Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods.

This data article describes a controlled, spiked proteomic dataset for which the "ground truth" of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies.

Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.

Unlabelled: Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, based either on spectral counting or on MS signal analysis, which appear as an attractive way to analyze differential protein expression in complex biological samples. However, the computational processing of the data for label-free quantification still remains a challenge.

Bacterial protein signals are associated with Crohn's disease.

Objective: No Crohn's disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls.

Design: We first developed and validated a workflow-including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS-to discover protein signals from CD-associated gut microbial communities.

An improved stable isotope N-terminal labeling approach with light/heavy TMPP to automate proteogenomics data validation: dN-TOP.

In silico gene prediction has proven to be prone to errors, especially regarding precise localization of start codons that spread in subsequent biological studies. Therefore, the high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and especially proteogenomics fields. The trimethoxyphenyl phosphonium (TMPP) labeling approach (N-TOP) is an efficient N-terminomic approach that allows the characterization of both N-terminal and internal peptides in a single experiment.