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Add papyrus protein targets #336

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55b0e90
Add papyrus protein targets
phalem Jun 27, 2023
bd3c62a
feat: run precommit hooks
MicPie Jul 26, 2023
1b11da3
feat: run precommit hooks
MicPie Jul 26, 2023
b8a9dee
Merge branch 'main' into add_papyrus
MicPie Jul 26, 2023
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93 changes: 93 additions & 0 deletions data/papyrus_protein_targets/meta.yaml
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---
name: papyrus_protein_targets
description: Papyrus is an aggregated dataset of small molecule bioactivities. File contains data about proteins (e.g. sequence, organism,classification).
targets:
- id: Organism
description: Organism of the protein
units: ''
type: text
names:
- noun: The organism that the protein extracted from
- noun: For which organism protein related to
- noun: living that the protein extract from
uris:
- http://purl.bioontology.org/ontology/CCON
- id: organism_common_name
description: common name of the organism that protein extract from.
units: ''
type: text
names:
- noun: common name of the organism that the protein extracted from
- noun: common name of the organism for which protein related to
- noun: common name of the living that the protein extracted from
uris:
- http://purl.bioontology.org/ontology/CCON
- id: Classification
description: Protein classification as given by ChEMBL(version 29). Levels are separated by '->'. Multiple classifications are separated by a semilcolon
';'
units: ''
type: text
names:
- noun: Protein classification
- noun: protein classification by levels
- noun: Levels for which protein classify
- id: seq_length
description: Length of the protein sequence
units: ''
type: continuous
names:
- noun: Protein sequence length
- noun: Length for protein string
- id: Sequence
description: Protein sequence including mutations
units: ''
type: string
names:
- noun: Protein sequence character
- noun: FASTQ of the protein
- noun: protein string
uris:
- http://purl.bioontology.org/ontology/MESH/D009154
identifiers:
- id: target_id
type: Other
names:
- noun: protein identifier wtih mutation
- noun: target id plus mutation
- noun: protein target combined with mutation
description: A unique Papyrus protein identifier. It results from the concatenation of accessions and mutations(e.g. P47747_WT or P10721_V559D_T670I)
- id: target_id_without_mutation
type: Other
names:
- noun: protein identifier
- noun: target id
- noun: protein target
description: A unique protein identifier
- id: UniProtID
type: Other
names:
- noun: UniProt identifier
- noun: UniProtID
description: The UniProt identifier of the sequence
license: CC BY-SA 4.0
links:
- url: https://doi.org/10.1186/s13321-022-00672-x
description: corresponding publication
- url: https://doi.org/10.4121/16896406.v3
description: data source
- url: https://data.4tu.nl/articles/_/16896406/3
description: data source
num_points: 7058
bibtex:
- |-
@article{B_quignon_2023,
doi = {10.1186/s13321-022-00672-x},
url = {https://doi.org/10.1186%2Fs13321-022-00672-x},
year = {2023},
month = jan,
publisher = {Springer Science and Business Media LLC},
volume = {15},
number = {1},
author = {O. J. M. Bequignon and B. J. Bongers and W. Jespers and A. P. IJzerman and B. van der Water and G. J. P. van Westen},
title = {Papyrus: a large-scale curated dataset aimed at bioactivity predictions},
journal = {Journal of Cheminformatics}
224 changes: 224 additions & 0 deletions data/papyrus_protein_targets/transform.py
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import pandas as pd
import yaml


def get_and_transform_data():
target_folder = "papyrus_protein_targets"
data_path = "https://data.4tu.nl/file/ca10bf7d-f508-4d54-9c9a-5a9e9c1adef9/e5863d58-c613-418b-8393-012eb6c9a04a"
fn_data_original = "data_original.csv"
df = pd.read_csv(data_path, compression="gzip", sep="\t")
df.to_csv(fn_data_original, index=None)
df = df.fillna("unkown")
df["organism_common_name"] = df["Organism"].apply(
lambda s: s[s.index("(") + 1 : -1] if "(" in s else "unknown"
)
df["target_id_without_mutation"] = df["target_id"].apply(
lambda s: s.split("_")[0] if "_" in s else s
)
df["UniProtID"] = df["UniProtID"].apply(
lambda s: s.split("_")[0] if "_" in s else s
)
df = df.drop_duplicates(subset="target_id")
fields_orig = df.columns.tolist()
assert fields_orig == [
"target_id",
"HGNC_symbol",
"UniProtID",
"Status",
"Organism",
"Classification",
"Length",
"Sequence",
"organism_common_name",
"target_id_without_mutation",
]

fields_clean = [
"target_id",
"target_id_without_mutation",
"HGNC_symbol",
"UniProtID",
"Status",
"Organism",
"organism_common_name",
"Classification",
"Length",
"Sequence",
]
df = df[fields_clean]
fields_clean = [
"target_id",
"target_id_without_mutation",
"HGNC_symbol",
"UniProtID",
"Status",
"Organism",
"organism_common_name",
"Classification",
"seq_length",
"Sequence",
]

assert fields_orig != fields_clean
assert not df.duplicated().sum()
fn_data_csv = "data_clean.csv"
df.to_csv(fn_data_csv, index=False)

# create meta yaml
meta = {
"name": f"{target_folder}", # unique identifier, we will also use this for directory names
"description": """Papyrus is an aggregated dataset of small molecule bioactivities. File contains data about proteins (e.g. sequence, organism,classification).""", # noqa: E501
"targets": [
{
"id": "Organism", # name of the column in a tabular dataset
"description": "Organism of the protein", # description of what this column means
"units": "", # units of the values in this column (leave empty if unitless)
"type": "text", # can be "categorical", "ordinal", "continuous"
"names": [ # names for the property (to sample from for building the prompts).
{"noun": "The organism that the protein extracted from"},
{"noun": "For which organism protein related to"},
{"noun": "living that the protein extract from"},
],
"uris": [
"http://purl.bioontology.org/ontology/CCON", # organism
],
},
{
"id": "organism_common_name", # name of the column in a tabular dataset
"description": "common name of the organism that protein extract from.",
"units": "", # units of the values in this column (leave empty if unitless)
"type": "text", # can be "categorical", "ordinal", "continuous"
"names": [ # names for the property (to sample from for building the prompts).
{
"noun": "common name of the organism that the protein extracted from"
},
{
"noun": "common name of the organism for which protein related to"
},
{
"noun": "common name of the living that the protein extracted from"
},
],
"uris": [
"http://purl.bioontology.org/ontology/CCON", # organism
],
},
{
"id": "Classification", # name of the column in a tabular dataset
"description": "Protein classification as given by ChEMBL(version 29). Levels are separated by '->'. Multiple classifications are separated by a semilcolon ';'", # noqa: E501
"units": "", # units of the values in this column (leave empty if unitless)
"type": "text", # can be "categorical", "ordinal", "continuous"
"names": [ # names for the property (to sample from for building the prompts).
{"noun": "Protein classification"},
{"noun": "protein classification by levels"},
{"noun": "Levels for which protein classify"},
],
},
{
"id": "seq_length", # name of the column in a tabular dataset
"description": "Length of the protein sequence", # description of what this column means
"units": "", # units of the values in this column (leave empty if unitless)
"type": "continuous", # can be "categorical", "ordinal", "continuous"
"names": [ # names for the property (to sample from for building the prompts).
{"noun": "Protein sequence length"},
{"noun": "Length for protein string"},
],
},
{
"id": "Sequence", # name of the column in a tabular dataset
"description": "Protein sequence including mutations", # description of what this column means
"units": "", # units of the values in this column (leave empty if unitless)
"type": "string", # can be "categorical", "ordinal", "continuous"
"names": [ # names for the property (to sample from for building the prompts).
{"noun": "Protein sequence character"},
{"noun": "FASTQ of the protein"},
{"noun": "protein string"},
],
"uris": [
"http://purl.bioontology.org/ontology/MESH/D009154" # mutation
],
},
],
"identifiers": [
{
"id": "target_id", # column name
"type": "Other",
"names": [
{"noun": "protein identifier wtih mutation"},
{"noun": "target id plus mutation"},
{"noun": "protein target combined with mutation"},
],
"description": "A unique Papyrus protein identifier. It results from the concatenation of accessions and mutations(e.g. P47747_WT or P10721_V559D_T670I)", # noqa: E501
},
{
"id": "target_id_without_mutation", # column name
"type": "Other",
"names": [
{"noun": "protein identifier"},
{"noun": "target id"},
{"noun": "protein target"},
],
"description": "A unique protein identifier", # description (optional, except for "Other")
},
{
"id": "UniProtID", # column name
"type": "Other",
"names": [
{"noun": "UniProt identifier"},
{"noun": "UniProtID"},
],
"description": "The UniProt identifier of the sequence", # description (optional, except for "Other")
},
],
"license": "CC BY-SA 4.0", # license under which the original dataset was published
"links": [ # list of relevant links (original dataset, other uses, etc.)
{
"url": "https://doi.org/10.1186/s13321-022-00672-x",
"description": "corresponding publication",
},
{
"url": "https://doi.org/10.4121/16896406.v3",
"description": "data source",
},
{
"url": "https://data.4tu.nl/articles/_/16896406/3",
"description": "data source",
},
],
"num_points": len(df), # number of datapoints in this dataset
"bibtex": [
"""@article{B_quignon_2023,
doi = {10.1186/s13321-022-00672-x},
url = {https://doi.org/10.1186%2Fs13321-022-00672-x},
year = {2023},
month = jan,
publisher = {Springer Science and Business Media LLC},
volume = {15},
number = {1},
author = {O. J. M. Bequignon and B. J. Bongers and W. Jespers and A. P. IJzerman and B. van der Water and G. J. P. van Westen},
title = {Papyrus: a large-scale curated dataset aimed at bioactivity predictions},
journal = {Journal of Cheminformatics}""", # noqa: E501
],
}

def str_presenter(dumper, data):
"""configures yaml for dumping multiline strings
Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
"""
if data.count("\n") > 0: # check for multiline string
return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
return dumper.represent_scalar("tag:yaml.org,2002:str", data)

yaml.add_representer(str, str_presenter)
yaml.representer.SafeRepresenter.add_representer(
str, str_presenter
) # to use with safe_dum
fn_meta = "meta.yaml"
with open(fn_meta, "w") as f:
yaml.dump(meta, f, sort_keys=False)

print(f"Finished processing {meta['name']} dataset!")


if __name__ == "__main__":
get_and_transform_data()
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