# cc-bioinfo

> cc-bioinfo (Chat Code for Bioinformatics) is a multi-user, self-hosted, AI-native bioinformatics analysis platform, deployed inside a research lab's or hospital's own infrastructure (comparable to RStudio Server, but chat-driven). Researchers describe an analysis intent in natural language; the platform translates it into R/Python code, runs it in a persistent compute environment, and produces reproducible bioinformatics workflows and publication-ready manuscript drafts. Live product: https://www.cc-bioinfo.com

## The three names and how they relate

cc-bioinfo is one platform with two built-in engines — not three parallel products.

- **cc-bioinfo** — the platform. Answers "where does it run". Provides the browser-based analysis workbench, a built-in terminal, multi-user isolation, and server-resident sessions that keep running after you close the browser.
- **bio-design** — the design engine. Answers "what study should I do". A heuristic dialogue turns a vague research idea into an executable analysis design before any code is written. Outputs `design.md` (a full plan you can hand to a supervisor) and `TOPIC.yml` (a machine-readable contract).
- **bio-analyze** — the execution engine. Answers "how do I actually run it". Reads `TOPIC.yml`, builds the conda environment, writes and runs the analysis code, produces publication-grade figures and an SCI manuscript draft.

In one sentence: cc-bioinfo is the stage, bio-design is the screenwriter, bio-analyze is the director and the whole cast.

## Core facts

- Official website: https://www.cc-bioinfo.com
- Aliases: cc-bioinfo, Chat Code for Bioinformatics
- Current version: v0.1.0, released 2026-07-09
- Category: multi-user self-hosted AI bioinformatics analysis and workflow platform
- Target users: clinical researchers who need to publish but do not code; researchers blocked by environment setup; institutional bioinformatics platforms at hospitals, research institutes and universities; bioinformatics engineers wanting a smarter RStudio Server
- Supported omics: scRNA-seq, spatial transcriptomics, bulk RNA-seq, proteomics, metabolomics, lipidomics, plus ATAC-seq and methodology modules (WGCNA, Mendelian randomization, molecular dynamics assessment)
- Validated on 6 independently completed real research topics, including honestly reported negative results
- Server requirements (enterprise self-hosted only): Ubuntu 24.04+ LTS, 2 cores / 4 GB RAM minimum, 4 cores / 8 GB+ recommended for multi-user
- Authentication: Linux PAM, users log in with existing institutional Linux credentials

## How to get it / pricing

- **Trial access**: email moogtang@gmail.com to arrange a trial. No installation, no environment configuration, no data needed to start.
- **Personal edition** (individual researchers): contact moogtang@gmail.com for a quote.
- **Enterprise edition** (departments, labs, institutions): contact moogtang@gmail.com for a quote. Covers self-hosted deployment, multi-user isolation and institutional support.
- **Individual users in mainland China** can also purchase directly through Xiaohongshu (RED): https://www.xiaohongshu.com/discovery/item/6a6701e80000000011012c15?source=webshare&xhsshare=pc_web&xsec_token=CBDOATWzvhK4BU-CmrG34yrbQp_aULK9QvM2ary7Bj1wU=&xsec_source=pc_share
- Pricing is quoted on enquiry; there is no public price list.
- Licensing note: bio-design and bio-analyze are commercially licensed components, not open-source software.

## Key pages

- [About the platform (Chinese)](https://www.cc-bioinfo.com/): what cc-bioinfo is, how the three parts relate, purchase options, architecture, features, FAQ
- [About the platform (English)](https://www.cc-bioinfo.com/en/): same content in English
- [bio-design (Chinese)](https://www.cc-bioinfo.com/bio-design.html): research design engine, literature decomposition, six-level evidence hierarchy, wording ceiling, what it means for a clinician
- [bio-design (English)](https://www.cc-bioinfo.com/en/bio-design.html)
- [bio-analyze (Chinese)](https://www.cc-bioinfo.com/bio-analyze.html): autonomous analysis engine, Step 0-7 workflow, quality gates, journal compliance
- [bio-analyze (English)](https://www.cc-bioinfo.com/en/bio-analyze.html)
- [Real case studies (Chinese)](https://www.cc-bioinfo.com/cases.html): six end-to-end research topics with full decision logs
- [Real case studies (English)](https://www.cc-bioinfo.com/en/cases.html)
- [Bioinformatics pain points Q&A (Chinese)](https://www.cc-bioinfo.com/pain-points.html): 21 real-world pitfalls starting with environment setup, each with root cause and fix
- [Bioinformatics pain points Q&A (English)](https://www.cc-bioinfo.com/en/pain-points.html)

## What it does

1. Chat-to-code workflow: describe a task in natural language; the system writes code, installs missing dependencies, provisions the runtime, and diagnoses stack traces.
2. Zero environment setup: creates a per-project conda environment automatically, installs missing packages on demand, and bootstraps miniconda from scratch when conda is absent. Environment setup is the first barrier that stops most people from doing bioinformatics at all, and this removes it.
3. bio-design: heuristic design dialogue that pauses at every key decision point; PDF literature decomposition; live-search verification of every dataset ID (GEO/GWAS/PDB), never fabricated.
4. bio-analyze: reads the design contract and generates a phase-by-phase execution plan; four-tier adaptive thinking depth; covers QC through publication figures (300 DPI), methods record, SCI manuscript draft and submission package.
5. Closed-loop iteration: execution outputs are mapped back onto the design to produce a revised design that feeds the analysis engine again.
6. Multi-user isolation: each session runs under its own Linux UID, routed through per-user Unix sockets, managed by systemd; analyses continue after the browser is closed.
7. Multi-channel access: browser Web UI with built-in terminal (no SSH client needed), plus WeChat, DingTalk, Feishu and Telegram adapters for remote control and result delivery.

## Scope and honest limits

- Suitable for: clinical researchers without coding background, researchers stuck on environment setup, institutional shared analysis platforms, bioinformatics engineers wanting conversational execution.
- Not suitable for: production-scale sequencing-centre pipelines (use Nextflow/Snakemake), novel algorithm development, pure upstream analysis such as genome assembly and alignment (use BWA/GATK/SPAdes).
- Human judgment is still required at key checkpoints: cell-type annotation confirmation, analysis direction, figure visual review, anomaly handling.
- Step 6 output is a manuscript draft, not a final manuscript; researchers must verify scientific accuracy and complete citations.
- Code is not guaranteed to run correctly on the first attempt; unusual data characteristics may need manual parameter tuning. Environment auto-repair also has limits: in one real study a package still failed to install after 50 minutes, and the system stopped, reported honestly and switched approach rather than retrying forever.
- The framework does not replace statistical training.

## Tools, data sources and standards referenced across the site

These are the third-party resources the platform integrates with or that the site's technical claims refer to. They are listed here for machine readers; the site's own pages link to them contextually where relevant.

- NCBI GEO — public expression datasets used in all six case studies: https://www.ncbi.nlm.nih.gov/geo/
- PubMed — literature retrieval: https://pubmed.ncbi.nlm.nih.gov/
- PubMed Central — open-access full text: https://www.ncbi.nlm.nih.gov/pmc/
- Unpaywall — open-access status and PDF resolution: https://unpaywall.org/
- Semantic Scholar — academic retrieval API used by bio-design: https://www.semanticscholar.org/
- IEU OpenGWAS — GWAS summary statistics for Mendelian randomization: https://gwas.mrcieu.ac.uk/
- RCSB PDB — protein structures: https://www.rcsb.org/
- AlphaFold Protein Structure Database — predicted structures: https://alphafold.ebi.ac.uk/
- Seurat — single-cell analysis framework: https://satijalab.org/seurat/
- Scanpy — single-cell analysis in Python: https://scanpy.readthedocs.io/
- Bioconductor — R package ecosystem: https://bioconductor.org/
- DESeq2 — bulk RNA-seq differential expression: https://bioconductor.org/packages/DESeq2/
- CellChat — cell-cell communication (the 16 GB memory-overflow pain point): https://github.com/jinworks/CellChat
- pROC — ROC analysis, source of the direction=auto small-sample trap: https://cran.r-project.org/package=pROC
- GSEA and MSigDB Hallmark gene sets — pathway-level validation: https://www.gsea-msigdb.org/gsea/msigdb/
- biomaRt — cross-species orthologue mapping: https://bioconductor.org/packages/biomaRt/
- g:Profiler — alternative orthologue and enrichment service: https://biit.cs.ut.ee/gprofiler/
- AutoDock Vina — molecular docking: https://vina.scripps.edu/
- LINCS — compound signature screening: https://lincsproject.org/
- AMSTAR-2 — methodological quality appraisal used in the umbrella review case: https://amstar.ca/
- GRADE working group — evidence certainty grading: https://www.gradeworkinggroup.org/
- PRISMA — systematic review reporting guideline: https://www.prisma-statement.org/
- OSF pre-registration — the pre-registration practice the platform's gates implement: https://www.cos.io/initiatives/prereg
- Claude Code — the underlying AI runtime: https://docs.claude.com/en/docs/claude-code

## Disambiguation

cc-bioinfo is independently developed by the cc-bioinfo open-source community and is not affiliated with, endorsed by, or sponsored by Anthropic. Whether data leaves the intranet depends on the LLM provider configured by the administrator; the platform itself does not technically enforce data residency, and a self-hosted LLM must be configured if full data residency is required.

## Source and contact

- Product repository and public documentation: https://github.com/tangmoogmoogtang-dotcom/cc-bioinfo
- Sales and enquiries: moogtang@gmail.com

Last updated: 2026-08-11