Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing

• URL: http://arxiv.org/abs/2411.15684v1
• Date: Sun, 24 Nov 2024 02:10:29 GMT
• Title: Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
• Authors: Zheng Ma, Zeping Mao, Ruixue Zhang, Jiazhen Chen, Lei Xin, Paul Shan, Ali Ghodsi, Ming Li,
• Abstract summary: Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks. It is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality.
• Score: 7.24090686599962
• License:
• Abstract: 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, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established system DeepNovo-DIA by from 25% to 81%, averaging 48%, for amino acid recall, and by from 27% to 89%, averaging 57%, for peptide recall, by equipping the model with a deeper understanding of coeluted DIA spectra. 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. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing.

Related papers

• NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics [58.03989832372747]
  We present the first unified benchmark NovoBench for emphde novo peptide sequencing. It comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $pi$-HelixNovo are integrated into our framework.
  arXiv  Detail & Related papers  (2024-06-16T08:23:21Z)
• AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information [46.23980841020632]
  We propose AdaNovo, a novel framework that calculates conditional mutual information (CMI) between the spectrum and each amino acid/peptide. AdaNovo excels in identifying amino acids with post-translational modifications (PTMs) and exhibits robustness against data noise.
  arXiv  Detail & Related papers  (2024-03-09T11:54:58Z)
• Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry [1.338778493151964]
  We introduce DiaTrans, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods.
  arXiv  Detail & Related papers  (2024-02-17T19:04:23Z)
• ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing [70.12220342151113]
  ContraNovo is a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides. ContraNovo consistently outshines contemporary state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-12-18T12:49:46Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• DePS: An improved deep learning model for de novo peptide sequencing [7.468176246958974]
  In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing. For the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively.
  arXiv  Detail & Related papers  (2022-03-16T16:45:48Z)
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity [77.78624443410216]
  We propose improved FDAM algorithms for detecting heterogeneous chest data. A result of this paper is that the communication of the proposed algorithm is strongly independent of the number of machines and also independent of the accuracy level. Experiments have demonstrated the effectiveness of our FDAM algorithm on benchmark datasets and on medical chest Xray images from different organizations.
  arXiv  Detail & Related papers  (2021-02-09T04:05:19Z)
• Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data [87.61504710345528]
  We propose two strategies for freeing a neural network from tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference.
  arXiv  Detail & Related papers  (2020-02-26T04:18:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.