hitlist
A curated, harmonized, ML-training-ready MHC ligand mass-spectrometry dataset.
hitlist ingests immunopeptidome data from IEDB, CEDAR, and paper supplementary tables (PRIDE/jPOSTrepo); partitions MS-eluted observations from in-vitro binding-assay measurements into two separate parquet files (so downstream consumers never silently conflate them); joins every MS observation to expert-curated sample metadata (HLA genotype, tissue, disease, perturbation, instrument); and ships both indexes as parquet + a pandas-friendly Python API.
What's in the two indexes
After hitlist build observations (snapshot of the shipping 1.10.x default build):
observations.parquet — MS-eluted immunopeptidome
| Total observations (MS-eluted, all species) | 4,053,693 |
| Unique peptides | 1,285,987 |
| Unique MHC alleles | 691 |
| MHC species covered | 21 |
| IEDB rows | 3,986,991 |
| CEDAR rows | 595 |
| Supplementary rows | 66,107 |
binding.parquet — in-vitro binding-assay measurements
| Total binding rows (peptide microarray, refolding, MEDi, qualitative-tier) | 895,785 |
| Unique peptides | 258,199 |
The two indexes share the schema (including gene annotations from the peptide-mappings sidecar), but supplementary curation is MS-only — binding is pure IEDB/CEDAR.
Human MHC-I breakdown
| Observations | 2,672,046 |
| Unique peptides | 748,386 |
| Mono-allelic (exact allele) | 579,096 obs / 300K peptides / 119 alleles |
| Multi-allelic with allele match | 450,399 obs |
Multi-allelic with class-pool (N of M alleles) |
784,370 obs |
Allele-resolved sample_mhc coverage |
74.8% |
What's curated
Those numbers rest on per-study human curation. Rather than trust the raw
IEDB/CEDAR cell_name, disease, and tissue fields — which carry mislabels,
free-text placeholders, and Other/Unknown sentinels — hitlist annotates each
study (keyed by PMID) in pmid_overrides.yaml. Known mislabels are corrected,
and every MS sample is tagged with the metadata a training pipeline actually
needs (HLA genotype, tissue, disease, perturbation, instrument) plus a reference
proteome for flanking and source-protein attribution. A few papers whose peptides
never reached IEDB are ingested directly from their PRIDE / jPOSTrepo
supplementary tables.
| What's curated | Count | Detail |
|---|---|---|
Curated PMIDs (pmid_overrides.yaml) |
159 | Cover 89.5% of all observations |
↳ ms_samples with per-sample metadata |
633 | HLA genotype, tissue, disease, perturbation, instrument |
↳ ms_samples with 4-digit HLA typing |
446 | The subset with allele-level genotype |
| Supplementary CSVs ingested (PRIDE / jPOSTrepo) | 9 | 3 papers — listed below |
| Species reference proteomes | 22 | Ensembl ×4, UniProt ×18 |
| Viral reference proteomes | 33 viruses | 58 name aliases |
Supplementary papers ingested (data not in IEDB, read straight from PRIDE/jPOSTrepo — 9 CSVs total):
- Abelin 2019 — MAPTAC mono-allelic class I, class II, and DR-tissue (3)
- Gómez-Zepeda 2024 — JY, HeLa, Raji, SK-MEL-37, and plasma (5)
- Stražar 2023 — HLA-II (1)
Install
pip install hitlist
Quick start for ML training
# One-time: register IEDB + CEDAR downloads and build
hitlist data register iedb /path/to/mhc_ligand_full.csv
hitlist data register cedar /path/to/cedar-mhc-ligand-full.csv
hitlist build observations # a few minutes end-to-end;
# writes observations.parquet +
# binding.parquet + peptide_mappings.parquet
# Export training-ready CSVs
hitlist export training --include-evidence ms --class I --species "Homo sapiens" --mono-allelic \
--min-allele-resolution four_digit -o mono_allelic_classI.csv
hitlist export training --include-evidence ms --class II --species "Homo sapiens" \
-o classII_training.csv
# Presto-style flank-aware export: one row per (evidence row, peptide mapping)
hitlist export training --include-evidence both --class I --species "Homo sapiens" \
--explode-mappings -o presto_training.parquet
hitlist export training does not create a new canonical store. It composes the existing observations.parquet, binding.parquet, and peptide_mappings.parquet indexes into one training-facing export surface. The low-level indexes keep their semantic boundaries; the training export gives downstream consumers one obvious API/CLI path when they want model-ready tables.
Python API
hitlist build observations checks the source files and the curation inputs that define stored
annotations, including study overrides, tissue/cell-line metadata, and peptide-attribution CSVs.
Editing these inputs triggers a rebuild without --force. Upgrading from an older cache that
lacks curation fingerprints also triggers one rebuild.
Training-data export
from hitlist.export import generate_ms_observations_table
from hitlist.export import generate_training_table
# Mono-allelic human class I MS observations with ground-truth allele
mono_ms = generate_ms_observations_table(
mhc_class="I",
species="Homo sapiens",
is_mono_allelic=True,
min_allele_resolution="four_digit",
)
# Presto-style mapping-aware export: one row per (evidence row, peptide mapping)
presto = generate_training_table(
include_evidence="both",
mhc_class="I",
species="Homo sapiens",
map_source_proteins=True,
)
# columns now include: evidence_kind, evidence_row_id, protein_id, position,
# n_flank, c_flank, proteome, proteome_source
generate_observations_table() remains available as a backward-compatible alias.
Gene queries can mix symbols and Ensembl IDs: gene=["PRAME", "ENSG00000198681"] selects
peptides matching either gene. An additional peptide= filter narrows that union. Mapping-expanded
training exports retain only mappings for the selected genes, even when a peptide also maps to
an unrelated gene. Separately supplied gene_name and gene_id filters on the low-level loaders
remain conjunctive.
Allele-set filters use the same normalization in raw loaders and exports: mhc_allele_in_set="A*02:01"
and mhc_allele_in_set="HLA-A*02:01" select the same evidence. Explicit empty allele-set queries
raise ValueError consistently.
Species filters accept any variant — "Homo sapiens", "human", "homo_sapiens", "Homo sapiens (human)" all work.
MS, binding, training, and peptide-summary exports support independent source_species and
host_species filters, plus exclude_chimeric=True. The existing species filter selects MHC
species. For example, generate_training_table(species="human", source_species="mouse") selects
mouse-source peptides presented on human MHC. The matching CLI flags are --source-species,
--host-species, and --exclude-chimeric; quote multiword species names. Defaults retain all
species and chimeric systems. Source filtering and system flags both fall back to the raw
species column when source_organism is blank; raw source annotations remain available.
Raw observations loading
from hitlist.observations import (
load_ms_observations, # MS-eluted immunopeptidome
load_binding, # in-vitro binding-assay measurements
load_all_evidence, # union, tagged with an evidence_kind column
is_built, is_binding_built,
observations_path, binding_path,
)
# MS-elution (the default training-data path)
df = load_ms_observations() # everything (MS-eluted only)
df = load_ms_observations(mhc_class="I") # class I only
df = load_ms_observations(species="Homo sapiens") # human only
df = load_ms_observations(source="iedb") # filter by source
df = load_ms_observations(columns=["peptide", "mhc_restriction", "src_cancer"])
# Binding assays — same filter API, reads binding.parquet
bd = load_binding(mhc_class="I", mhc_restriction="HLA-A*02:01")
# Union — for affinity-predictor training, or UI flags that want both.
# Rows are tagged with evidence_kind ∈ {"ms", "binding"}.
both = load_all_evidence(gene_name="PRAME", mhc_class="I")
both["evidence_kind"].value_counts()
load_observations() remains available as a backward-compatible alias.
Building / curation
from hitlist.builder import build_observations
from hitlist.curation import (
classify_ms_row,
normalize_species,
normalize_allele,
load_pmid_overrides,
)
from hitlist.supplement import scan_supplementary, load_supplementary_manifest
build_observations(with_flanking=True, use_uniprot_search=True, force=False)
normalize_species("human") # → "Homo sapiens"
normalize_allele("H-2Kb") # → "H2-K*b"
scan_supplementary() # DataFrame of curated paper-supplement peptides
Peptide → protein attribution and flanking context
hitlist build observations always produces three parquet files (use --no-mappings to skip peptide_mappings.parquet):
~/.hitlist/observations.parquet— one row per assay observation~/.hitlist/binding.parquet— one row per binding-assay observation~/.hitlist/peptide_mappings.parquet— one row per (peptide, protein, position)
The mappings sidecar preserves multi-mapping so a peptide shared by MAGEA1/A4/A10/A12 keeps every paralog. Ensembl mappings include gene_biotype: the default index covers ordinary protein_coding genes plus the coding IG_V/D/J/C_gene and TR_V/D/J/C_gene biotypes, while excluding pseudogenes. These receptor records are germline segments; Ensembl does not contain a donor's recombined receptor, so peptides spanning a V(D)J junction cannot map. Observations additionally carry semicolon-joined identity columns:
| column | example |
|---|---|
gene_names |
MAGEA4;MAGEA10 |
gene_ids |
ENSG00000147381;ENSG00000124260 |
protein_ids |
P43359;P43363 |
n_source_proteins |
2 |
from hitlist.observations import load_ms_observations
from hitlist.mappings import load_peptide_mappings
# Central columns — fast for everyday filters (uses mappings sidecar for pushdown)
df = load_ms_observations(gene_name="PRAME")
# Long form for paralog / position / flank analysis
mappings = load_peptide_mappings(gene_name="MAGEA4")
# Receptor-derived mappings remain distinguishable from ordinary proteins
receptor_mappings = load_peptide_mappings(
gene_biotype=["IG_V_gene", "IG_D_gene", "IG_J_gene", "IG_C_gene",
"TR_V_gene", "TR_D_gene", "TR_J_gene", "TR_C_gene"]
)
# columns include: peptide, protein_id, gene_name, gene_id, gene_biotype,
# transcript_id, position, n_flank, c_flank, proteome
For ad-hoc queries without building the full table:
from hitlist.proteome import ProteomeIndex
idx = ProteomeIndex.from_ensembl(release=112, species="human") # coding + germline IG/TR
flanking = idx.map_peptides(["SLLMWITQC", "GILGFVFTL"], flank=10)
# Explicit compatibility mode for the historical protein-coding-only index:
ordinary_only = ProteomeIndex.from_ensembl(release=112, biotype="protein_coding")
Proteome registry / UniProt resolution
from hitlist.downloads import (
lookup_proteome, # org string → registry entry (dict)
fetch_species_proteome, # download FASTA and cache to ~/.hitlist/proteomes/
resolve_proteome_via_uniprot, # direct UniProt REST lookup
list_proteomes, # manifest section
)
lookup_proteome("Mycobacterium tuberculosis")
# → {'kind': 'uniprot', 'proteome_id': 'UP000001584', ...} # H37Rv reference
Output schema — generate_ms_observations_table()
| Column | Meaning |
|---|---|
peptide |
Amino acid sequence |
mhc_restriction |
Allele from IEDB (may be "HLA class I" for multi-allelic studies) |
sample_mhc |
Allele(s) known for the source sample — the useful field for training |
mhc_class |
Canonical I, II, or non-classical; molecule-derived when possible |
mhc_class_reported |
Source-reported class, retained verbatim for auditability |
mhc_class_source, mhc_class_corrected |
Whether class came from one molecule, a consistent donor set, or source fallback; whether it corrected the source |
mhc_species |
Canonical MHC species (mhcgnomes plus explicit per-study context for ambiguous names) |
mhc_species_source, mhc_species_context_disagrees |
Species-resolution provenance and explicit context-conflict signal |
restriction_evidence |
How the named peptide-to-MHC restriction was established: experimental, monoallelic, predicted, or unknown |
is_monoallelic |
True if sample has a single transfected allele (721.221, C1R, K562, MAPTAC…) |
has_peptide_level_allele |
True if mhc_restriction is a specific allele (not "HLA class I") |
is_potential_contaminant |
True for MS-eluted peptides that failed NetMHCpan binding prediction |
sample_match_type |
How sample_mhc was populated (see below) |
matched_sample_count |
Number of curated samples for this PMID |
src_cancer, src_healthy_tissue, src_ebv_lcl, ... |
Mutually-exclusive biological source categories |
source |
iedb, cedar, or supplement |
source_organism, reference_title, cell_name, source_tissue, disease |
IEDB sample context |
instrument, instrument_type, acquisition_mode, fragmentation, labeling, ip_antibody |
MS acquisition from ms_samples curation |
gene_names, gene_ids, protein_ids, n_source_proteins |
Multi-mapping peptide → source-protein attribution (always populated; use peptide_mappings.parquet for long-form positions + flanks) |
sample_match_type — join provenance
| Value | Meaning | Training-grade? |
|---|---|---|
allele_match |
IEDB recorded a specific allele and it matched a curated sample genotype | Yes — high confidence |
single_sample_fallback |
IEDB class-only but study has exactly 1 sample, so sample_mhc = that sample's full genotype |
Yes (for deconvolution) |
pmid_class_pool |
IEDB class-only + multiple samples — sample_mhc = union of all class-matching alleles across samples |
Yes (for deconvolution), lower precision |
unmatched |
No curated sample for this PMID, or all samples have mhc: unknown |
No — sample_mhc empty |
Biological source classification
Every observation is classified by mutually-exclusive biological source category:
| Category | Flag | Rule |
|---|---|---|
| Cancer | src_cancer |
Tumor tissue, cancer patient biofluids, or non-EBV cell lines |
| Adjacent to tumor | src_adjacent_to_tumor |
Surgically resected "normal" tissue (per-PMID override) |
| Activated APC | src_activated_apc |
Monocyte-derived DCs/macrophages with pharmacological activation |
| Healthy somatic | src_healthy_tissue |
Direct ex vivo, healthy donor, non-reproductive, non-thymic |
| Healthy thymus | src_healthy_thymus |
Direct ex vivo thymus (expected for CTAs, AIRE-mediated) |
| Healthy reproductive | src_healthy_reproductive |
Direct ex vivo testis, ovary (expected for CTAs) |
| EBV-LCL | src_ebv_lcl |
EBV-transformed B-cell lines |
| Cell line | src_cell_line |
Any cultured cell line |
Cancer-specific = src_cancer AND NOT src_healthy_tissue. Thymus, reproductive tissue, adjacent tissue, EBV-LCLs, and activated APCs do NOT disqualify a peptide from being cancer-specific.
CLI reference
Data management
hitlist data register <name> <path> [-d DESCRIPTION] # register a local file
hitlist data fetch <name> [--force] # download a known dataset (IEDB/CEDAR/viral FASTAs)
hitlist data refresh <name> # re-download
hitlist data info <name> # detailed metadata (JSON)
hitlist data path <name> # print the registered path
hitlist data remove <name> [--delete] # unregister (optionally delete file)
hitlist data list # show registered datasets
hitlist data available # show all known datasets
Build the observations table
hitlist build observations [--force] # ~90s full scan with tqdm progress
hitlist build observations # always builds peptide_mappings.parquet
hitlist build observations --use-uniprot # broader proteome coverage via UniProt REST
hitlist build observations --no-mappings # skip mapping step (faster, no gene attribution)
hitlist build observations --no-fetch-proteomes # don't auto-download missing proteomes
hitlist build observations --proteome-release 112 # Ensembl release for human/mouse/rat
Proteome management
hitlist data fetch-proteomes [--min-observations N] [--use-uniprot] [--force]
hitlist data list-proteomes
Export
hitlist export ms [filters...] -o train.csv # MS immunopeptidome + sample metadata
hitlist export ms -o train.parquet # parquet output supported
hitlist export peptide-summary --gene PRAME --serotype A24 # per-peptide support for one allele/serotype
hitlist export binding [filters...] -o binding.csv # binding-assay index (separate from MS)
hitlist export training [filters...] -o training.csv # unified training export from canonical indexes
hitlist export peptide-counts --by class # species x class peptide counts
hitlist export peptide-counts --by study # peptide counts per study/PMID
hitlist samples [--class I|II] # per-sample conditions (promoted from `export samples`)
hitlist qc normalization # validate YAML alleles with mhcgnomes
hitlist qc mhc-tokens # unparseable MHC tokens across all modalities
hitlist qc resolution # allele-resolution histogram for IEDB/CEDAR
hitlist export ms is the canonical name for the MS observations export; hitlist export observations remains as a backward-compatible alias.
Canonical indexes and the training export
Each hitlist build observations writes three parquet files to ~/.hitlist/:
observations.parquet— MS-eluted immunopeptidome (IEDB + CEDAR + curated supplementary).binding.parquet— binding-assay rows (peptide microarray, refolding, MEDi, and quantitative-tier measurements likePositive-High/Intermediate/Low).peptide_mappings.parquet— long-form peptide → protein/position/flank mappings.
The canonical indexes are never silently mixed. Supplementary data is MS-only.
Use hitlist export observations and hitlist export binding when you want
the raw evidence families separately. Use hitlist export training or
generate_training_table(...) when you want a composed model-facing export
with evidence_kind tagging and optional mapping explosion for flank-aware
training pipelines.
Filters on hitlist export observations
| Flag | Values |
|---|---|
--class |
I, II, non classical |
--species |
Any species variant (normalized via mhcgnomes) |
--mono-allelic / --multi-allelic |
Filter on is_monoallelic |
--instrument-type |
Orbitrap, timsTOF, TOF, QqQ, ... |
--acquisition-mode |
DDA, DIA, PRM |
--min-allele-resolution |
four_digit, two_digit, serological, class_only |
--mhc-allele |
Exact match on mhc_restriction after allele normalization. Repeatable / comma-separated. |
--restriction-evidence |
Restriction evidence: experimental, monoallelic, predicted, or unknown. Independent of allele-set provenance. Repeatable. |
--gene |
Symbol, Ensembl ID, or old alias (HGNC synonym lookup). Repeatable / comma-separated. Requires the mappings sidecar (default-on at build). |
--gene-name |
Exact match on gene_name column (no HGNC lookup) |
--gene-id |
Exact match on gene_id column (ENSG) |
--peptide |
Exact match on peptide sequence. Repeatable / comma-separated. |
--serotype |
HLA serotype: locus-specific (A24, B57, DR15) or public epitope (Bw4, Bw6). Matches any serotype the allele belongs to, so --serotype Bw4 returns A*24:02, B*27:05, B*57:01, etc. Split serotypes are rolled into their broad parent, so --serotype A24 also matches A*24:03 (serotype A2403). Repeatable / comma-separated. |
--exclude-class-label-suspect |
Drop rows where the curated class disagrees with peptide length severely enough to be flagged suspect or implausible (mhc_class_label_severity). |
--exclude-class-label-implausible |
Strict-cleaning variant — drops only implausible rows (class-I ≥18aa or ≤7aa, class-II ≤4 or ≥45aa). Keeps borderline + suspect tiers, useful when bulged class-I 15-17aa peptides should be retained. |
--apm-only |
Filter to peptide rows from samples where any APM gene was perturbed (apm_perturbed=True). Reflects the sample's own condition; the parent study's perturbation panel is carried separately in study_apm_perturbed / study_apm_genes. |
--output / -o |
.csv or .parquet |
All filters are pushed down to the parquet reader (pyarrow), so --gene PRAME reads
only the matching row groups — typically milliseconds rather than a full table scan.
Examples:
hitlist export ms --gene PRAME --class I -o prame_classI.csvhitlist export ms --gene "MART-1"(HGNC resolves toMLANA)hitlist export ms --mhc-allele HLA-A*02:01 --mono-allelichitlist export ms --serotype A24(locus-specific)hitlist export ms --serotype Bw4(public epitope — A23/24/25/32, B13/27/44/51/52/53/57/58)
hitlist export peptide-summary
Collapses the MS observations to one row per peptide, scoring how strongly each peptide is supported on a single target allele or serotype. Answers questions like "which PRAME peptides might be presented on A24 in cancers?"
hitlist export peptide-summary --gene PRAME --serotype A24 -o prame_a24.csv
hitlist export peptide-summary --gene PRAME --mhc-allele HLA-A*24:02
Requires exactly one of --mhc-allele / --serotype (a single value) plus a
gene/peptide scope filter. Each peptide's support is split into ranked tiers,
strongest first:
| Bucket column | Meaning |
|---|---|
n_mono_exact_rows |
Mono-allelic elution on the exact target allele (strongest) |
n_multi_exact_rows |
Multi-allelic elution including the exact target allele |
n_mono_serotype_rows / n_multi_serotype_rows |
Elution on a same-serotype allele |
n_class_only_sample_allele_rows |
Class-only row whose sample genotype carries the target allele |
n_class_only_sample_serotype_rows |
Class-only row whose sample genotype carries a same-serotype allele |
n_unknown_allele_rows |
Class-only row with no usable sample genotype (weakest) |
best_support names the strongest tier with any rows. A cancer / healthy /
adjacent / other source breakdown (n_cancer_rows, ...) comes along for free,
so you can prioritize peptides seen in tumors over healthy tissue.
Filters on hitlist export binding
Same shape as the observations filters minus the MS-specific ones
(--mono-allelic, --instrument-type, --acquisition-mode). --source
accepts only iedb or cedar — supplementary data is MS-only and never
appears in the binding index.
hitlist export binding --gene PRAME --class I -o prame_binding.csv
hitlist export binding --mhc-allele HLA-A*02:01 --serotype Bw4
Filters on hitlist export training
hitlist export training exposes the shared pMHC filters plus two export-shape controls:
--include-evidence ms|binding|bothchooses which canonical evidence families to compose.--explode-mappingsexpands the output to one row per(evidence row, peptide mapping)withprotein_id,gene_biotype,position,n_flank,c_flank,proteome, andproteome_source.
MS-specific filters (--mono-allelic, --instrument-type, --acquisition-mode) apply only to the MS slice. Binding rows never gain fake sample context; they remain tagged as evidence_kind="binding" with sample_match_type="not_applicable".
hitlist export training --include-evidence both --gene PRAME --class I -o prame_training.csv
hitlist export training --include-evidence ms --mono-allelic --class I -o mono_ms.csv
hitlist export training --include-evidence both --explode-mappings -o presto_training.parquet
Sample-level expression anchors (issue #140)
For line-like ms_samples (C1R, 721.221, JY and sibling EBV-LCLs, HAP1,
HeLa, HEK293, THP-1, SaOS-2, A375, K562, GM12878, and common engineered
derivatives) hitlist.line_expression resolves every sample to a stable
expression backend + key via a 6-tier fallback hierarchy:
- exact-line RNA / transcript quant (registry hit with shipped data)
- parent-line / engineered-derivative RNA (e.g. HeLa.ABC-KO → HeLa)
- line-family class anchor (EBV-LCL → GM12878; mono-allelic host → K562)
- cancer-type surrogate (caller-supplied
pirlygenesbackend) - broad tissue / lineage surrogate (HPA)
no_expression_anchor
Four provenance columns — expression_backend, expression_key,
expression_match_tier, expression_parent_key — are written on every
row so downstream tooling can distinguish "exact JY RNA" from "generic
EBV-LCL stand-in" from "melanoma cohort surrogate" instead of treating
them as equally trustworthy.
CLI:
hitlist export samples --with-expression-anchors -o sample_anchors.csv
hitlist export training \
--include-evidence ms --class I --mono-allelic \
--with-peptide-origin \
-o training_with_origin.parquet
hitlist export line-expression --line-key GM12878 --gene-name TP53
--with-peptide-origin attaches, per row, the argmax-TPM candidate gene
(peptide_origin_gene, peptide_origin_tpm) for the peptide in the
sample's resolved line. When transcript-level TPM is available the
score is the sum across only those transcripts whose translation
actually contains the peptide — isoforms that splice out the peptide
contribute zero, and a transcript is counted once regardless of how many
times the peptide appears in its protein.
Register DepMap matrices to broaden exact-line coverage:
hitlist data register depmap_rna /path/to/OmicsExpressionProteinCodingGenesTPMLogp1.csv
hitlist data register depmap_rna_transcript /path/to/OmicsExpressionTranscriptsTPMLogp1.csv
hitlist build observations
A note on mono-allelic curation
Mono-allelic is a PMID-level flag, not a per-sample property. Curation
lives in pmid_overrides.yaml (see mono_allelic_host and ms_samples).
Rows can legitimately have is_monoallelic=True with an empty
mhc_restriction — for example, supplementary contaminant peptides under
a mono-allelic PMID override carry the flag but not a per-row allele. If
your downstream pipeline needs a strict "mono-allelic AND has allele"
subset, post-filter on
is_monoallelic & mhc_restriction.str.startswith("HLA-").
Reports
hitlist report [--class I|II] [--output report.txt]
QC diagnostics
hitlist qc # run all checks, print summary
hitlist qc resolution # allele-resolution histogram
hitlist qc normalization # YAML alleles whose normalize_allele output drifts
hitlist qc mhc-tokens # invalid/parser-gap/unknown MHC tokens across modalities
hitlist qc cross-reference # alleles in YAML but not data, and reverse
hitlist qc discrepancies [--by sample] # per-PMID curation drift signals
hitlist qc plan [--top 10] # ranked next-PMID-to-curate roadmap
hitlist qc proteome-coverage # per-source-organism proteome registry coverage
hitlist qc proteome-coverage --missing-only --min-rows 100
Development
./develop.sh # install in dev mode
./format.sh # ruff format
./lint.sh # ruff check + format check
./test.sh # pytest with coverage (~3 min)
./deploy.sh # lint + test + build + upload to PyPI
See docs/pmid-curation.md for the curation YAML format and per-study overrides.
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Details for the file hitlist-1.58.5-py3-none-any.whl.
File metadata
- Download URL: hitlist-1.58.5-py3-none-any.whl
- Upload date:
- Size: 2.1 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.12.6
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