Tsinghua University
Beijing, China, Beijing, Shenzhen, China · 25 authors
Tsinghua University (Beijing, China, Beijing, Shenzhen, China): 25 authors and 26 papers in the de novo peptide sequencing catalog.
Departments
- Department of Electronic Engineering
- School of Life Sciences
- Tsinghua Institute of Multidisciplinary Biomedical Research
- Tsinghua Shenzhen International Graduate School
Papers (26)
- π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection (2026, bioRxiv)
- High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques (2026, Nature Communications)
- De Novo Peptide Sequencing Daily Report (DNPS-DR) (2026, Hugging Face)
- False discovery rate control for trustworthy AI-based de novo peptide sequencing (2026, bioRxiv)
- π-HelixNovo2: Making Accurate Online De Novo Peptide Sequencing Available to All (2026, Genomics, Proteomics & Bioinformatics)
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026, Nature Biotechnology)
- π-MSNet: A billion-scale, AI-ready living proteomics data portal (2026, bioRxiv)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Accurate de novo sequencing of the modified proteome with OmniNovo (2025, arXiv)
- A Comprehensive and Systematic Review for Deep Learning-Based De Novo Peptide Sequencing (2025, IJCAI 2025)
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025, Research Square)
- Do-It-Yourself De Novo Antibody Sequencing Workflow that Achieves Complete Accuracy of the Variable Regions (2025, Journal of Proteome Research)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, Nature Communications)
- NovoBench: Benchmarking Deep Learning-based De Novo Sequencing Methods in Proteomics (2024, NeurIPS 2024)
- Transforming de novo peptide sequencing by explainable AI (2024, Research Square)
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024, arXiv)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024, bioRxiv)
- Mirror-image trypsin digestion and sequencing of D-proteins (2024, Nature Chemistry)
- ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024, AAAI 2024)
- AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024, arXiv)
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024, Briefings in Bioinformatics)
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023, bioRxiv)
- pNovo+: De Novo Peptide Sequencing Using Complementary HCD and ETD Tandem Mass Spectra (2013, Journal of Proteome Research)
- Stable Isotope N-Phosphorylation Labeling for Peptide de Novo Sequencing and Protein Quantification Based on Organic Phosphorus Chemistry (2012, Analytical Chemistry)
- An effective method for de novo peptide sequencing based on phosphorylation strategy and mass spectrometry (2011, Talanta)
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010, Journal of Proteome Research)