Fredrik Edfors

borgonovo improves protein sequence accuracy from tandem mass spectra by consensus assembly of redundant de novo peptide reads, achieving 0.87 mean fixed-window identity on therapeutic antibodies.

This study identifies ppGpp-binding proteins in plants and cyanobacteria using chemoproteomics, revealing novel regulatory targets and conserved mechanisms compared to E. coli.

The paper uses machine learning on circulating proteomic data to identify biomarkers and reveal molecular subgroups in obesity and type 2 diabetes.

The paper integrates multi-omics data from 101 individuals over two years to identify stable immune patterns and genetic associations with immune cell types and functions.

The paper analyzes how genetics and environmental factors influence metabolic profiles over two years in 101 healthy individuals, identifying 22 genetically determined metabolites and 87 causal metabolite-protein associations.

This study applies longitudinal exposomics to a multiomic cohort, revealing dynamic environmental chemical mixtures and their associations with biological markers over time.

The paper identifies metabolite-protein interactions in the Calvin cycle of cyanobacteria and lithoautotrophic bacteria, revealing species-specific regulation and feed-forward activation mechanisms.

The paper evaluates the impact of different trypsin variants on proteomics experiments across multiple sample types and methods, identifying variations in performance and providing handling recommendations.

The paper annotates all human protein-coding genes with their function and spatial distribution across cell types using single-cell transcriptomics data and introduces a new classification tool.

The paper compares transcriptomic profiles of HEK293 cell lines producing erythropoietin or GFP to identify factors associated with improved erythropoietin production.

The paper identifies secretory pathway differences between HEK293 and CHO cells that improve recombinant protein expression when switching hosts.

The paper maps and characterizes the human secretome by annotating 2,623 predicted secreted proteins and their spatial distribution, using a high-throughput cell factory for recombinant protein production and analysis.

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.