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We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

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SARS-CoV-2, one of the human RNA viruses, is widely studied around the world. Significant efforts have been made to understand its molecular mechanisms of action and how it interacts with epithelial cells and the human microbiome since it has also been observed in gut microbiome bacteria. Many studies emphasize the importance of surface immunity and also that the mucosal system is critical in the interaction of the pathogen with the cells of the oral, nasal, pharyngeal, and intestinal epithelium. Recent studies have shown how bacteria in the human gut microbiome produce toxins capable of altering the classical mechanisms of interaction of viruses with surface cells. This paper presents a simple approach to highlight the initial behavior of a novel pathogen, SARS-CoV-2, on the human microbiome. The immunofluorescence microscopy technique can be combined with spectral counting performed at mass spectrometry of viral peptides in bacterial cultures, along with identification of the presence of D-amino acids within viral peptides in bacterial cultures and in patients' blood. This approach makes it possible to establish the possible expression or increase of viral RNA viruses in general and SARS-CoV-2, as discussed in this study, and to determine whether or not the microbiome is involved in the pathogenetic mechanisms of the viruses. This novel combined approach can provide information more rapidly, avoiding the biases of virological diagnosis and identifying whether a virus can interact with, bind to, and infect bacteria and epithelial cells. Understanding whether some viruses have bacteriophagic behavior allows vaccine therapies to be focused either toward certain toxins produced by bacteria in the microbiome or toward finding inert or symbiotic viral mutations with the human microbiome. This new knowledge opens a scenario on a possible future vaccine: the probiotics vaccine, engineered with the right resistance to viruses that attach to both the epithelium human surface and gut microbiome bacteria.

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Longitudinal changes in the blood proteome during gestation relate to fetal development and maternal homeostasis. Charting the maternal blood proteome in normal pregnancies is critical for establishing a baseline reference when assessing complications and disease. Using mass spectrometry-based shotgun proteomics, we surveyed the maternal plasma proteome across uncomplicated pregnancies. Results indicate a significant rise in proteins that govern placentation and are vital to the development and health of the fetus. Importantly, we uncovered proteome signatures that strongly correlated with gestational age. Fold increases and correlations between the plasma concentrations of ADAM12 (ρ = 0.973), PSG1 (ρ = 0.936), and/or CSH1/2 (ρ = 0.928) with gestational age were validated with ELISA. Proteomic and validation analyses demonstrate that the maternal plasma concentration of ADAM12, either independently or in combination with either PSG1 or CSH1/2, correlates with gestational age within ±8 days throughout pregnancy. These findings suggest that the gestational age in healthy pregnancies may be determined by referencing the concentration of select plasma proteins.

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Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of "missing at random" (MAR) across batches of samples and varying rates of "missing not at random" (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

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OBJECTIVE: Endometrial cancer is the most common gynecologic cancer and yet much is still unknown about this disease. Our goal was to identify unique biomarkers of disease by performing a comprehensive proteomic analysis of early stage, low-grade endometrial cancer through analysis of serum collected from patients pre- and post-definitive surgery. METHODS: We used mass spectrometry (MS)-based proteomics to identify serum proteins from these patients. Serum samples from women undergoing hysterectomy with bilateral salpingo-oophorectomy for benign reasons served as control samples for the correlative studies. We then correlated our findings with The Cancer Genome Atlas (TCGA) database for additional confirmation. RESULTS: The Ingenuity Pathway Analysis of proteins that were differentially expressed in endometrial cancer showed increased cell survival and decreased organismal death, the most common hallmarks of cancer. We identified over expression of FAM83D (family with sequence similarity 83, member D) in the serum of patients with early stage low-grade endometrial cancer and verified the same in the endometrial cancer cell lines and patient tumors. We also confirmed our hypothesis that FAM83D may serve as a biomarker for endometrial cancer in a cohort of patients with endometrial cancer from The Cancer Genome Atlas (TCGA) project. CONCLUSION: Comprehensive proteomic analysis is a feasible strategy for potential biomarker identification. Using this technique, FAM83D was identified as a candidate biomarker in early endometrial cancer in our patient samples and was not present in benign control samples. FAM83D has been associated with poor clinical outcomes in several human malignancies. SIGNIFICANCE: Our manuscript describes an alternative approach to comprehensive protein analysis in a model pre and post tumor removal for a sample of patients with early endometrial cancer. The model is innovative and the findings of over expression FAM83D in this population of early cancer may be useful in the study of a disease where there are few biomarkers or targetable therapies.

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PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for 'control' and 'treated' samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

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Label-free quantification has become a common-practice in many mass spectrometry-based proteomics experiments. In recent years, we and others have shown that spectral clustering can considerably improve the analysis of (primarily large-scale) proteomics data sets. Here we show that spectral clustering can be used to infer additional peptide-spectrum matches and improve the quality of label-free quantitative proteomics data in data sets also containing only tens of MS runs. We analyzed four well-known public benchmark data sets that represent different experimental settings using spectral counting and peak intensity based label-free quantification. In both approaches, the additionally inferred peptide-spectrum matches through our spectra-cluster algorithm improved the detectability of low abundant proteins while increasing the accuracy of the derived quantitative data, without increasing the data sets' noise. Additionally, we developed a Proteome Discoverer node for our spectra-cluster algorithm which allows anyone to rebuild our proposed pipeline using the free version of Proteome Discoverer.

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Plasmodesmata are channels that link adjacent cells in plant tissues through which molecular exchanges take place. They are involved in multiple processes vital to plant cells, such as responses to hormonal signaling or environmental challenges including osmotic stress, wounding and pathogen attack. Despite the importance of plasmodesmata, their proteome is not well-defined. Here, we have isolated fractions enriched in plasmodesmata from cell suspension cultures of Populus trichocarpa and identified 201 proteins that are enriched in these fractions, thereby providing further insight on the multiple functions of plasmodesmata. Proteomics analysis revealed an enrichment of proteins specifically involved in responses to stress, transport, metabolism and signal transduction. Consistent with the role of callose deposition and turnover in the closure and aperture of the plasmodesmata and our proteomic analysis, we demonstrate the enrichment of callose synthase activity in the plasmodesmata represented by several gene products. A new form of calcium-independent callose synthase activity was detected, in addition to the typical calcium-dependent enzyme activity, suggesting a role of calcium in the regulation of plasmodesmata through two forms of callose synthase activities. Our report provides the first proteomic investigation of the plasmodesmata from a tree species and the direct biochemical evidence for the occurrence of several forms of active callose synthases in these structures. Data are available via ProteomeXchange with identifier PXD010692.

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The practice of data sharing in the proteomics field took off and quickly spread in recent years as a result of collective effort. Nowadays, most journal editors mandate the submission of the original raw mass spectra to one of the databases of the ProteomeXchange consortium. With the exception of large institutional initiatives such as PeptideAtlas or the GPMDB, few new studies are however based on the reanalysis of mass spectrometry data. A wealth of information is thus left unexploited in public databases and repositories. Here, we present the large-scale reanalysis of 41 publicly available data sets corresponding to experiments carried out on the HeLa cancer cell line using a custom workflow. In addition to the search of new post-translational modification sites and "missing proteins", our main goal is to identify single amino acid variants and evaluate their impact on protein expression and stability through the spectral counting quantification approach. The X!Tandem software was selected to perform the search of a total of 56 363 701 tandem mass spectra against a customized variant protein database, compiled by the application of the in-house MzVar tool on HeLa-specific somatic and genomic variants retrieved from the COSMIC cell line project. After filtering the resulting identifications with a 1% FDR threshold computed at the protein level, 49 466 unique peptides were identified in 7266 protein entries, allowing the validation of 5576 protein entries in accordance with the HPP guidelines version 2.1. A new "missing protein" was observed (FRAT2, NX_O75474, chromosome 10), and 189 new phosphorylation and 392 new protein N-terminal acetylation sites could be identified. Twenty-four variant peptides were also identified, corresponding to 21 variants in 21 proteins. For three of the nine heterozygous cases where both the variant peptide and its wild-type counterpart were detected, the application of a two-tailed sign test showed a significant difference in the abundance of the two peptide versions.

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Proximity-dependent biotinylation strategies have emerged as powerful tools to characterize the subcellular context of proteins in living cells. The popular BioID approach employs an abortive E. coli biotin ligase mutant (R118G; denoted as BirA*), which when fused to a bait protein enables the covalent biotinylation of endogenous proximal polypeptides. This approach has been mainly applied to the study of protein proximity in immortalized mammalian cell lines. To expand the application space of BioID, here we describe a set of lentiviral vectors that enable the inducible expression of BirA*-tagged bait fusion proteins for performing proximity-dependent biotinylation in diverse experimental systems. We benchmark this highly adaptable toolkit across immortalized and primary cell systems, demonstrating the ease, versatility and robustness of the system. We also provide guidelines to perform BioID using these reagents.

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A Disintegrin And Metalloproteinase 12 (ADAM12) is highly expressed in multiple cancers such as breast and cervical cancers and its high expression reduces the overall patient survival rate. ADAM12 has two major splicing variants, the long membrane-anchored form ADAM12L and the short secreted form ADAM12S. However, how they are regulated and whether they are modulated similarly or differently in cells are not clear. Here, we use affinity purification and mass spectrometry to identify the ADAM12S-interacting proteins. Spectral counting and MaxQuant label-free quantification reveal that ADAM12S but not ADAM12L specifically interacts with a subset of endoplasmic reticulum proteins, such as endoplasmin (GRP94), 78 kDa glucose-regulated protein (GRP78), and UDP-glucose:glycoprotein glucosyltransferase I (UGGT1), that regulate the folding and processing of secreted proteins. Further biochemical experiments validate the interaction between ADAM12S and several of its interacting proteins. Computational docking analysis demonstrates that GRP94 preferentially interacts with ADAM12S over ADAM12L. The data also suggest that both the protein expression level and the secretion of ADAM12S are regulated by GRP94 expression and knockdown. Our results reveal a link between these two proteins that are highly expressed in cancer cells. Furthermore, our studies define a new ADAM12S-specific regulator that may contribute to the cancer development. SIGNIFICANCE: A Disintegrin And Metalloproteinase 12 (ADAM12) is highly expressed in many cancers such as lung, breast, and cervical cancers. ADAM12 has two major splicing variants, the long membrane-anchored form ADAM12L and the short secreted form ADAM12S. However, how they are regulated and whether they are modulated similarly or differently are not completely understood. We use affinity purification and label-free quantitative proteomics to identify the ADAM12S-interacting proteins. Our results reveal that ADAM12S specifically interacts with a subset of endoplasmic reticulum proteins, including endoplasmin (GRP94), UDP-glucose:glycoprotein glucosyltransferase I (UGGT1), and neutral α-glucosidase AB (GANAB). Computer modeling reveals that ADAM12S interacts with the surface amino acids of GRP94 more strongly than ADAM12L. Biochemical experiments further reveal that GRP94 regulates both the protein level and the secretion of ADAM12S. Database mining finds that both GRP94 and ADAM12 are highly expressed in multiple cancers and their high expression is correlated with poor patient survival rate. Taken together, our work discovers a new upstream regulator for ADAM12S, which may contribute to its distinct functions in the regulation of the migration and invasion of cancer cells.

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Influenza A virus infections can result in severe respiratory diseases. The H7N9 subtype of avian influenza A virus has been transmitted to humans and caused severe disease and death. Nonstructural protein 1 (NS1) of influenza A virus is a virulence determinant during viral infection. To elucidate the functions of the NS1 encoded by influenza A H7N9 virus (H7N9 NS1), interaction partners of H7N9 NS1 in human cells were identified with immunoprecipitation followed by SDS-PAGE coupled with liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). We identified 36 cellular proteins as the interacting partners of the H7N9 NS1, and they are involved in RNA processing, mRNA splicing via spliceosome, and the mRNA surveillance pathway. Two of the interacting partners, cleavage and polyadenylation specificity factor subunit 2 (CPSF2) and CPSF7, were confirmed to interact with H7N9 NS1 using coimmunoprecipitation and immunoblotting based on the previous finding that the two proteins are involved in pre-mRNA polyadenylation machinery. Furthermore, we illustrate that overexpression of H7N9 NS1, as well as infection by the influenza A H7N9 virus, interfered with pre-mRNA polyadenylation in host cells. This study comprehensively profiled the interactome of H7N9 NS1 in host cells, and the results demonstrate a novel endotype for H7N9 NS1 in inhibiting host mRNA maturation.

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PURPOSE: Great progresses have been made for generating in vitro pluripotent stem cell pancreatic ß-like cells. However, the maturation stage of the cells still requires in vivo maturation to recreate the environmental niche. A deeper understanding of the factors promoting maturation of the cells is of great interest for clinical applications. EXPERIMENTAL DESIGN: Label-free mass spectrometry based proteomic analysis is performed on samples from a longitudinal study of differentiation of human induced pluripotent stem cells toward glucose responsive insulin producing cells. RESULTS: Proteome patterns correlate with specific transcription factor gene expression levels during in vitro differentiation, showing the relevance of the technology for identification of pancreatic-specific markers. The analysis of proteomes of the implanted cells in a longitudinal study shows that the neovascularization process linked to the extracellular matrix environment is time-dependent and conditions the proper maturation of the cells in ß-like cells secreting insulin in response to glucose. CONCLUSIONS AND CLINICAL RELEVANCE: Proteomic profiling is valuable to qualify and better understand in vivo maturation of progenitor cells toward ß-cells. This is critical for future clinical trials where in vivo maturation still needs to be improved for robustness and effectiveness of cell therapy. Novel biomarkers for predicting the efficiency of maturation represents noninvasive monitoring tools for following efficiency of the implant.

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Ubiquitinated proteins carried by the extracellular vesicles (EV) released by myeloid-derived suppressor cells (MDSC) have been investigated using proteomic strategies to examine the effect of tumor-associated inflammation. EV were collected from MDSC directly following isolation from tumor-bearing mice with low and high inflammation. Among the 1092 proteins (high inflammation) and 925 proteins (low inflammation) identified, more than 50% were observed as ubiquitinated proteoforms. More than three ubiquitin-attachment sites were characterized per ubiquitinated protein, on average. Multiple ubiquitination sites were identified in the pro-inflammatory proteins S100 A8 and S100 A9, characteristic of MDSC and in histones and transcription regulators among other proteins. Spectral counting and pathway analysis suggest that ubiquitination occurs independently of inflammation. Some ubiquitinated proteins were shown to cause the migration of MDSC, which has been previously connected with immune suppression and tumor progression. Finally, MDSC EV are found collectively to carry all the enzymes required to catalyze ubiquitination, and the hypothesis is presented that a portion of the ubiquitinated proteins are produced in situ.

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In arbuscular mycorrhizal (AM) roots, the plasma membrane (PM) of the host plant is involved in all developmental stages of the symbiotic interaction, from initial recognition to intracellular accommodation of intra-radical hyphae and arbuscules. Although the role of the PM as the agent for cellular morphogenesis and nutrient exchange is especially accentuated in endosymbiosis, very little is known regarding the PM protein composition of mycorrhizal roots. To obtain a global overview at the proteome level of the host PM proteins as modified by symbiosis, we performed a comparative protein profiling of PM fractions from Medicago truncatula roots either inoculated or not with the AM fungus Rhizophagus irregularis. PM proteins were isolated from root microsomes using an optimized discontinuous sucrose gradient; their subsequent analysis by liquid chromatography followed by mass spectrometry (MS) identified 674 proteins. Cross-species sequence homology searches combined with MS-based quantification clearly confirmed enrichment in PM-associated proteins and depletion of major microsomal contaminants. Changes in protein amounts between the PM proteomes of mycorrhizal and non-mycorrhizal roots were monitored further by spectral counting. This workflow identified a set of 82 mycorrhiza-responsive proteins that provided insights into the plant PM response to mycorrhizal symbiosis. Among them, the association of one third of the mycorrhiza-responsive proteins with detergent-resistant membranes pointed at partitioning to PM microdomains. The PM-associated proteins responsive to mycorrhization also supported host plant control of sugar uptake to limit fungal colonization, and lipid turnover events in the PM fraction of symbiotic roots. Because of the depletion upon symbiosis of proteins mediating the replacement of phospholipids by phosphorus-free lipids in the plasmalemma, we propose a role of phosphate nutrition in the PM composition of mycorrhizal roots.

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BACKGROUND: Barley seed proteins are of prime importance to the brewing industry, human and animal nutrition and in plant breeding for cultivar identification. To obtain comprehensive proteomic data from seeds, total protein from a two-rowed (Conrad) and a six-rowed (Lacey) barley cultivar were precipitated in acetone, digested in-solution, and the resulting peptides were analyzed by nano-liquid chromatography coupled with tandem mass spectrometry. RESULTS: The raw mass spectra data searched against Uniprot's Barley database using in-house Mascot search engine identified 1168 unique proteins. Gene Ontology (GO) analysis indicated that the majority of the seed proteins were cytosolic, with catalytic activity and associated with carbohydrate metabolism. Spectral counting analysis showed that there are 20 differentially abundant seed proteins between the two-rowed Conrad and six-rowed Lacey cultivars. CONCLUSION: This study paves the way for the use of a top-down gel-free proteomics strategy in barley for investigating more complex traits such as malting quality. Differential abundance of hordoindoline proteins impact the seed hardness trait of barley cultivars.

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The mechanisms involved in bleomycin-induced lung toxicity have not been fully understood to date. This work aimed to compare the proteome profiling of bleomycin-induced lung toxicity by using 2D-nano-LC-MS/MS and spectral counting. By comparing the spectral counts of identified proteins between control and bleomycin-treated groups, we noted that 102 proteins were upregulated and 28 proteins were downregulated in the bleomycin-treated group. Among these differently expressed proteins, five proteins were chosen for validation by Western blot analysis. The levels of these five proteins were consistent with proteomic results. These potential mediators can facilitate the translation of the underlying mechanisms of bleomycin-induced lung toxicity to molecular targets in the clinical arena.

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BACKGROUND: A targeted analysis of the 50kDa C-terminal fragment of insulin-response element binding protein-1 (IRE-BP1) activation of target genes through the insulin receptor substrate receptor/PI-3 kinase/Akt pathway has been demonstrated for the insulin growth factor-1 receptor. The broader effects of 50kDa C-terminal IRE-BP1 fragment over-expression on protein abundance in pancreatic islet beta cells have not been determined. RESULTS: Liquid-chromatography coupled to tandem mass spectrometry (LC-MS/MS) analyses of replicate lysates of pancreatic islets isolated from background strain animals and transgenic animals, overexpressing IRE-BP1 in pancreatic islet beta cells, demonstrated statistically significant increases in the expression of proteins involved in protein synthesis, endoplasmic reticulum (ER) stress and scaffolding proteins important for protein kinase C signaling; some of which were confirmed by immunoblot analyses. Bioinformatic analysis of protein expression network patterns suggested IRE-BP1 over-expression leads to protein expression patterns indicative of activation of functional protein networks utilized for protein post-translational modification, protein folding, and protein synthesis. Co-immunoprecipitation experiments demonstrate a novel interaction between two differentially regulated proteins receptor for activated protein kinase C (RACK1) and translationally controlled tumor protein (TCTP). CONCLUSIONS: Proteomic analysis of IRE-BP1 over-expression in pancreatic islet beta cells suggest IRE-BP1 (a) directly or indirectly through establishing hyperglycemia results in increased expression of ribosomal proteins and markers of ER stress and (b) leads to the enhanced and previously un-described interaction of RACK1 and TCTP. SIGNIFICANCE: This study identified C-terminal 50kDa domain of IRE-BP1 over-expression results in increased markers of ER-stress and a novel interaction between the scaffolding proteins RACK1 and TCTP.

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Reliable quantification of low-abundance proteins in complex proteomes is challenging largely owing to the limited number of spectra/peptides identified. In this study we developed a straightforward method to improve the quantitative accuracy and precision of proteins by strategically retrieving the less confident peptides that were previously filtered out using the standard target-decoy search strategy. The filtered-out MS/MS spectra matched to confidently-identified proteins were recovered, and the peptide-spectrum-match FDR were re-calculated and controlled at a confident level of FDR≤1%, while protein FDR maintained at ~1%. We evaluated the performance of this strategy in both spectral count- and ion current-based methods. >60% increase of total quantified spectra/peptides was respectively achieved for analyzing a spike-in sample set and a public dataset from CPTAC. Incorporating the peptide retrieval strategy significantly improved the quantitative accuracy and precision, especially for low-abundance proteins (e.g. one-hit proteins). Moreover, the capacity of confidently discovering significantly-altered proteins was also enhanced substantially, as demonstrated with two spike-in datasets. In summary, improved quantitative performance was achieved by this peptide recovery strategy without compromising confidence of protein identification, which can be readily implemented in a broad range of quantitative proteomics techniques including label-free or labeling approaches. SIGNIFICANCE: We hypothesize that more quantifiable spectra and peptides in a protein, even including less confident peptides, could help reduce variations and improve protein quantification. Hence the peptide retrieval strategy was developed and evaluated in two spike-in sample sets with different LC-MS/MS variations using both MS1- and MS2-based quantitative approach. The list of confidently identified proteins using the standard target-decoy search strategy was fixed and more spectra/peptides with less confidence matched to confident proteins were retrieved. However, the total peptide-spectrum-match false discovery rate (PSM FDR) after retrieval analysis was still controlled at a confident level of FDR≤1%. As expected, the penalty for occasionally incorporating incorrect peptide identifications is negligible by comparison with the improvements in quantitative performance. More quantifiable peptides, lower missing value rate, better quantitative accuracy and precision were significantly achieved for the same protein identifications by this simple strategy. This strategy is theoretically applicable for any quantitative approaches in proteomics and thereby provides more quantitative information, especially on low-abundance proteins.

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Label-free quantitative methods are advantageous in bottom-up (shotgun) proteomics because they are robust and can easily be applied to different workflows without additional cost. Both label-based and label-free approaches are routinely applied to discovery-based proteomics experiments and are widely accepted as semiquantitative. Label-free quantitation approaches are segregated into two distinct approaches: peak-abundance-based approaches and spectral counting (SpC). Peak abundance approaches like MaxLFQ, which is integrated into the MaxQuant environment, require precursor peak alignment that is computationally intensive and cannot be routinely applied to low-resolution data. Not limited by these constraints, SpC approaches simply use the number of peptide identifications corresponding to a given protein as a measurement of protein abundance. We show here that spectral counts from multidimensional proteomic data sets have a mean-dispersion relationship that can be modeled in edgeR. Furthermore, by simulating spectral counts, we show that this approach can routinely be applied to large-scale discovery proteomics data sets to determine differential protein expression.

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