YouTube video by Prof. Jean Fan

  Okay y'all. I'm going to approach this one with a healthy pile of skepticism, but I need a solution - and probably you do as well. A small...

Job title: PhD Fellowship in Computational Systems Immunology (286608), Employer: University of Oslo, Deadline: Wednesday, October 15, 2025

Advances in mass spectrometry and associated computational tools have resulted in the wide-spread adoption of Data-Independent Acquisition (DIA) for proteomics. Experiments using state-of-the-art inst...

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein–protein interac...

Metabolite analysis is essential for understanding the biochemical processes and pathways that sustain life, providing insights into the complex interactions within cellular systems and clinical exami...

  I don't manufacture antibodies, and I honestly only have a loose understanding of the process. If I really like you, or your project I've ...

Aligning trapped ion mobility with a mass-selective quadrupole and time-of-flight mass spectrometry enables parallel accumulation–serial fragmentation, which improves proteomic analysis. This protocol...

Aspartate in the tumour environment activates the N-methyl-d-aspartate receptor in cancer cells to induce cellular programmes that increase the aggressiveness of metastasis.

Quantitative analysis of proteomics data frequently employs peptide-identity-propagation (PIP) — also known as match-between-runs (MBR) — to increase the number of peptides quantified in a given LC-MS/MS experiment. PIP can routinely account for up to 40% of all quantitative results, with that proportion rising as high as 75% in single-cell proteomics. Therefore, a significant concern for any PIP method is the possibility of false discoveries: errors that result in peptides being quantified incorrectly. Although several tools for label-free quantification (LFQ) claim to control the false discovery rate (FDR) of PIP, these claims cannot be validated as there is currently no accepted method to assess the accuracy of the stated FDR. We present a method for FDR control of PIP, called “PIP-ECHO” (PIP Error Control via Hybrid cOmpetition) and devise a rigorous protocol for evaluating FDR control of any PIP method. Using three different datasets, we evaluate PIP-ECHO alongside the PIP procedures implemented by FlashLFQ, IonQuant, and MaxQuant. These analyses show that PIP-ECHO can accurately control the FDR of PIP at 1% across multiple datasets. Only PIP-ECHO was able to control the FDR in data with injected sample size equivalent to a single-cell dataset. The three other methods fail to control the FDR at 1%, yielding false discovery proportions ranging from 2–6%. We demonstrate the practical implications of this work by performing differential expression analyses on spike-in datasets, where different known amounts of yeast or E. coli peptides are added to a constant background of HeLa cell lysate peptides. In this setting, PIP-ECHO increases both the accuracy and sensitivity of differential expression analysis: our implementation of PIP-ECHO within FlashLFQ enables the detection of 53% more differentially abundant proteins than MaxQuant and 146% more than IonQuant in the spike-in dataset. ### Competing Interest Statement The authors have declared no competing interest.