  Accurate predictions of peptide retention times (RT) in liquid chromatography
have many applications in mass spectrometry-based proteomics. Herein, we
present DeepRT, a deep learning based software for peptide retention time
prediction. DeepRT automatically learns features directly from the peptide
sequences using the deep convolutional Neural Network (CNN) and Recurrent
Neural Network (RNN) model, which eliminates the need to use hand-crafted
features or rules. After the feature learning, principal component analysis
(PCA) was used for dimensionality reduction, then three conventional machine
learning methods were utilized to perform modeling. Two published datasets were
used to evaluate the performance of DeepRT and we demonstrate that DeepRT
greatly outperforms previous state-of-the-art approaches ELUDE and GPTime.
