Biotechnology Data Provider: 24 Cataloged Datasets · Head-to-head
Human Protein Atlas vs NCBI Gene Expression Omnibus (GEO)
Which biotechnology data provider: 24 cataloged datasets data fits your job: Human Protein Atlas, or NCBI GEO. API, files, or your warehouse. Daily, weekly, or hourly.
Human Protein Atlas
NCBI GEO
Where the fields line up
No shared field names. These two answer different questions.
| Field | Human Protein Atlas | NCBI GEO |
|---|---|---|
Gene | documented | not in this set |
Ensembl | documented | not in this set |
Uniprot | documented | not in this set |
Protein class | documented | not in this set |
Evidence | documented | not in this set |
RNA tissue specificity | documented | not in this set |
RNA tissue specific nTPM | documented | not in this set |
RNA single cell type specificity | documented | not in this set |
Reliability (IH) | documented | not in this set |
Subcellular location | documented | not in this set |
Series_geo_accession | not in this set | documented |
Series_title | not in this set | documented |
Coverage, side by side
| Human Protein Atlas | NCBI GEO | |
|---|---|---|
| Geographic | Human biology - organism-level, not jurisdictional; comparative pig and mouse brain sections included alongside | Global submissions; organisms span all domains of life |
| Temporal | Complete numbered editions since 2005; current release version 25.1 (May 2026) built on Ensembl 109 | Unbroken sequence from 2000 to present, with per-record submission and revision dates |
| Granularity | One row per gene/protein, expression values hanging off it per tissue, cell type, cancer type and cell line | Per-sample (GSM) measurement rows grouped into per-study (GSE) experiments, with platform (GPL) designs beneath |
What each contains
Pick by fit, not by loyalty.
| Human Protein Atlas | NCBI GEO | |
|---|---|---|
| Publisher | Human Protein Atlas program at SciLifeLab, Sweden - a designated Global Core Biodata Resource funded by the Knut and Alice Wallenberg Foundation | National Center for Biotechnology Information (NCBI), archiving community submissions since 2000 |
| Subject lens | Where each human protein appears: tissue, cell type, cancer and subcellular localization, with antibody-based imaging, RNA-seq and mass spectrometry stacked on one gene-keyed schema | Community-submitted functional genomics: expression profiling, ChIP-seq binding, methylation, genome variation, non-coding RNA and protein profiling under MIAME/MINSEQE standards |
| Unit of analysis | One row per gene/protein, expression values hanging off it per tissue, cell type, cancer type and cell line | Per-sample (GSM) measurement rows grouped into per-study (GSE) experiments, with platform (GPL) designs beneath |
| Geographic coverage | Human biology - organism-level, not jurisdictional; comparative pig and mouse brain sections included alongside | Global submissions; organisms span all domains of life |
| Temporal coverage | Complete numbered editions since 2005; current release version 25.1 (May 2026) built on Ensembl 109 | Unbroken sequence from 2000 to present, with per-record submission and revision dates |
| Formats | TSV, XML and JSON exports, RDF search results, SVG schematics | SOFT (line-based ASCII), MINiML (XML), tab-delimited series matrix tables, tar archives of raw submitted files |
| Scale | 27,883 antibodies against 17,407 proteins; 15,312 genes with tissue staining; 13,603 with subcellular localization; 19,904 predicted structures | About 250,000 Series and 7.9M+ Samples; 143,551 sequencing-expression Series, 69,855 array-expression Series, 50,704 ChIP-seq Series |
| Best for | Target triage, tissue-restriction checks, biomarker localization, citable expression figures | Cohort discovery, signature reanalysis, model-organism work, raw-study provenance |
What each does better
Human Protein Atlas
Comparability no archive can offer. Every verdict comes from one pipeline - antibodies, RNA-seq, mass spectrometry - normalized into one schema, so a call on gene A means the same thing as a call on gene B. Immunohistochemistry reaches 15,312 genes across 45 normal tissue types and 20 cancer types; transcriptomics extends quantitative depth across 51 tissue types; every stain rides with a Reliability tier (Enhanced, Supported, Approved or Uncertain) so downstream filters can weigh the evidence rather than trust it blind.
Spatial resolution nothing else in the pairing attempts. Subcellular localization resolves 13,603 genes into 49 organelle classes; single-cell profiling spans 34 tissue types and 154 cell types; blood plasma profiling combines Olink Explore and SomaScan measurements; and predicted structures cover 19,904 proteins. Ask the atlas where a protein sits - which tissue, which cell type, which compartment - and the answer is a lookup.
Versioned, citable snapshots. Releases have run since 2005, each a complete frozen edition; the current one, version 25.1 built on Ensembl 109, arrived in May 2026. Pin an analysis to a release number and the gene coordinates underneath stay fixed - reproducibility the archive's continuous intake cannot promise.
NCBI GEO
Breadth the atlas does not attempt. Roughly 250,000 Series and more than 7.9 million Samples, led by 143,551 expression-profiling-by-sequencing Series, 69,855 array-based expression Series and 50,704 ChIP-seq binding Series, with 5,984,301 Samples linked back to raw sequencing runs. Any question that starts 'has anyone measured X in condition Y' has a quarter-million studies to interrogate here and effectively one curated species-width of preprocessed evidence in the atlas.
Every domain of life. GEO takes submissions from any organism; Human Protein Atlas profiles exactly one species, Homo sapiens, with comparative pig and mouse brain sections as context. Model organisms, crops, pathogens and non-human mammals live only in the archive.
Raw-study fidelity. GEO preserves each submitting laboratory's own design, protocol and processing - !Sample_ protocol and characteristics fields document how every measurement was made, and Platform tables retain the array designs behind them. The revision trail is visible too: the sampled Series GSE1000 went public on Jan 28 2004 and carries !Series_last_update_date Aug 10 2018, so staleness can be read off the record instead of assumed.
Assay diversity. Expression is the largest block, not the whole: methylation profiling, genome binding and occupancy, genome variation, SNP genotyping, non-coding RNA and protein profiling all sit in scope. The atlas measures protein location and transcript abundance; the archive holds whatever experiment met the reporting standard.
The verdict
Verdict: sample both, pick by fit. Let the unit of analysis decide.
If the question names a protein or a tissue - is this target restricted to heart muscle, where in the cell does it sit, how well validated is the stain, how does it behave across 20 cancer types - Human Protein Atlas is shaped for it, and its 10/10 score reflects a fixed, verified schema behind every verdict.
If the question names a study, a condition or an organism - benchmark cohorts for model training, disease-versus-control signatures, non-human species, assays beyond expression - NCBI GEO holds what the atlas does not attempt.
Three quick tests settle most cases. Need a validated spatial verdict? Only the atlas travels - no amount of archive searching produces a graded tissue map. Need the original cohort behind a published signature? Only the archive reaches it. Need the curated map and the underlying studies in one deliverable? That is the both-of-them case, and in translational work it comes up constantly.
Sample both, pick by fit. See Human Protein Atlas · See NCBI GEO
Or take both in one feed
Yes - they stack because they occupy different layers of the same system. A defensible workflow: let the atlas decide which targets matter - tissue-restricted, cancer-specific, enhanced-validated, sitting in a druggable compartment - then take those gene symbols into GEO to pull the studies that measured them in your condition of interest.
Two alignments decide whether the merge holds. First, identifiers: the atlas hands over Ensembl IDs and UniProt accessions, while GEO speaks its own accessions and submitter-authored text, so the join runs through gene identifiers matched against !Sample_ characteristics - read those characteristics before trusting any match. Second, symbols drift: HGNC names change over time, and a symbol-only match can pair two studies of the same gene that share no experimental design. Expect a partial join and treat it as one.
Browse the rest of the shelf at the biotechnology data hub.
Datadory ships either record alone or both merged onto one calendar, aligned on identifiers so the join lands already done - delivered daily, weekly, or hourly - your call. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Is Human Protein Atlas better than NCBI GEO?
Better at different jobs. The atlas owns the curated map: 27,883 antibodies against 17,407 proteins, staining evidence graded from Enhanced to Uncertain, subcellular addresses for 13,603 genes across 49 organelle classes. GEO owns breadth: roughly 250,000 series and more than 7.9 million samples spanning every domain of life. Sample both, pick by fit.
Do the two datasets cover the same ground?
Only partly. The overlap is human expression measurement, and even there they disagree by design: the atlas normalizes everything through one assay pipeline into one per-gene schema, while GEO preserves each submitting laboratory's own processing. Outside that overlap they diverge completely - spatial and subcellular protein evidence on one side, multi-organism study archives including methylation, binding and variation assays on the other.
Which dataset covers more organisms?
NCBI GEO, by an enormous margin: submissions span all domains of life, from bacteria through plants to human cohorts. Human Protein Atlas profiles one species, Homo sapiens, with comparative pig and mouse brain sections carried as cross-species context for the brain resource.
Which should anchor a drug-target prioritization screen?
Start with the atlas. Tissue restriction, cancer specificity and antibody-validation tiers turn 15,312 genes across 45 tissue types and 20 cancer types into a filterable shortlist. Then take the shortlist into GEO to pull the specific studies that measured those genes in your disease context. Reversing the order means reading hundreds of heterogeneous series before any target earns a verdict.
Can Datadory deliver both datasets together?
Yes - alone or merged onto one calendar, delivered daily, weekly, or hourly, your call. Each arrives normalized to its documented field dictionary (21 fields on the atlas side, 12 on the archive side) with sample rows for inspection before anything ships; the joining work is gene-identifier alignment against sample characteristics, handled in the merge. Or take both in one feed.