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[HTML][HTML] Acquisition and analysis of DIA-based proteomic data: A comprehensive survey in 2023
R Lou, W Shui - Molecular & Cellular Proteomics, 2024 - Elsevier
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful
technology for high-throughput, accurate, and reproducible quantitative proteomics. This …
technology for high-throughput, accurate, and reproducible quantitative proteomics. This …
Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP
Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with
high specificity and sensitivity. To increase its throughput, we developed nano-proteomic …
high specificity and sensitivity. To increase its throughput, we developed nano-proteomic …
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass
spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we …
spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we …
Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides
Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5'cap and the
poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active …
poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active …
Sage: an open-source tool for fast proteomics searching and quantification at scale
The growing complexity and volume of proteomics data necessitate the development of
efficient software tools for peptide identification and quantification from mass spectra. Given …
efficient software tools for peptide identification and quantification from mass spectra. Given …
Prediction of glycopeptide fragment mass spectra by deep learning
Y Yang, Q Fang - Nature Communications, 2024 - nature.com
Deep learning has achieved a notable success in mass spectrometry-based proteomics and
is now emerging in glycoproteomics. While various deep learning models can predict …
is now emerging in glycoproteomics. While various deep learning models can predict …
Analysis and visualization of quantitative proteomics data using FragPipe-Analyst
The FragPipe computational proteomics platform is gaining widespread popularity among
the proteomics research community because of its fast processing speed and user-friendly …
the proteomics research community because of its fast processing speed and user-friendly …
Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit
Abstract Machine learning (ML) and deep learning (DL) models for peptide property
prediction such as Prosit have enabled the creation of high quality in silico reference …
prediction such as Prosit have enabled the creation of high quality in silico reference …
Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the
generation of immunopeptides from their parent proteins does not adhere to clear-cut rules …
generation of immunopeptides from their parent proteins does not adhere to clear-cut rules …
Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference
Identification of differentially expressed proteins in a proteomics workflow typically
encompasses five key steps: raw data quantification, expression matrix construction, matrix …
encompasses five key steps: raw data quantification, expression matrix construction, matrix …