Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identificati...

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Published inBioinformatics (Oxford, England) Vol. 40; no. 7
Main Authors Zhang, Yichi, Hu, Chenghui, Wu, Xiaohui, Song, Jian
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.07.2024
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ISSN1367-4803
1367-4811
1367-4811
DOI10.1093/bioinformatics/btae417

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Summary:The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.
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Yichi Zhang and Chenghui Hu Equal contribution.
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btae417