Advanced Proteomics Techniques and Applications

The paper introduces Triqler, a Bayesian model that integrates quantification and identification uncertainties for differential protein abundance analysis in shotgun proteomics.

Alpha-XIC improves peptide identification in DIA mass spectrometry by learning robust coelution scores from peak group data, enhancing identification accuracy and recovery of misidentified peptides.

AA_stat improves interpretation of open search results in proteomics by adding features for mass shift localization, modification inference, and calibration.

IceR improves proteome coverage and data completeness in global and single-cell proteomics by combining DDA's identification rates with DIA's low missing value rates.

The paper introduces ppx, a Python package for programmatic access to proteomics data repositories, demonstrating its utility in reproducible research workflows.

The paper develops a proteomics workflow using orthogonal proteases to generate spectral libraries, enabling comprehensive plasma proteome analysis and identifying TB-related proteins.

The paper introduces an ultrafast proteomics method using Azo and timsTOF Pro to enable high-throughput, reproducible protein identification and quantification from small amounts of human heart tissue.

The paper introduces a method to identify covalent modifications and protein interactions regulating immune signaling complex composition and function in monocytes.

This paper introduces a Bayesian semi-parametric model to analyze thermal proteome profiling data, improving sensitivity and identifying new drug-protein associations.

The paper introduces the Universal Spectrum Identifier (USI) to standardize referencing mass spectra in proteomics repositories, enabling transparent traceability of spectral evidence in publications.

The paper implements a five-level proteoform classification system in MetaMorpheus and develops a stand-alone tool to classify proteoform identifications from any software that provides sufficient ambiguity information.

The paper introduces falcon, a tool for efficient clustering of large-scale mass spectrometry data, which produces larger and purer clusters than existing tools.

This paper introduces a data-driven method to detect functional proteoform groups from bottom-up proteomic datasets, identifying state-specific proteoform groups linked to molecular mechanisms.

The paper identifies phosphosites that affect protein thermal stability by measuring stability differences between proteins and their phosphorylated forms at a proteomic scale.

The paper demonstrates a reliable false-discovery rate estimation method for protein-protein interactions identified by crosslinking mass spectrometry using Escherichia coli cell lysate.

The paper presents a workflow for single-cell proteomics to study cellular heterogeneity in leukemia, demonstrating the ability to quantify thousands of proteins per cell.

DIMA selects an imputation algorithm based on data characteristics, achieving reliable performance across proteomics datasets with missing values.

ProteoSushi is a software tool designed to annotate and quantify modification-specific proteomics datasets by prioritizing peptide-centric analysis and integrating biological information.

The paper uses a neural network to predict retention times in crosslinking mass spectrometry, improving protein-protein interaction identification.

PANOPLY is a cloud-based platform that automates and reproducibly analyzes proteogenomic data by integrating genomic, transcriptomic, proteomic, and post-translational modification data.

The paper quantifies tyrosine phosphorylation in FFPE tissues to reveal patient-specific signaling networks and identifies oncogenic kinases in cancer tumors.

The paper compares proteogenomics and open variant search approaches for detecting single amino acid variants in shotgun proteomics data using NA12878 cell line data.

The paper introduces a web-based tool for cross-resource peptide spectrum comparison and visualization, with a focus on integration and retrieval from spectral databases.

This study evaluates imputation methods for proteomic datasets, finding that local and global methods perform best in different contexts and that fragment-level imputation improves accuracy.

hu.MAP 2.0 integrates over 15,000 proteomic experiments to identify nearly 7,000 human protein complexes, improving accuracy and uncovering new biological insights.

This paper introduces TMTProC, an optimized complementary ion quantification method for TMTPro tags, improving sensitivity and accuracy in multiplexed proteomics.

This paper introduces a de novo metaproteomics pipeline and validation strategy to enable quantitative profiling of microbial communities without relying on sequence databases.

DIAmeter is a library-free search engine for analyzing DIA mass spectrometry data using peptide sequences, supporting various isolation windows and post-translational modifications.

This study compiles a proteomics resource for cancer cell lines and tumour samples, finding that protein abundance in cell lines often reflects tumours but fails to capture subtype-specific variations.

The paper provides a roadmap for selecting a bottom-up mass spectrometry protocol for histone analysis by comparing four methods and introducing a new enzyme.