Papers de novo peptide sequencing
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Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have proven highly effective for this task, outpe…
de novo peptide sequencingUniversal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribut…
de novo peptide sequencingRerankingZero-shot GeneralizationDisentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, …
de novo peptide sequencingNovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de …
Benchmarkingde novo peptide sequencingAdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying ami…
de novo peptide sequencingTransformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry
Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of p…
de novo peptide sequencingContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics…
Contrastive Learningde novo peptide sequencingMitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model
Novel protein discovery and immunopeptidomics depend on highly sensitive de novo peptide sequencing with tandem mass spectrometry. Despite notable improvement using deep learning models, the missing-fragmentation problem…
de novo peptide sequencingGraph Neural NetworkDe novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and m…
de novo peptide sequencingIntroducing π-HelixNovo for practical large-scale de novo peptide sequencing
De novo peptide sequencing is a promising approach for novel peptide discovery. We use a novel concept of complementary spectra to enhance ion information and propose a de novo sequencing model π-HelixNovo based on Trans…
de novo peptide sequencingPGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing
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. In re…
de novo peptide sequencingEfficient Neural NetworkDeep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
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 …
de novo peptide sequencingSensitivitySequence-to-sequence translation from mass spectra to peptides with a transformer model
A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequen…
de novo peptide sequencingDPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during infer…
Decoderde novo peptide sequencingDePS: An improved deep learning model for de novo peptide sequencing
De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of th…
de novo peptide sequencingPepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
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. However, current de novo sequencing algorith…
de novo peptide sequencingComputationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
De novo peptide sequencing is the key technology for finding novel peptides from mass spectra. The overall quality of sequencing results depends on the de novo peptide sequencing algorithm as well as the quality of mass …
de novo peptide sequencingUncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework
Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutat…
de novo peptide sequencingImproving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches
De novo peptide sequencing algorithms have been widely used in proteomics to analyse tandem mass spectra (MS/MS) and assign them to peptides, but quality-control methods to evaluate the confidence of de novo peptide sequ…
de novo peptide sequencingRe-RankingpNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion c…
Deep Learningde novo peptide sequencingLearning-To-Rank