Advanced Proteomics Techniques and Applications

LIMBR reduces batch effects and imputes missing data in time series proteomics by modeling experimental structure and circadian patterns.

The paper introduces a well-calibrated score function for high-resolution MS2 data that improves statistical power and outperforms existing search engines.

MaSS-Simulator generates MS/MS datasets with known ground truth for testing proteomics algorithms, showing high similarity to experimental spectra.

ANN-SoLo improves open modification spectral library searching by using approximate nearest neighbor indexing and a cascade search strategy, achieving faster speeds and more identifications than existing tools.

PANDA is a proteomics data quantification tool that offers higher accuracy and precision than MaxQuant with reduced computation time, supporting both label-free and labeled quantification.

This study assesses genetic influences on inter-individual variation in three human phosphoproteomes and identifies genetic associations with phosphopeptide variation.

This study demonstrates that triflic acid treatment solubilizes insoluble bacterial biomass, enabling comprehensive proteomic analysis of Gram- and Gram+ bacteria and human microbiome samples.

rawDiag is an R package that provides diagnostic plots for optimizing LC-MS methods in proteomics.

Xolik identifies cross-linked peptides efficiently by reducing computational complexity and outperforms existing tools in speed and statistical power.

The paper develops algorithms to comprehensively identify and quantify protein modifications, revealing oxidative damage in heart mitochondria due to mtDNA heteroplasmy.

The paper introduces a new statistical test for differential protein abundance in proteomics data, showing improved performance over existing methods.

The paper evaluates the impact of using a correct false discovery rate formula for heterobifunctional cross-linkers and finds minimal practical advantages.

This study quantifies spatial proteome heterogeneity in prostate tissues using multi-region analysis and identifies biomarker variability across benign and malignant tissues.

The paper compares phosphoproteomics data from Arabidopsis and rice to identify shared phosphorylation motifs and biological processes, finding high conservation and species-specific differences.

The paper introduces a protein standard with shared peptides to benchmark protein inference algorithms, showing that methods excluding shared peptides provide more accurate error estimates.

Specter improves DIA proteomics by directly deconvoluting spectra using linear algebra, enhancing sensitivity for similar peptides and challenging cases.

The paper creates a proteomic resource profiling 90 drugs in six cell lines, revealing cell-specific responses and enabling integration with transcriptional data to identify therapeutic opportunities.

The paper introduces TMTc+, a proteomics method that improves accuracy, sensitivity, and precision of multiplexed measurements compared to existing approaches.

The paper identifies urinary biomarkers in a rat model of autoimmune myocarditis using mass spectrometry and validates some with western blot.

The paper introduces two new data formats, proBAM and proBed, to integrate proteomics with genomics and transcriptomics data in proteogenomics studies.

This study normalizes and compares 19 yeast proteomic datasets to estimate absolute protein abundance and assess measurement variability across stress conditions.

This paper introduces a method to collect and preserve urinary proteins using a fluff pulp diaper, enabling reliable sample collection from infants and special needs patients.

The paper develops an integrative proteogenomics strategy to improve prokaryotic genome annotation by combining multiple resources and experimental data, identifying novel ORFs and correcting errors.

The paper quantifies protein and phospho-site dynamics during fertilization and meiotic exit in Xenopus eggs using mass spectrometry-based proteomics, revealing limited degradation and extensive dephosphorylation.

NOMAD is an R package that provides an efficient ANOVA-based normalization method for iTRAQ MS data to reduce MS run bias and enable valid cross-run comparisons.

The paper introduces AbScan, a proteogenomic method to identify antibody peptides from RNA-seq data, and applies it to colorectal cancer samples to find correlations between antibody peptides and survival.

ECL 2.0 improves peptide-peptide pair identification by achieving linear complexity and better sensitivity compared to existing tools.

This paper develops a high-sensitivity quantitative proteomics method using accumulated ion monitoring and automated multidimensional nano-flow chromatography, achieving sub-attomolar detection limits and accurate quantification of protein abundance and phosphorylation.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.

The paper argues that searching all expected peptides and removing irrelevant ones before FDR calculation improves reliability compared to discarding irrelevant peptides before or after FDR calculation.