MutCleaner: Cleaning and Standardizing Biological Mutation Datasets for Variant Effect Prediction
- 1Zhongnan University of Economics and Law
Abstract
Protein mutation datasets are widely used in variant effect prediction and protein engineering, but datasets from different sources often lack consistent conventions for mutation representation, sequence representation, data organization, and experimental labels, making these resources difficult to integrate directly and limiting their use in downstream analyses and modeling. MutCleaner is an extensible Python framework that cleans, validates, and standardizes protein- and codon-level mutation datasets through composable cleaning pipelines, unified sequence and mutation data structures, and dataset-specific cleaners. It provides standardized resources covering 16 protein and codon mutation datasets with more than 12.14 million mutation records. Availability and implementation: MutCleaner is an open-source Python package released under the Apache License 2.0. The source code is available on https://github.com/xulab-research/MutCleaner, the package is distributed through https://pypi.org/project/mutcleaner/, and the documentation is available https://xulab-research.github.io/MutCleaner/.
Related
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BibTeX
@misc{shi2026mutcleaner,
title = {{MutCleaner: Cleaning and Standardizing Biological Mutation Datasets for Variant Effect Prediction}},
author = {Z. Shi and Y. Tang and M. Yang and S. Yu and Y. Shi and Y. Xu},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.06.749687},
url = {https://www.biorxiv.org/content/10.64898/2026.09.06.749687v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - MutCleaner: Cleaning and Standardizing Biological Mutation Datasets for Variant Effect Prediction
AU - Z. Shi
AU - Y. Tang
AU - M. Yang
AU - S. Yu
AU - Y. Shi
AU - Y. Xu
PY - 2026
DA - 2026/09/06
PB - bioRxiv
DO - 10.64898/2026.09.06.749687
UR - https://www.biorxiv.org/content/10.64898/2026.09.06.749687v1
AB - Protein mutation datasets are widely used in variant effect prediction and protein engineering, but datasets from different sources often lack consistent conventions for mutation representation, sequence representation, data organization, and experimental labels, making these resources difficult to integrate directly and limiting their use in downstream analyses and modeling. MutCleaner is an extensible Python framework that cleans, validates, and standardizes protein- and codon-level mutation datasets through composable cleaning pipelines, unified sequence and mutation data structures, and dataset-specific cleaners. It provides standardized resources covering 16 protein and codon mutation datasets with more than 12.14 million mutation records. Availability and implementation: MutCleaner is an open-source Python package released under the Apache License 2.0. The source code is available on https://github.com/xulab-research/MutCleaner, the package is distributed through https://pypi.org/project/mutcleaner/, and the documentation is available https://xulab-research.github.io/MutCleaner/.
ER -