Retention　time　(RT)　alignment　is　a　crucial　step　in　liquid　chromatography-mass　spectrometry　(LC-MS)-based　proteomic　and　metabolomic　experiments,　especially　for　large　cohort　studies.　The　most　popular　alignment　tools　are　based　on　warping　function　method　and　direct　matching　method.　However,　existing　tools　can　hardly　handle　monotonic　and　non-monotonic　RT　shifts　simultaneously.　Here,　we　develop　a　deep　learning-based　RT　alignment　tool,　DeepRTAlign,　for　large　cohort　LC-MS　data　analysis.　DeepRTAlign　has　been　demonstrated　to　have　improved　performances　by　benchmarking　it　against　current　state-of-the-art　approaches　on　multiple　real-world　and　simulated　proteomic　and　metabolomic　datasets.　The　results　also　show　that　DeepRTAlign　can　improve　identification　sensitivity　without　compromising　quantitative　accuracy.　Furthermore,　using　the　MS　features　aligned　by　DeepRTAlign,　we　trained　and　validated　a　robust　classifier　to　predict　the　early　recurrence　of　hepatocellular　carcinoma.　DeepRTAlign　provides　an　advanced　solution　to　RT　alignment　in　large　cohort　LC-MS　studies,　which　is　currently　a　major　bottleneck　in　proteomics　and　metabolomics　research.　Retention　time　(RT)　alignment　is　a　crucial　step　in　large　cohort　proteomics　and　metabolomics　studies.　Here,　the　authors　introduce　DeepRTAlign,　a　deep　learning　tool　for　RT　alignment　that　shows　high　identification　sensitivity　and　quantitative　accuracy.