Seqout

Resolve the runs grid's column filters against every run in a study, not just the 500-run preview, so the filters stay correct on large studies. Returns up to 500 matches; `capped` is true when more exist.

find_study_runs

List all samples for an ArrayExpress experiment accession (E-XXXX-NNN).

get_ae_experiment_samples

Return matching common names from the common_names table for a scientific name.

get_common_name_by_scientific_name

Batch variant of /common-name: pass scientific_name repeatedly to resolve a whole list in one request. Missing names are simply absent from the result (no 404).

get_common_names_by_scientific_names

Return distinct project accessions from unified_metadata for a given country, optionally filtered by organism and assay. Returns a plain newline-separated text list of accessions.

get_country_accessions

Study counts per organism, assay category, archive, year and single-cell status for one country. Precomputed, capped to the top 40 values per facet. Values feed the filters of /country/{code}/projects.

get_country_facets

Return investigators for a whole country, ranked by project count descending. This is the map sidebar's ranking and takes no topic. For any question that names a research topic ('single cell', 'CRISPR'), use `get_search_investigators` instead; do NOT pull this unfiltered list and filter it yourself. An investigator is any author of a project's linked publication, or a GEO/ArrayExpress contributor when the project has no linked PMID. Returns {pis, covered_projects, total, took_ms}.

get_country_pis

Rounded lat/lng clusters with study counts for this country's submitting institutions, for map rendering (e.g. the OG image). Built by seqout-ingestion's sql/matviews/create_country_matview.sh.

get_country_points

One row per study submitted from this country: organism, assay category, archive, sample/experiment counts, single-cell status, FASTQ and .sra availability (NULL means unknown), and publication year. Filters take values from /country/{code}/facets. `q` matches title or study_accession (case-insensitive substring). Sort with sort= and order=. `total` is the filtered study count; `count` is this page.

get_country_projects

Corpus-wide totals for studies submitted from one country (ISO-3166-1 alpha-2 code, e.g. IN, US), across every archive and technology.

get_country_summary

GARD and NORD names

get_disease_aliases

Study counts per disease category, clinical specialty and assay. Precomputed; the live join is ~80 s.

get_disease_facets

One row per study with its per-sample rollup: title, organism, sample and cell counts, assay category, both sex measurements, the catalogue disease it matched and the MONDO ancestry the enrichment recorded. A null cell count means unmeasured, and blank ancestry means unrecorded. `has_fastq` and `has_sra` are NULL when the study is absent from the download-links table, which means unknown, not unavailable; filtering on them accepts only true for that reason. `inheritance` is the mode of inheritance from HPO and Orphanet, plus GARD's own where it publishes one; a disease can carry more than one, and 'Not applicable' means the disease has no Mendelian pattern rather than that nobody looked. `ancestries` is the submitter-reported ethnicity field verbatim, not inferred genetic ancestry, and is present on 13% of studies. `n_samples` counts the samples that matched this collection, not the study's total, so controls and out-of-scope samples are not in it. `total` is the filtered study count; `count` is this page. Filters take values from /disease/{collection}/facets and are rejected if the collection does not carry them. `q` matches title or study_accession (case-insensitive substring). Sort with sort= and order=. `scope` defaults to human_primary (human material that is neither an immortalized line nor a derived model); use patient_derived_model, cell_line, or all to see what that excludes. Every row carries the full set of scope counters either way, so nothing is hidden. A free-text term (not `rare`/`nord`) is paginated by cursor: pass `cursor_sort`/`cursor_acc` from the previous page's `next_cursor` (null on the last page).

get_disease_projects

Corpus-wide totals for studies with at least one sample annotated with a disease in this collection, resolved through MONDO ids and their descendants. `rare` is NIH GARD, matched by exact MONDO xref; `nord` is NORD, matched by name and so a looser set.

get_disease_summary

Return the most common values (with counts) for one enriched column (e.g. tissue, cell_type, disease, assay). Reads the enriched_column_top_values materialized view.

get_enriched_column

Return how many projects and samples carry AI-enriched ontology metadata, split by archive (geo, sra, ena, arrayexpress, gsa, dra, gea), by organism, by assay and by assay_category. Archive totals are planner row estimates, so the percentages are approximate. A project with several organisms or assays counts under each, so those rows sum past the corpus total. The organism and assay lists are capped at limit; distinct_values carries the full counts. Reads the enriched_coverage materialized view.

get_enriched_coverage

Return counts of one standardised column (breakdown) within each value of another (group) — e.g. group=organism&breakdown=tissue gives the tissues seen per organism, with a distinct-tissue count per organism. Both axes must be in the curated crosstab column set. Reads the enriched_crosstab materialized view.

get_enriched_crosstab

Return how many projects and samples have AI-enriched ontology metadata, plus per-column fill count and distinct-value count. Reads the enriched_summary materialized view.

get_enriched_stats

Return experiment metadata (library, platform, instrument, parent study, linked samples) plus runs for an SRA or ENA experiment accession (SRX/ERX/DRX). Use /experiment/{accession}/runs to fetch just the runs.

get_experiment

Return the ontology-enriched sample record(s) for a single SRA/ENA experiment (SRX/ERX/DRX), scoped from the parent study's enriched metadata. Same fields as /project/{accession}/enriched.

get_experiment_enriched

Return per-run JSON metadata rows for a single SRA/ENA experiment (SRX/ERX/DRX), in the same row format as /project/{accession}/metadata/rows but scoped to this experiment's runs.

get_experiment_metadata_rows

Return run download links and layout for an SRX/ERX/DRX experiment.

get_experiment_runs

List all samples for a GEO series (GSE) or ArrayExpress experiment (E-XXXX-NNN).

get_geo_series_samples

Return distinct values for organism, assay_l1, and assay_l2 filters, ordered by count descending. When country is provided, counts are scoped to that country. Global and country-scoped results are cached for 7 days.

get_global_contribution_filters

Return aggregated project/experiment/sample counts per geographic location (latitude/longitude rounded to 2 decimal places) from the unified_metadata table. Includes source breakdown (GEO/SRA/ArrayExpress/ENA), city, country, and top 5 organisms per location. Unfiltered and filtered results are cached for 7 days.

get_global_contributions

Return monthly counts of projects, experiments, or data volume per database. modes: projects (studies/series count), experiments (samples count), bases (nucleotide bases for ENA, FASTQ/SRA bytes for SRA). Counts are per-month (not cumulative). Returns {mode, series: {db: [{month, count}]}, took_ms}.

get_growth_stats

Return the date of the newest record in the index. With ?db=<source> (geo|sra|arrayexpress|ena|gsa|dra|gea) returns that source's latest date; without it, the overall maximum. Cached for 7 days. Returns {db, last_updated, by_source}.

get_last_updated

Study counts per technology, platform, instrument model, library strategy, organism, archive, chemistry, has_exact_chemistry, assay category, year and long-read-only status. Values feed the filters of /longread/projects.

get_longread_facets

One row per study with a PacBio or Oxford Nanopore experiment: technologies, platforms, instrument models, library strategies, organisms, the archives it appears in, long-read experiment count against the study's total (`long_read_only` is NULL when no experiment rows exist to compare), sample and run counts, FASTQ and .sra availability (NULL means unknown), assay category, year, and `chemistries`/`n_chemistry_exact` (per-run PacBio/ONT chemistry, see /longread/summary). Filters take values from /longread/facets; `year` is the publication year. `chemistry=` filters on a value from the `chemistry` facet; `has_exact_chemistry=true` restricts to studies with at least one BAM-header-measured run. `q` matches title or study_accession (case-insensitive substring). Sort with sort= and order=. `total` is the filtered study count; `count` is this page.

get_longread_projects

Corpus-wide totals for studies with at least one PacBio or Oxford Nanopore experiment, across GEO, SRA, ENA, DDBJ DRA and GSA. A study mirrored in more than one archive counts once. `studies_hybrid` are studies that also sequenced on a short-read platform; `studies_long_read_only` had no other platform; the remainder have no experiment rows to judge by (GEO-only entries). `studies_exact_chemistry` had at least one run whose chemistry came from the submitted BAM's own header, not a model/date guess.

get_longread_summary

Return monthly and cumulative experiment/sample counts for a given organism across GEO, SRA, ArrayExpress, and ENA databases. The organism parameter should be a scientific name (e.g. 'Homo sapiens'). mode=absolute returns raw counts; mode=percentage returns the organism's share of total experiments per month per database. Returns {organism, mode, series: {db: [{month, count, cumulative}]}, took_ms}.

get_organism_growth

Return total experiment/sample counts per organism across GEO, SRA, ArrayExpress, and ENA. Optionally filter by publication year range. Returns {organisms: [{organism, geo, sra, arrayexpress, ena, total}], year_from, year_to, total_organisms, took_ms}.

get_organism_totals

Counts of samples scanned from the reads themselves: sex verdicts, assay calls, and validated microbial detections.

get_pentimento_overview

Study counts per perturbation type, confidence, data availability, genetic subtype, perturbation method, compound, organism, tissue, readout assay and year, top 100 values per facet. Values feed the filters of /perturbation/projects.

get_perturbation_facets

One row per single-cell study with genetic (CRISPR KO/i/a, RNAi, ORF, base/prime editing), chemical (small-molecule or biologic drug) or other-stimulus perturbation evidence. `perturbation_type` is genetic, chemical, both or other. `confidence` is high, medium or low: high needs a named method (Perturb-seq, CROP-seq, sci-Plex, ...) or corroborated guide libraries or a compound design with a control arm; low is a single weak signal. Use `min_confidence` for 'at least' (medium keeps high and medium); `confidence` matches one tier exactly. `evidence` lists the signals that fired. `data_availability` is both, matrix_only, fastq_only or neither, from `has_matrix` and `has_fastq`. `readout_assays` are the sample-level assay categories in the study. `compounds` are ChEBI-matched names from sample treatment values, empty for designs whose drug arms live in supplementary files (e.g. sci-Plex). `title_compounds` are compounds matched in the study's own title text, populated whenever the title matches, independently of whether `compounds` is also populated; title evidence alone never reaches medium/high, so a row whose only chemical evidence is title_compounds is chemical/low. `cell_lines` and `sample_types` describe the material (named cell lines; cell line/primary tissue/organoid/xenograft/iPSC-derived and similar from the sample metadata), not the perturbation. `is_long_read` marks a study that also has PacBio or Oxford Nanopore sequencing. Filters take values from /perturbation/facets; `year` is the publication year. `q` matches title or study_accession. `total` is the filtered study count; `count` is this page.

get_perturbation_projects

Corpus-wide totals for single-cell studies with genetic or chemical perturbation evidence (same population as /perturbation/projects). Detection is rule-based. `studies_high_medium` counts the studies whose evidence is strong enough to rely on.

get_perturbation_summary

Return available organisms, assays, and countries for a platform. Used to populate filter dropdowns in the sequencing technology stats card.

get_platform_filters

Return monthly experiment or project counts for a sequencing platform. Optionally filter by instrument_model. Returns {platform, mode, series: {db: [{month, count, cumulative}]}, took_ms}.

get_platform_growth

Return instrument models for a platform with experiment counts.

get_platform_instruments

Return total experiment counts per sequencing platform across databases. Also available at /platforms.

get_platform_totals