The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: AttributeError
Message: 'str' object has no attribute 'items'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
return HubDatasetModuleFactory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 681, in get_module
{
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 682, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 170, in __post_init__
self.features = Features.from_dict(self.features)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1872, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1459, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
AttributeError: 'str' object has no attribute 'items'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
P-KNN Precomputed Scores Dataset
This dataset provides precomputed pathogenicity prediction scores generated by the P-KNN method using dbNSFP v5.2 (academic or commercial version) with joint calibration.
It contains pathogenicity assessments for all missense variants, organized into multiple subfolders.
Dataset Structure
1. precomputed_score_academic_chromosome
Includes precomputed scores derived from the academic version of dbNSFP v5.2a, organized by genomic coordinates.
2. precomputed_score_academic_gene
Includes precomputed scores derived from the academic version of dbNSFP v5.2a, organized by MANE Ensembl transcripts. You can explore these scores using the P-KNN-Viewer.
3. precomputed_score_commercial_chromosome
Includes precomputed scores derived from the commercial version of dbNSFP v5.2c, organized by genomic coordinates.
4. precomputed_score_commercial_gene
Includes precomputed scores derived from the commercial version of dbNSFP v5.2a, organized by MANE Ensembl transcripts. You can explore these scores using the P-KNN-Viewer.
Note: For any commercial use of dbNSFP data, please review the dbNSFP commercial license requirements.
Both folders share the same file structure and columns. Files are typically named by genome build, variant type, database version, and chromosome, e.g.: P_KNN_hg38_missense_dbNSFP_chrY.csv
Column Descriptions
files in _chromosome folder:
Each CSV file contains the following 8 columns:
| Column | Description |
|---|---|
#Chr |
Chromosome number or identifier (e.g., 1–22, X, Y). |
Start |
1-based genomic start position of the variant. |
End |
1-based genomic end position of the variant. For SNVs, this is typically the same as Start. |
Ref |
Reference allele observed in the reference genome. |
Alt |
Alternate allele observed in the variant call. |
P_KNN_posterior_probability(pathogenic) |
Posterior probability that the variant is pathogenic. |
P_KNN_posterior_probability(benign) |
Posterior probability that the variant is benign. |
P_KNN_log_likelihood_ratio(evidence_strength) |
Log-likelihood ratio corresponding to the ACMG guideline evidence strength. |
files in _gene folder:
| Column | Description |
|---|---|
genename |
Official gene symbol where the variant is located. |
Ensembl_transcriptid |
Ensembl transcript identifier associated with the variant. |
Pos(cDNA) |
Position of the variant in the complementary DNA (cDNA) sequence of the transcript. |
#Chr |
Chromosome number or identifier (e.g., 1–22, X, Y). |
Pos(hg38) |
1-based genomic start position of the variant in the human genome build hg38. |
Ref |
Reference allele observed in the reference genome. |
Alt |
Alternate allele observed in the variant call. |
Pos(protein) |
Position of the variant in the protein sequence. |
aaref |
Reference amino acid at the given protein position. |
aaalt |
Alternate amino acid resulting from the variant. |
P_KNN_posterior_probability(pathogenic) |
Posterior probability that the variant is pathogenic. |
P_KNN_posterior_probability(benign) |
Posterior probability that the variant is benign. |
P_KNN_log_likelihood_ratio(evidence_strength) |
Log-likelihood ratio corresponding to the ACMG guideline evidence strength. |
Other Folders
3. dataset4commandline
Contains the default calibration and regularization datasets used by the P-KNN command-line tool.
For more details, see the P-KNN GitHub repository.
4. manuscript_dataset
Contains additional datasets used in the study:
P-KNN: Maximizing variant classification evidence through joint calibration of multiple pathogenicity prediction tools.
5. dataset4viewer
Contains the re-organized data used in P-KNN precomputed score viewer.
Citation
If you use this dataset, please cite the relevant publication (when available) and the P-KNN tool.
Related Resources
- Tool & Source Code: Brandes-Lab/P-KNN on GitHub
- Gene based precomputed score viewer: P-KNN-Viewer
- Manuscript: P-KNN: Maximizing variant classification evidence through joint calibration of multiple pathogenicity prediction tools
- dbNSFP License: dbNSFP Commercial Use Requirements
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