license: other
license_name: embl-ebi-terms-of-use
license_link: https://www.ebi.ac.uk/about/terms-of-use/
task_categories:
- feature-extraction
tags:
- proteomics
- mass-spectrometry
- peptide-sequencing
pretty_name: InstaNovo-FM training corpus
configs:
- config_name: hcfm_splits
default: true
data_dir: splits/hcfm
- config_name: mcfm_splits
data_dir: splits/mcfm
- config_name: lcfm_splits
data_dir: splits/lcfm
- config_name: hcfm_by_project
data_files:
- split: full
path: by_project/hcfm/**/*.parquet
- config_name: mcfm_by_project
data_files:
- split: full
path: by_project/mcfm/**/*.parquet
- config_name: lcfm_by_project
data_files:
- split: full
path: by_project/lcfm/**/*.parquet
- config_name: peptide_registry
data_files:
- split: full
path: peptide_registry.parquet
InstaNovo-FM training corpus
Tandem mass spectra with peptide-spectrum-match labels, uniformly reprocessed from public PRIDE submissions, used to pretrain and evaluate InstaNovo-FM.
Layout
Spectra are grouped into three confidence tiers. Each is a Foundational Model dataset named for how stringently its peptide-spectrum matches (PSMs) were filtered: LCFM (Low), MCFM (Medium) and HCFM (High). The names are relative, not absolute — every labelled tier consists of high-confidence PSMs, and "low confidence" marks LCFM as the broadest, least stringently filtered one, from which the stricter subsets are derived. They are nested: HCFM ⊂ MCFM ⊂ LCFM. A fourth tier, ACFM (All Confidence), is the unlabelled superset and is not published here.
Each tier is published twice, once under splits/ and once under by_project/.
Which you want depends on whether you are consuming the corpus or rebuilding it:
splits/— the train/validation/test partitions the model actually consumed: quality-filtered, shuffled, and peptide-disjoint. Take this to reproduce or extend the published results.by_project/— the same tier before filtering and splitting, one directory per PRIDE accession. Take this to apply your own quality criteria or derive your own partitions, whichsplits/cannot support because the filtering is lossy.
The tier sits inside the folder rather than above it — splits/lcfm/, not
lcfm/splits/. That nesting is deliberate: it means a download pattern like --include "by_project/*" cannot stray outside the folder you named. Nested the other way,
lcfm/* would have matched both folders at once and quietly handed you ~900 GB
containing two overlapping copies of the same spectra, one filtered and one not.
InstaDeepAI/InstaNovo
│
├── splits/ FILTERED · SHUFFLED · PEPTIDE-DISJOINT
│ ├── hcfm/ 11 files · 11.4 GB · 3,670,113 rows
│ │ ├── hcfm-train-00000-of-00007.parquet … 00006-of-00007
│ │ ├── hcfm-validation-00000-of-00001.parquet
│ │ └── hcfm-test-00000-of-00003.parquet … 00002-of-00003
│ ├── mcfm/ 44 files · 52.5 GB · 18,255,265 rows
│ │ ├── mcfm-train-00000-of-00029.parquet … 00028-of-00029
│ │ ├── mcfm-validation-00000-of-00001.parquet
│ │ └── mcfm-test-00000-of-00014.parquet … 00013-of-00014
│ └── lcfm/ 454 files · 467.1 GB · 181,777,591 rows
│ ├── lcfm-train-00000-of-00293.parquet … 00292-of-00293
│ ├── lcfm-validation-00000-of-00019.parquet … 00018-of-00019
│ └── lcfm-test-00000-of-00142.parquet … 00141-of-00142
│
├── by_project/ COMPLETE TIER · NOT FILTERED · NOT SPLIT
│ ├── hcfm/ 15,166 files · 11.4 GB · 3,684,448 rows
│ │ ├── PXD000561/ 82 accessions in every tier
│ │ │ ├── Adult_Adrenalgland_Gel_Elite_49_f01.parquet
│ │ │ └── … one file per instrument run
│ │ └── PXD000865/ …
│ ├── mcfm/ 15,244 files · 51.7 GB · 18,422,236 rows
│ │ └── PXD000561/ … same runs, fewer rows each
│ └── lcfm/ 15,286 files · ~450 GB · 184,607,213 rows
│ └── PXD000561/ …
│
├── peptide_registry.parquet 5,613,657 peptides and their split
│
└── manifests/
└── empty_runs.csv 162 runs with no PSMs at their threshold
Pick one tier, from one folder. Because the tiers are nested, a second tier
re-downloads the same spectra at a stricter threshold — and splits/* or
by_project/* fetches all three, roughly 531 GB and 510 GB.
splits/ — start here
train, validation and test parquet, exactly as the model consumed them. The
partitions are peptide-disjoint 80/10/10: a peptide sequence appears in only one
of the three, so evaluation does not reward memorisation. Rows are shuffled and have
passed the quality filters below.
Rows per split, measured:
| train | validation | test | |
|---|---|---|---|
hcfm_splits |
2,459,391 (67.0%) | 178,997 (4.9%) | 1,031,725 (28.1%) |
mcfm_splits |
11,703,040 (64.1%) | 790,417 (4.3%) | 5,761,808 (31.6%) |
lcfm_splits |
117,230,014 (64.5%) | 7,716,481 (4.3%) | 56,831,096 (31.3%) |
The 80/10/10 ratio is over peptides, not spectra: the registry assigns each peptide to one split and every spectrum of that peptide follows it. Peptides differ in how many spectra they have, and test peptides carry disproportionately many — 11.5% of peptides but ~31% of rows — so the test partition is about 2.7x its nominal share and validation about half of its own.
Use this to reproduce or extend the published results.
by_project/ — the input the splits came from
Each tier before filtering and splitting. Published because the filtering is lossy:
rows dropped by the quality gates are not recoverable from splits/.
Use this to apply different quality criteria, or to re-derive the partitions with
peptide_registry.parquet (see below).
Quality filters applied to splits/ but not to by_project/
| field | kept |
|---|---|
| retention time | <= 10800 s |
| lower isolation offset | <= 300 Da |
| precursor charge | 0-7 inclusive (0 is kept) |
| precursor m/z | <= 2000 |
| modification annotation | only if resolvable to a UNIMOD identifier |
A null in any numeric field passes its condition, so a reimplementation that discards nulls instead would produce a different dataset. These tiers contain no nulls in any filtered field, so the two behave identically here — but the distinction matters if you apply the same criteria to new data.
Measured effect on LCFM (184,607,213 -> 181,777,591 rows, 98.5% kept): retention time removes ~2.54 M, the unresolved-modification condition ~385 k, lower offset 8,147, and the charge and m/z bounds remove nothing — no row in the corpus exceeds either.
The unresolved annotations are modifications the pipeline could not resolve to a UNIMOD identifier ([IN:<digits>]; 175 tokens over ids 3000–3174, predominantly N-glycans on asparagine but not exclusively — K[IN:3174] is on lysine). Glycopeptides whose glycan does have a
UNIMOD identifier are retained: 100 distinct UNIMOD modifications occur across 30.7% of
the corpus.
peptide_registry.parquet
The split assignment of every peptide in the corpus. It is what makes the
peptide-disjoint partitions reproducible, and it lets new data be partitioned
consistently with these splits rather than at random — apply it to by_project/,
or to your own spectra, to keep a held-out set genuinely held out.
Joining by_project/ to the registry
Join on registry_key — not on sequence, and not on unmodified_peptide:
from datasets import load_dataset
# hcfm is the smallest tier (11.4 GB); swap in mcfm or lcfm when you need more.
runs = load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project", split="full")
registry = load_dataset("InstaDeepAI/InstaNovo", "peptide_registry", split="full")
labelled = runs.to_polars().join(
registry.to_polars(), left_on="registry_key", right_on="peptide", how="left"
)
Both calls download and cache from the Hub, so nothing needs fetching by hand.
to_polars() materialises the tier in memory, and mz_array and intensity_array
dominate that, so call runs.select_columns(["registry_key", "experiment_name", "scan"]) first if you only need the split assignment. To work through one accession at
a time rather than a whole tier, pass streaming=True and filter on experiment_name,
or fetch single files directly:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
"InstaDeepAI/InstaNovo",
"by_project/hcfm/PXD000561/Adult_Adrenalgland_Gel_Elite_49_f01.parquet",
repo_type="dataset",
)
registry_key is unmodified_peptide with every I rewritten to L, which is the
convention the registry itself uses: its peptide column contains no I at all.
Isoleucine and leucine are isobaric, so a peptide pair differing only by I/L is
indistinguishable to MS/MS, and collapsing them into one key is what stops one variant
training while the other tests.
Mind the direction. Collapsing L to I instead yields keys that match nothing, and a
failed join returns no rows rather than visibly wrong ones — which reads naturally as
"these peptides are new".
The collapse applies to the key only. sequence and unmodified_peptide keep their
original I and L residues, and the model was trained on those unnormalised sequences.
There is no normalised_peptide column. Earlier internal copies carried one that was
never populated; it is removed here so nothing invites a join that would silently match
nothing.
Confidence tiers
The tiers are nested subsets at increasing PSM-confidence thresholds: LCFM is the full labelled corpus, MCFM and HCFM are progressively stricter. MCFM and HCFM inherit LCFM's split assignments, so a peptide has the same split in every tier.
The unlabelled ACFM tier is not published here. It is approximately 5.7 TB and comprises every MS/MS scan from the same raw files, so it is reconstructible from the PRIDE accessions with the conversion pipeline deposited at Figshare, and is otherwise available from the authors on request.
Loading a specific tier
The configs above name every tier and flavour, so nothing is inferred from the directory layout:
from datasets import load_dataset
load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits") # train/validation/test
load_dataset("InstaDeepAI/InstaNovo", "lcfm_splits", split="test")
load_dataset("InstaDeepAI/InstaNovo", "hcfm_by_project") # one "full" split
To fetch files without loading them, select by path — tier and flavour are directory prefixes:
hf download InstaDeepAI/InstaNovo --repo-type dataset --include "hcfm/*"
File naming
Split files follow the Hub's sharding convention,
{split}-{index:05d}-of-{total:05d}.parquet:
splits/hcfm/hcfm-train-00000-of-00007.parquet
splits/hcfm/hcfm-validation-00000-of-00001.parquet
The -of-{total} suffix makes an incomplete download self-evident, and the
zero-padding sorts correctly in a plain lexicographic listing.
by_project/ deliberately does not use that convention. It has no train,
validation or test split, and any of those keywords in a filename would make the
Hub advertise a split that does not exist. Its files are named
data-{index:05d}-of-{total:05d}.parquet within each project accession directory,
so per-project selection stays possible:
by_project/lcfm/PXD012345/<instrument-run-name>.parquet
Versioning
v0.1 is the first release, and the one the paper's results were produced from. Pin it
rather than tracking main, so a later addition to the corpus cannot change what you
fetch:
from datasets import load_dataset
ds = load_dataset("InstaDeepAI/InstaNovo", "hcfm_splits", revision="v0.1")
main moves as tiers are extended or corrected; a tag does not.
Licence
The spectra derive from public submissions to PRIDE, so use of this dataset is governed by the EMBL-EBI terms of use.
The code that produced the corpus is available on Figshare under a CC BY 4.0 licence, and the model checkpoints are CC BY-NC-SA 4.0.
Citation
See the InstaNovo-FM manuscript. Code, including the pipeline that produced these files, is at https://github.com/instadeepai/InstaNovo-FM.