-DB
SugarbindDB	https://sugarbind.expasy.org/	204
GlycoEpitope	https://www.glycoepitope.jp/	173	Also available at https://glycosmos.org/glycoepitope
Cummings	https://pubmed.ncbi.nlm.nih.gov/19756298/
BioOligo-DB	https://glyco3d.cermav.cnrs.fr/search.php?type=bioligo
Monosac-DB	https://glycopedia.eu/resources/presentation/
UniLectin3D	https://unilectin.unige.ch/unilectin3D/
GlycoMaple	https://glycosmos.org/glycomaple/Human

Data Model

Describe the core data model(s) used by this project, including how glycan structure terms are represented, stored, and linked to external resources.

• Primary storage: (e.g., JSONL, SQLite)

• Key entities:

Each source terms file (*terms.jsonl) after formatting should produce lines like:

{
    "lbl": "sialyl Lewis x",
    "term_uuid": "GSD:32e928fb-1550-5e0a-945f-2218ac79b83c",
    "gtc_id": [
      "G00054MO"
    ],
    "sources": [
      {
        "src_lbl": "sialyl Lewis x",
        "src": "SRC:EOG_VARKI_4E",
        "src_uuid": "SRC:66cc8ff8-5b05-4882-8c47-8ab4f036bed3"
      },
      {
        "src_lbl": "sialyl Lewis x",
        "src": "SRC:GSD_GLYGEN_V0",
        "src_uuid": "SRC:0e4ec742-01a0-4d61-b1fb-655f380ac009"
      },
      {
        "src_lbl": "sialyl Lewis x",
        "src": "SRC:PUBDICTIONARIES-GLYCAN-IMAGE",
        "src_uuid": "SRC:5c02589c-9c5e-489f-8863-e0bd2618d901"
      }
    ],
    "gsd_id": "GSD000151"
  },

Edges (*edges.jsonl) follow:

{
    "subj": "GSD:a7868da4-a6c2-4825-97b9-c86700b1c213",
    "pred": "is_a_related_synonym_of",
    "obj": "GSD:8ce1f4e6-8cbe-5167-8ece-a1cfc850d3a5",
    "comment": "GA1 is a related synonym of asialo-GM1"
  },

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License

MIT License. Copyright (c) 2025 GlyGen

See LICENSE for more details.

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Acknowledgements

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