Paper collection about de novo peptide Sequening
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional algorithms have faced accuracy bottlenecks due to the inherent complexity of proteomics data. Deep learning methods, however, have shown promising progress in this area. This repository is dedicated to curating a collection of research papers focused on deep learning for de novo peptide sequencing, highlighting advancements in this field.
Contributions to this repository are welcome!
If you have come across relevant resources, feel free to open an issue or submit a pull request.
- (*conference|journal*) paper_name [[pdf](link)][[code](link)]
(Analytica Chimica Acta 2023.05) Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review paper
(NeurIPS 2024) NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics paper
(PNAS 2017.07) De novo peptide sequencing by deep learning paper code
(Anal. Chem. 2017.11) pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning paper code
(Nature Methods 2019.01) Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry paper
(Arxiv 2019.05) DeepNovoV2: Better de novo peptide sequencing with deep learning paper code
(Nature Machine Intelligence 2021.03) Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices paper code
(Mol. Cell. Proteomics 2019.12) Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework paper code
(Arxiv 2022.03) DePS: An improved deep learning model for de novo peptide sequencing paper
(ICML'22) De novo mass spectrometry peptide sequencing with a transformer model. paper code
(Nature Communications 2023.10) Accurate de novo peptide sequencing using fully convolutional neural networks paper code
(bioRxiv 2023.08.30) De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments paper code
(Nature Machine Intelligence 2023.10) Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model paper code
(bioRxiv 2023.08) Introducing
(AAAI24 2023.12) ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing paper code
(PLoS Comput. Biol. 2024.02) Bidirectional de novo peptide sequencing using a transformer model paper code
(bioRxiv 2024.05) π-PrimeNovo: An Accurate and Efficient Non-Autoregressive Deep Learning Model for De Novo Peptide Sequencing paper
(NeurIPS 2024) AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information paper
(NeurIPS 2024 AIDrugX) Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo paper
(ICLR 2025 Under Review) ReNovo: Retrieval-Based \emph{De Novo} Mass Spectrometry Peptide Sequencing paper
(ICLR 2025 Under Review) RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing paper