Below is a large, curated list (~100 resources) used by real clinical / translational biology researchers (cancer, immunology, aging, multi-omics, drug discovery, computational biology).

I grouped them by practical research workflow layers so it’s actually usable in real frontier biology research.

🧬 1. Core Global Biology Data Repositories (FOUNDATIONAL)

These are the primary data backbones for modern biology research.

Cancer / disease mega-datasets

  1. The Cancer Genome Atlas – multi-omics cancer dataset (20k+ tumors).
  2. COSMIC – somatic mutations in cancer.
  3. Cancer Genome Anatomy Project
  4. The Cancer Imaging Archive
  5. Network of Cancer Genes

👉 These datasets are the core backbone of computational oncology — TCGA alone produced petabytes of multi-omics data and transformed molecular cancer classification. (Cancer.gov)

Major functional genomics repositories

  1. NCBI GEO (Gene Expression Omnibus)
  2. ArrayExpress
  3. ENCODE
  4. GTEx
  5. SRA (Sequence Read Archive)
  6. BioProject
  7. BioSample

➡ GEO alone hosts millions of samples across 200k+ studies. (rna.cd-genomics.com)

Multi-omics integrated resources

  1. cBioPortal
  2. DepMap
  3. Human Protein Atlas
  4. ProteomicsDB
  5. TCGA Pan-Cancer Atlas

🧬 2. GitHub Curated Bioinformatics Resource Lists (START HERE)

These act as meta-indexes to thousands of tools.

  1. openbiox/awesome-bioinformatics
  2. mdozmorov/Immuno_notes
  3. OMICtools search engine
  4. Bioinformatics-papers list repos
  5. Biostar handbook repos

OMICtools alone indexes 18,000+ bioinformatics tools. (arXiv)

🧬 3. Cancer Research Toolchains (GitHub heavy)

Key software pipelines used in research labs.

Genomics analysis

  1. GATK
  2. MuTect2
  3. VarScan2
  4. Pindel
  5. Strelka

These are actually the exact variant callers used in TCGA pipelines. (gdc.cancer.gov)

RNA-seq workflows

  1. nf-core RNA-seq
  2. STAR aligner
  3. HISAT2
  4. Salmon
  5. kallisto
  6. DESeq2
  7. edgeR

Multi-omics integration

  1. DRPPM-EASY
  2. Cancer Multi-Omics Benchmark (CMOB)
  3. MultiAssayExperiment
  4. iClusterPlus

CMOB provides ready-processed datasets across 32 cancers. (arXiv)

🧬 4. Immunology-Specific Research Tools

Critical for immunotherapy & immune system modeling.

Repertoire sequencing

  1. Immcantation framework
  2. MiXCR
  3. AIRRflow

Immune deconvolution tools

  1. CIBERSORT
  2. TIMER
  3. xCell
  4. EPIC

Immunology datasets

  1. ImmPort
  2. IEDB (Immune Epitope Database)
  3. VDJdb

🧬 5. Single-Cell Biology Research Tools

A massive frontier area.

  1. Seurat
  2. Scanpy
  3. Monocle
  4. Cell Ranger
  5. Harmony
  6. CellPhoneDB

Single-cell datasets

  1. Human Cell Atlas
  2. Single Cell Portal
  3. PanglaoDB

🧬 6. Aging / Longevity Research Databases

Essential for geroscience.

  1. GenAge
  2. LongevityMap
  3. Human Ageing Genomic Resources
  4. Aging Atlas
  5. SenNet

🧬 7. Structural Biology & Protein Tools

Used in drug discovery & immunology.

  1. AlphaFold DB
  2. PDB (Protein Data Bank)
  3. Rosetta
  4. FoldX
  5. PyMOL

🧬 8. Drug Discovery & Pharmacogenomics Resources

Important in translational oncology.

  1. DrugBank
  2. ChEMBL
  3. LINCS L1000
  4. Open Targets Platform
  5. PharmGKB

🧬 9. Pathway & Systems Biology Tools

  1. KEGG
  2. Reactome
  3. STRING
  4. BioGRID
  5. Cytoscape
  6. GenMAPP

GenMAPP integrates gene-level datasets with pathways for disease analysis. (Wikipedia)

🧬 10. Machine Learning in Biology Repositories

Rapidly growing frontier.

  1. DeepChem
  2. BioBERT
  3. DNABERT
  4. ESM protein language models
  5. AlphaFold-multimer

🧬 11. Clinical Research & Translational Platforms

  1. ClinicalTrials.gov dataset APIs
  2. OHDSI / OMOP
  3. i2b2
  4. REDCap open tools

🧬 12. Imaging & Radiomics Resources

  1. TCIA radiomics tools
  2. PyRadiomics
  3. MONAI (medical AI)

🧬 13. Microbiome / Metagenomics Tools

  1. QIIME2
  2. Kraken2
  3. MetaPhlAn
  4. HUMAnN

🧬 14. Text Mining & Knowledge Graph Resources

  1. PubTator
  2. Europe PMC mining
  3. BioASQ datasets

🧬 15. Experimental Protocol Repositories

  1. Protocols.io
  2. Addgene plasmid repository
  3. Benchling open tools

🧠 Reality Check: How Frontier Biology Research Actually Works

A real clinical research workflow typically uses:

RAW DATA → GEO / TCGA
     ↓
Preprocessing → nf-core pipelines
     ↓
Analysis → Seurat / DESeq2
     ↓
Integration → Multi-omics frameworks
     ↓
Interpretation → Pathway / protein databases
     ↓
Translation → drug discovery resources