PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios

iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification

MOTIVATION

Isobaric labelling techniques such as iTRAQ and TMT are popular methods for relative protein abundance estimation in proteomic studies. However, measurements are assessed at the peptide spectrum level and exhibit substantial heterogeneity per protein. Hence, clever summarization strategies are required to infer protein ratios. So far, current methods rely exclusively on quantitative values, while additional information on peptides is available, yet it is not considered in these methods.

METHODS

We present iPQF ( I: sobaric P: rotein Q: uantification based on F: eatures) as a novel peptide-to-protein summarization method, which integrates peptide spectra characteristics as well as quantitative values for protein ratio estimation. We investigate diverse features characterizing spectra reliability and reveal significant correlations to ratio accuracy in spectra. As a result, we developed a feature-based weighting of peptide spectra.

RESULTS

A performance evaluation of iPQF in comparison to nine different protein ratio inference methods is conducted on five published MS2 and MS3 datasets with predefined ground truth. We demonstrate the benefit of using peptide feature information to improve protein ratio estimation. Compared to purely quantitative approaches, our proposed strategy achieves increased accuracy by addressing peptide spectra reliability.

AVAILABILITY AND IMPLEMENTATION

The iPQF algorithm is available within the established R/Bioconductor package MSnbase (version ≥ 1.17.8).

CONTACT

renardB@rki.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ProfileDB: a resource for proteomics and cross-omics biomarker discovery

The increasing size and complexity of high-throughput datasets pose a growing challenge for researchers. Often very different (cross-omics) techniques with individual data analysis pipelines are employed making a unified biomarker discovery strategy and a direct comparison of different experiments difficult and time consuming. Here we present the comprehensive web-based application ProfileDB. The application is designed to integrate data from different high-throughput 'omics' data types (Transcriptomics, Proteomics, Metabolomics) with clinical parameters and prior knowledge on pathways and ontologies. Beyond data storage, ProfileDB provides a set of dedicated tools for study inspection and data visualization. The user can gain insights into a complex experiment with just a few mouse clicks. We will demonstrate the application by presenting typical use cases for the identification of proteomics biomarkers. All presented analyses can be reproduced using the public ProfileDB web server. The ProfileDB application is available by standard browser (Firefox 18+, Internet Explorer Version 9+) technology via http://profileDB.-microdiscovery.de/ (login and pass-word: profileDB). The installation contains several public datasets including different cross-'omics' experiments. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.