GenoMAS
A code-driven multi-agent framework for automated gene expression analysis workflows.
- Source repo
- Liu-Hy/GenoMAS
- Stars
- ★ 134
- Last updated
- 5mo ago
- License
- MIT
- Primary language
- Python
- FA score
- 51/100 · Major gaps
At a glance
- Works with
- Universal · cross-platformOpenAI API · Claude API
- You'll need
- Typical use
- A computational genomics researcher needs repeatable preprocessing and regression runs across many GenoTEX trait-condition pairs.
- Main limitation
- The full GenoTEX benchmark requires roughly 42 GB of input data; the documented estimate is 3–5 continuous days and more than $300 for all pairs.
- Source review
- 51/100 · Major gaps 1 safety controls not found
What does this agent do, and when should you use it?
GenoMAS combines a minimal multi-agent framework for scientific workflow automation with a specialized implementation for gene expression analysis. Its typed messaging and notebook-style workflow let agents plan, write code, execute it, debug, and backtrack across multi-step tasks. The specialized system analyzes transcriptomic datasets from GEO and TCGA to identify trait-related significant genes while accounting for confounders. Experiments run from the command line and write GenoTEX-structured preprocessing data, regression results, and logs under output/. Adoption requires local input data, a Python environment, and at least one provider credential, although local models can be run through Ollama.
Experiments are launched with python main.py, with configuration such as --version, --model, --api, and --data-root. The system reads GenoTEX input data, performs cohort preprocessing and regression analysis, and writes preprocessed cohort datasets to output/preprocess/{trait_name}/ and regression results to output/regress/{trait_name}/. Separate model and API-index settings are available for the Code Review, Domain Expert, Data Engineer, Statistician, and planning roles, while --parallel-mode cohorts can process cohorts concurrently. With --generate-action-units, it generates Action Unit prompts from agent guidelines, pauses for optional manual editing by default, and can proceed automatically with --non-interactive. It records activity in ./output/log_{version}.txt and supports checkpoint resume when the same interrupted command is rerun.
- A computational genomics researcher needs repeatable preprocessing and regression runs across many GenoTEX trait-condition pairs.
- A bioinformatics team working with GEO or TCGA transcriptomic data wants to identify trait-associated genes while accounting for confounders.
- A research engineer wants to evaluate heterogeneous model assignments for code review, domain expertise, data engineering, statistics, and planning.
- A team wants to validate cohort preprocessing on a few datasets before committing to the full statistical workflow, using --quick-test.
- A user constrained by API rate limits wants to reduce wall-clock time by processing a controlled number of cohorts with --parallel-mode cohorts.
How do you install or deploy this agent?
Create the documented environment and install dependencies:
conda create -n genomas python=3.10
conda activate genomas
pip install -r requirements.txtCopy the configuration template and provide at least one model-provider API key:
cp env.example .envOpenAI models also require an organization ID. Download the input data separately and place it under the data root; the documented default is ../data.
How do you use this agent?
Validate downloaded data first:
cd download
python validator.py --data-dir /path/to/data --validateThen run a basic experiment from the project root:
python main.py --version exp1 --model gpt-5-mini-2025-08-07 --api 1Add --data-root /path/to/data when the data is elsewhere. Use --quick-test for a small preprocessing-focused run. A local Ollama model can be selected with, for example, --model llama3.1.
What are this agent's strengths and limitations?
- It pairs a general multi-agent scientific-workflow framework with a concrete implementation for GEO and TCGA transcriptomic analysis.
- Its notebook-style execution model explicitly includes planning, code writing, execution, debugging, and backtracking instead of only producing recommendations.
- It supports both global model selection and role-level overrides for Code Review, Domain Expert, Data Engineer, Statistician, and planning.
- Its documented output layout, human-readable logs, and checkpoint resume make lengthy benchmark runs easier to inspect and continue.
- The full GenoTEX benchmark requires roughly 42 GB of input data; the documented estimate is 3–5 continuous days and more than $300 for all pairs.
- Cloud use requires at least one provider API key, OpenAI use additionally requires an organization ID, and parallel runs can stress API rate limits.
- Local deployment of large models has substantial GPU-memory and latency demands; the README states that DeepSeek-R1 671B requires multiple high-end GPUs.
- Full regression requires all related datasets to be preprocessed, so the documented quick-test path validates preprocessing rather than the complete analysis workflow.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| GenoMAS This agent | 51 · Major gaps | ★ 134 | 5mo ago | Python | OpenAI API · Claude API |
| GenoTEX Gene Expression Benchmark | 36 · Major gaps | ★ 65 | 4mo ago | Jupyter Notebook | — |
| BambooAI Data Analyst | 85 · Good | ★ 790 | 7d ago | Python | OpenAI API · Claude API |
| MiroFish Offline | 30 · Major gaps | ★ 2.5k | 6mo ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence: README requires users to configure API keys (.env), no hardcoded keys or excessive permissions found; --generate-action-units interactive mode pauses for manual editing and confirmation; output structure clear, logs traceable; no sensitive data handling details; dependencies not pinned, no security audit; external effects mainly API calls and local file writes, no malicious behavior; checkpoint resume mechanism provides rollback support; author identity clear in README and LICENSE. Deductions: dependencies not pinned, no security audit; sensitive data handling not specified; external effects not explicitly limited.
Evidence: README consistent with code structure, detailed parameter descriptions; dependency list exists but not pinned, may affect reproducibility; error handling has hints (e.g., model name errors list supported models), but no complete error message examples. Deductions: dependency versions not pinned, error message coverage limited.
Evidence: Targeted at scientific users, provides multiple usage scenarios (basic, heterogeneous models, open-source, parallel, AU generation); capability boundaries explained via parameters and role configuration; triggers clear (command-line arguments); environment requirements (Python 3.10, conda, API keys) clear. Deductions: no detailed system requirements (e.g., memory, GPU), limited environment fit.
Evidence: README structure complete, includes TOC, usage, output structure, troubleshooting; installation steps clear; naming stable (parameter names consistent); multiple examples and troubleshooting provided; known limitations (memory, timeout) documented; MIT license complete; no version number or changelog; maintenance responsibility not clear (no maintainer or contribution guidelines). Deductions: no version/changelog, maintenance responsibility unclear.
Evidence: Output structure follows GenoTEX convention, easy to use; benchmark results (F1 score) and cost estimates provided, showing marginal value; cost-benefit estimated ($300+ full run), but no detailed comparison. Deductions: cost-benefit analysis rough.
Evidence: README cites arXiv paper and benchmark, traceable; but no independent verification data; facts and inferences not clearly separated (e.g., performance claims without detailed experimental data). Deductions: insufficient cross-validation, facts and inferences mixed.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Dependencies are not pinned, which may introduce security vulnerabilities or compatibility issues.
- API key management relies on user configuration; ensure .env file security.
- Full run cost is high ($300+), evaluate resources carefully.
- No version number or changelog provided, making updates hard to track.