DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform.

Monika Svecla,Fiorenza Faré,Giangiacomo Beretta,Giulia Garrone,Giacomo Aletti,Giuseppe Danilo Norata

doi:10.1002/pmic.202000319

Abstract

In this study we investigated the performance of a computational pipeline for protein identification and label free quantification (LFQ) of LC–MS/MS data sets from experimental animal tissue samples, as well as the impact of its specific peptide search combinatorial approach. The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open‐source OpenMS software running within the KNIME analytics platform. Two different in silico tryptic digestion, database‐search assisted approaches (X!Tandem and MS‐GF+), de novo peptide sequencing based on Novor and consensus library search (SpectraST), were tested for the processing of LC‐MS/MS raw data files obtained from proteomic LC‐MS experiments done on proteolytic extracts from mouse ex vivo liver samples. The results from proteomic LFQ were compared to those based on the application of the two software tools MaxQuant and Proteome Discoverer for protein inference and label‐free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.

Highlights

High resolution mass spectrometry (HRMS) is considered as the most powerful applicable tool for proteomic analysis
The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open-source OpenMS software running within the KNIME analytics platform
Admirable advances in LC-high resolution mass spectrometry (HRMS) techniques, together with the availability of more powerful informatic hardware, increased the demand for bioinformatic tools for the efficient management of MS-based peptide sequencing, protein inference and label free quantification (LFQ) methods which is impacted by the massive and increasing size of the raw data files associated to the results of shotgun proteomic experiments

Summary

Introduction

High resolution mass spectrometry (HRMS) is considered as the most powerful applicable tool for proteomic analysis The popularity of this technique is due to its high sensitivity and capacity to collect fast and reliable structural information [1]. In this context, shotgun proteomic studies are of great interest to researchers from different scientific fields, especially for those involved in biological disciplines, experimental and clinical medicine and in pharmaceutical science and biopharmaceutics [2]. The detected peptide sequences and their relative MS data are submitted to computational techniques aimed at determining the identity of their parent proteins (protein inference) as well as their relative or absolute amounts through different computational approaches

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