Enhanced Information Output From Shotgun Proteomics Data by Protein Quantification and Peptide Quality Control (PQPQ)

Forshed, Jenny; Johansson, Henrik J.; Pernemalm, Maria; Branca, Rui M.; Sandberg, AnnSofi; Lehtiö, Janne

doi:10.1074/mcp.m111.010264

Cited by 31 publications

(33 citation statements)

References 32 publications

(37 reference statements)

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“…Therefore, wrong identity assignment of intensive chromatographic features poses higher risks for quantification than that of less intensive ones. For this reason, an identification-centered approach has a stringent requirement of identity correctness (46), but a quantification-centered method should be able to cope with possible misidentification using quantitative information (35).…”

Section: A Combined Identity Propagation Methods Substantially Alleviamentioning

confidence: 99%

“…In contrast, in a quantification-centered approach, peptide abundance is the central factor to be investigated (9,15,(32)(33)(34). When peptide abundance is considered together with other parameters, such as RT difference and mass error, for the overall assessment of peptide reliability, deviating abundance behavior may result in exclusion of a given peptide from consideration (35). An expected abundance behavior should, on the other hand, strengthen the certainty of peptide identity.…”

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confidence: 99%

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DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang

Käll

Zubarev

2016

Molecular & Cellular Proteomics

120

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Label-free quantification (LFQ) is one of the most efficient approaches for quantifying proteome differences between multiple states of a biological system. LFQ aims to reproducibly identify and quantify peptides through multiple liquid-chromatography-coupled tandem mass spectrometry (LC-MS/MS) experiments. In the popular data-dependent acquisition (DDA) approach named Top-N DDA, the appearance of a peptide-like signal in a "survey" mass spectrum triggers a tandem mass spectrometry (MS/MS) event, targeting the (N) most-abundant precursor ions. Previous studies have shown that, due to the limited speed of a mass spectrometer, the majority of peptide ions detected in MS 1 are not targeted in MS/MS, especially when a nonfractionated complex sample is analyzed (1, 2). This low sampling efficiency (Ͻ50%), combined with the stochastic nature of precursor selection and a limited efficiency of MS/MS identification (Ͻ70%) (3), frequently causes the absence of MS/MS identification for an individual peptide in some LC-MS/MS experiments ("runs") within a larger dataset, even when replicate measurements are made (4). This deficiency is known as the missing value problem in LFQ. The problem significantly limits the size of the DDA-acquired proteomics dataset across which reliable quantification can be made for each protein (5, 6).One of the causes of the missing value problem is the traditional focus on the process of identifying a peptide as opposed to its quantification. For historical reasons, peptide sequence identification has been considered the focal point and the most important step in the whole proteomics procedure, while quantification came as almost an afterthought (7,8). This dominant proteomics paradigm can be characterized as the identification-centered approach, also known as a spectrum-centric approach (9). Only gradually the missing value problem has been identified as one of the biggest drawbacks of the DDA approach (4, 5). To address the reproducibility issue in MS/MS identification, several alternative data acquisition strategies had been suggested, including targeted (10) and semi-targeted (11, 12) approaches. However, none of the improved DDA strategies has solved the missing value problem anywhere close to the data-independent acquisition (DIA) (13,14). The latter approach, however, typically provides somewhat lower depth and breadth of the proteome coverage than the DDA methods.In our opinion, the DDA-associated missing value problem is caused by the sequential execution of two independent processes: peptide identification by MS/MS and its quantification by MS 1 . At first glance, performing MS 1 -based quantification simultaneously with MS/MS identification should provide an obvious solution to the missing value problem. Since MS 1 spectra contain many more peptide ions than are selected for MS/MS in DDA (or identified in DIA), the peptide's mass information is practically always present when an iden-

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Section: A Combined Identity Propagation Methods Substantially Alleviamentioning

confidence: 99%

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confidence: 99%

DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang

Käll

Zubarev

2016

Molecular & Cellular Proteomics

120

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“…: PXD000065). (18) of the peptides in quantitative clusters has been performed, each peptide is filled with the same color as the cluster that it was assigned to by PQPQ. On the left, the boxes in different colors represent different clusters.…”

Section: Mass Spectrometry Data Processingmentioning

confidence: 99%

“…To address this problem, we recently developed a tool, PQPQ (protein quantification by peptide quality control) (18), to detect protein variants in MS-based shotgun proteomics data. This method is based on the assumption that peptides derived from a given protein variant will have a correlated quantitative pattern over samples.…”

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confidence: 99%

SpliceVista, a Tool for Splice Variant Identification and Visualization in Shotgun Proteomics Data

Zhu

Hultin‐Rosenberg

Forshed

et al. 2014

Molecular & Cellular Proteomics

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Alternative splicing is a pervasive process in eukaryotic organisms. More than 90% of human genes have alternatively spliced products, and aberrant splicing has been shown to be associated with many diseases. Current methods employed in the detection of splice variants include prediction by clustering of expressed sequence tags, exon microarray, and mRNA sequencing, all methods focusing on RNA-level information. There is a lack of tools for analyzing splice variants at the protein level. Here, we present SpliceVista, a tool for splice variant identification and visualization based on mass spectrometry proteomics data. SpliceVista retrieves gene structure and translated sequences from alternative splicing databases and maps MS-identified peptides to splice variants. Eukaryotic genes are composed of exonic (protein-coding) and intronic (non-coding) regions. Alternative splicing is a process in which pre-mRNA is cut at junction sites and the resulting exonic sequences are reconnected in different ways to form different versions of mature mRNA. It has been shown that 92% to 94% of human genes can undergo alternative splicing (1, 2). This process plays an essential role in increasing the proteome diversity in eukaryotic organisms. For multiexon mRNAs, different splicing patterns can occur, such as exon skipping (an exon is either included or excluded from the mature mRNAs), alternative 5Ј or 3Ј splicing (exons are spliced in different lengths), or mutually exclusive splicing (exons are selectively spliced to be exclusively present in different splice forms). Alternative splicing is carried out by the spliceosome, which consists of five small nuclear ribonucleoprotein particles, U1, U2, U4, U5, and U6, and more than 150 other proteins (3). Mutations in splicing sites or in the main components of the splicing machinery will affect the genes' splicing patterns and potentially give rise to alternative protein products that might have different conformations, functions, or subcellular locations. Disruption of the splicing machinery has been shown to be associated with many human diseases such as cystic fibrosis, Alzheimer disease, and cancer (4 -6).Large efforts have been put into the identification of gene products generated by alternative splicing. This is a challenging task, as alternative splice forms are often temporal, tissue specific, and low abundant (7). So far most work has been done starting at the mRNA level, making use of the vast amount of public-domain expressed sequence tag data as well as RNA sequencing data (8 -11). Expressed sequence tags or RNA sequencing reads that belong to one gene are clustered together and then aligned with the genomic sequence in order to identify alternative splicing events. These efforts have resulted in many publically available alternative splicing databases. Most of them are generated by mining data from GenBank, UniGene and Swiss-Prot. The Evidence

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“…Protein identifications were based on at least one unique peptide above the score. The protein quantities from Proteome Discoverer were further curated using the in-house developed software PQPQ (28). PQPQ checks all the peptides matched to a protein by analyzing the quantitative pattern over samples.…”

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confidence: 99%