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DeepLC can predict retention times for peptides that carry as-yet unseen modifications

Robbin Bouwmeester (UGent) , Ralf Gabriels (UGent) , Niels Hulstaert (UGent) , Lennart Martens (UGent) and Sven Degroeve (UGent)
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Abstract
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration.

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MLA
Bouwmeester, Robbin, et al. “DeepLC Can Predict Retention Times for Peptides That Carry As-yet Unseen Modifications.” BioRxiv, 2020, doi:10.1101/2020.03.28.013003.
APA
Bouwmeester, R., Gabriels, R., Hulstaert, N., Martens, L., & Degroeve, S. (2020). DeepLC can predict retention times for peptides that carry as-yet unseen modifications. BioRxiv. https://doi.org/10.1101/2020.03.28.013003
Chicago author-date
Bouwmeester, Robbin, Ralf Gabriels, Niels Hulstaert, Lennart Martens, and Sven Degroeve. 2020. “DeepLC Can Predict Retention Times for Peptides That Carry As-yet Unseen Modifications.” BioRxiv. https://doi.org/10.1101/2020.03.28.013003.
Chicago author-date (all authors)
Bouwmeester, Robbin, Ralf Gabriels, Niels Hulstaert, Lennart Martens, and Sven Degroeve. 2020. “DeepLC Can Predict Retention Times for Peptides That Carry As-yet Unseen Modifications.” BioRxiv. doi:10.1101/2020.03.28.013003.
Vancouver
1.
Bouwmeester R, Gabriels R, Hulstaert N, Martens L, Degroeve S. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. bioRxiv. 2020;
IEEE
[1]
R. Bouwmeester, R. Gabriels, N. Hulstaert, L. Martens, and S. Degroeve, “DeepLC can predict retention times for peptides that carry as-yet unseen modifications,” bioRxiv, 2020.
@article{8668340,
  abstract     = {The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration.},
  author       = {Bouwmeester, Robbin and Gabriels, Ralf and Hulstaert, Niels and Martens, Lennart and Degroeve, Sven},
  journal      = {bioRxiv},
  language     = {und},
  title        = {DeepLC can predict retention times for peptides that carry as-yet unseen modifications},
  url          = {http://dx.doi.org/10.1101/2020.03.28.013003},
  year         = {2020},
}

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