Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
Center for Systems Biology Dresden, 01307 Dresden, Germany.
Bioinformatics. 2024 Jul 1;40(7). doi: 10.1093/bioinformatics/btae424.
Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry.
Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions.
deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.
蛋白质合成过程中遗传信息处理错误会导致表型突变,例如氨基酸替换,例如转录或翻译错误。虽然可以使用 DNA 测序轻松识别遗传突变,并且可以使用 RNA 测序识别转录错误引起的突变,但只有使用质谱法才能在蛋白质组范围内识别翻译错误。
在这里,我们提供了一个 Python 包实现的高通量管道,用于检测质谱数据集的氨基酸替换。我们的工具使用户能够以批处理模式处理数百个质谱数据集,以检测氨基酸替换并计算密码子特异性和位点特异性翻译错误率。deTELpy 将有助于系统地了解氨基酸掺入率(翻译错误率),并推断不同生物体和应激条件下(例如药物治疗或疾病条件)的错误模型。
deTELpy 是用 Python 3 实现的,在 https://git.mpi-cbg.de/tothpetroczylab/detelpy 和 https://pypi.org/project/deTELpy/ 上提供了详细的文档和实际示例,并可通过 pip install deTELpy 轻松安装。