Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN)

Organ-Based Proteome and Post-Translational Modification Profiling of a Widely Cultivated Tropical Water Fish, Labeo rohita

Proteomics has enormous applications in human and animal research. However, proteomic studies in fisheries science are quite scanty particularly for economically important species. Few proteomic studies have been carried out in model fish species, but comprehensive proteomics of aquaculture species are still scarce. This study aimed to perform a comprehensive organ-based protein profiling of important tissue samples for one of the most important aquaculture species,Labeo rohita.Deep proteomic profiling of 17 histologically normal tissues, blood plasma, and embryo provided mass-spectrometric evidence for 8498 proteins at 1% false discovery rate that make up about 26% of the total annotated protein-coding sequences in Rohu. Tissue-wise expression analysis was performed, and the presence of several biologically important proteins was also verified using a targeted proteomic approach. We identified the global post-translational modifications (PTMs) in terms of acetylation (N-terminus and lysine), methylation (N-terminus, lysine, and arginine), and phosphorylation (serine, threonine, and tyrosine) to present a comprehensive proteome resource. An interactive web-based portal has been developed for an overall landscape of protein expression across the studied tissues of Labeo rohita (www.fishprot.org). This draft proteome map of Labeo rohita would advance basic and applied research in aquaculture to meet the most critical challenge of providing food and nutritional security to an increasing world population.

RIPPER: a framework for MS1 only metabolomics and proteomics label-free relative quantification

RIPPER is a framework for mass-spectrometry-based label-free relative quantification for proteomics and metabolomics studies. RIPPER combines a series of previously described algorithms for pre-processing, analyte quantification, retention time alignment, and analyte grouping across runs. It is also the first software framework to implement proximity-based intensity normalization. RIPPER produces lists of analyte signals with their unnormalized and normalized intensities that can serve as input to statistical and directed mass spectrometry (MS) methods for detecting quantitative differences between biological samples using MS.

AVAILABILITY AND IMPLEMENTATION

http://www.z.umn.edu/ripper

CONTACT

vanr0014@umn.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeMix-Q: Quantification-Centered Data Processing Workflow

For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.