de novo peptide sequencing

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Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (24 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry
  17 Feb 2024 3 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples.
• Sequence-to-sequence translation from mass spectra to peptides with a transformer model
  4 Jan 2023 2 repositories listed
  A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum.
• Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
  18 Mar 2021 2 repositories listed
  De novo peptide sequencing is the key technology for finding novel peptides from mass spectra.
• Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework
  1 Dec 2019 2 repositories listed
  Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases.
• De novo peptide sequencing by deep learning
  18 Jul 2017 2 repositories listed
  In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing.
• Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
  16 Jun 2025 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  To address these issues, we propose an improved non-autoregressive peptide sequencing model that incorporates a structured protein sequence curriculum learning strategy.
• Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
  23 May 2025 1 repository listed
  We present RankNovo, the first deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models.
• Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
  24 Nov 2024 1 repository listed
  This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode.
• ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
  18 Dec 2023 1 repository listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research.
• Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model
  19 Oct 2023 1 repository listed
  Here we reveal that in the process of peptide prediction, missing fragmentation results in the generation of incorrect amino acids within those regions and causes error accumulation thereafter.
• De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
  31 Aug 2023 1 repository listed
  Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum.
• Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
  27 Aug 2023 1 repository listed
  De novo peptide sequencing is a promising approach for novel peptide discovery.
• PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing
  25 Apr 2023 1 repository listed
  De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies.
• Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
  6 Jan 2023 1 repository listed
  Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope…
• DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
  23 Mar 2022 1 repository listed
  De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data.
• PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
  17 Feb 2022 1 repository listed
  The de novo peptide sequencing, which does not rely on a comprehensive target sequence database, provided us a way to identify novel peptides from tandem mass (MS/MS) spectra.
• pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
  5 Jul 2019 1 repository listed
  In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum.
• DeepNovoV2: Better de novo peptide sequencing with deep learning
  17 Apr 2019 1 repository listed
  Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy.
• Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
  20 Dec 2018 1 repository listed
  We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data.
• Protein identification with deep learning: from abc to xyz
  8 Oct 2017 1 repository listed
  We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to…

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.