The dataset include the following figures and tables: 1)Changes in protein expression of the 14 pathway regulators induced by Ni (II). 2)Hierarchical clustering of 12 differentially expressed or phosphorylated proteins in BEAS-2B cells treated with Ni (II). 3) Relative cell survival (X-axis) vs. protein expression or phosphorylation levels (Y-axis) in BEAS-2B control cells treated with Ni (II) at 4 different concentrations 4)Four representative proteins, PDIA1, ACADM, RUVBL1, PRDX2 identified using 2-DE profiling were either increased or decreased in a concentration responsive manner 5)Networks of proteins showing inter-relationships and pathways which was obtained using IPA 6)Schematic representation of the interplay of the core proteins and cytotoxicity pathways mediated by Ni (II). 7) some supplementary data. This dataset is associated with the following publication: Ge , Y., M. Bruno , N. Coates , K. Wallace , D. Andrews , A. Swank , W. Winnik , and J. Ross. Proteomic Assessment of Biochemical Pathways That Are Critical to Nickel-Induced Toxicity Responses in Human Epithelial Cells. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 11(9): 1-20, (2016).

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100 000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC–MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

MGVB is a collection of tools for proteomics data analysis. It covers data processing from in silico digestion of protein sequences to comprehensive identification of postranslational modifications and solving the protein inference problem. The toolset is developed with efficiency in mind. It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. MGVB, as it is a native application, is much faster than existing proteomics tools such as MaxQuant and MSFragger and, in the same time, finds very similar, in some cases even larger number of peptides at a chosen level of statistical significance. It implements a probabilistic scoring function to match spectra to sequences, and a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. This report describes the algorithms behind the tools, presents benchmarking data sets analysis comparing MGVB performance to MaxQuant/Andromeda, and provides step by step instructions for using it in typical analytical scenarios. The toolset is provided free to download and use for academic research and in software projects, but is not open source at the present. It is the intention of the author that it will be made open source in the near future—following rigorous evaluations and feedback from the proteomics research community.

This Excel file lists the samples uploaded in PRIDE. The table “Table Sorted PP and Replicates” in the Excel file has all the relevant annotation.

There are more than the expected 168 samples in the PRIDE upload for the following reasons:

First, all of the measurements from the experiment had been uploaded, including files for measurements that were repeated because of problems during the MS run. These samples are not annotated in the table. Second, we had included 4 Gold Standard samples (2 replicates on each of the two large gels used to process all samples). These 4 gold standard samples in 7 fractions explain 28 extra samples. Third, we did not have 168 but 166 samples in the photoperiod set. Fractions 1 and 2 of sample 43 (Photoperiod 2, bio replicate 1, tech. replicate 2) were lost during sample preparation. While the remaining fractions were measured and are included in the PRIDE upload and the table, this sample was not used in the data analysis. Photoperiod 2 bio rep. 1 was only used with one technical replicate in the calculations.

We analyzed the proteomic data and DIDAR filtered/QC'ed proteomic data from a recent study of 270 single human cells divided between control and sotorasib treatments. The data included here is the processed results using Proteome Discoverer 2.4 using the same search parameters. This data is in support of Figure 2 of Jenkins and Orsburn 2022.

Proteomic workflows generate vastly complex peptide mixtures that are analyzed by liquid chromatography–tandem mass spectrometry, creating thousands of spectra, most of which are chimeric and contain fragment ions from more than one peptide. Because of differences in data acquisition strategies such as data-dependent, data-independent or parallel reaction monitoring, separate software packages employing different analysis concepts are used for peptide identification and quantification, even though the underlying information is principally the same. Here, we introduce CHIMERYS, a spectrum-centric search algorithm designed for the deconvolution of chimeric spectra that unifies proteomic data analysis. Using accurate predictions of peptide retention time, fragment ion intensities and applying regularized linear regression, it explains as much fragment ion intensity as possible with as few peptides as possible. Together with rigorous false discovery rate control, CHIMERYS accurately identifies and quantifies multiple peptides per tandem mass spectrum in data-dependent, data-independent or parallel reaction monitoring experiments.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) analyzes cancer biospecimens by mass spectrometry, characterizing and quantifying their constituent proteins, or proteome. Proteomic analysis for each CPTAC study is carried out independently by Proteomic Characterization Centers (PCCs) using a variety of protein fractionation techniques, instrumentation, and workflows. Mass spectrometry and related data files are organized into datasets by study, sub-proteome, and analysis site.

A comprehensive proteomics data and process provenance ontology.

Proteomics dataset of 344 formalin-fixed paraffin-embedded (FFPE) tissue samples from patients with Diffuse large B-cell lymphoma (DLBCL). 2033 unmodified and 198 modified (containing co-/post-translational modifications) peptides were measured via label-free micro-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra - mass spectrometry (microLC-SWATH-MS).

The project contains raw and result files from a comparative proteomic analysis of malignant [primary breast tumor (PT) and axillary metastatic lymph nodes (LN)] and non-tumor [contralateral (NCT) and adjacent breast (ANT)] tissues of patients diagnosed with invasive ductal carcinoma. A label-free mass spectrometry was conducted using nano-liquid chromatography coupled to electrospray ionization–mass spectrometry (LC-ESI-MS/MS) followed by functional annotation to reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups of samples analyzed. Ingenuity Pathway Analysis software version 2.3 (QIAGEN Inc.) was employed to identify the most relevant signaling and metabolic pathways, diseases, biological functions and interaction networks affected by the deregulated proteins. Upstream regulator and biomarker analyses were also performed by IPA’s tools. Altogether, our findings revealed differential proteomic profiles that affected the associated and interconnected cancer signaling processes.

Milk samples were collected from Holstein and Jersey breeds of dairy cattle maintained under the same management practices and environmental conditions over a seven-day period. Milk samples were collected twice daily from six cows of each breed as described in Tacoma et al., 2016. Samples were composited within individual cow over the experimental period and skimmed to remove the fat layer. Skimmed milk samples were fractionated using CaCl2 precipitation, ultracentrifugation and ProteoMiner treatment to remove the high abundance milk proteins. Separation of the low abundance proteins was achieved using SDS-PAGE. Differential protein abundances were analyzed by mass spectrometry-based proteomic approaches followed by statistical analyses of the peptide count data. The complete list of low-abundance proteins identified in both breeds is provided in this dataset as well as the total number of distinct sequenced peptides and gene ontology functions for each protein. The relative abundance of a select few proteins is depicted using the SIEVE software. Data are presented in Excel format (zipped for download): Supplemental Table 1: Identification of gene ontology functions of the full protein profile between different breeds; Supplemental Table 2: The complete list of the low-abundance proteins identified as well as peptide count data from both breeds. Resources in this dataset:Resource Title: Comparative proteomics dataset of skimmed milk samples from Holstein and Jersey dairy cattle. File Name: Web Page, url: https://www.sciencedirect.com/science/article/pii/S2352340916000445 Data in Brief article presenting the complete list of low-abundance proteins identified in both breeds as well as the total number of distinct sequenced peptides and gene ontology functions for each protein. Data are presented in Excel format (zipped for download): Supplemental Table 1: Identification of gene ontology functions of the full protein profile between different breeds; Supplemental Table 2: The complete list of the low-abundance proteins identified as well as peptide count data from both breeds.

To unbiasedly evaluate the quantitative performance of different quantitative methods, and compare different popular proteomics data processing workflows, we prepared a benchmark dataset where the various levels of spikeed-in E. Coli proteome that true fold change (i.e. 1 fold, 1.5 fold, 2 fold, 2.5 fold and 3 fold) and true identities of positives/negatives (i.e. E.Coli proteins are true positives while Human proteins are true negatives) are known. To best mimic the proteomics application in comparison of multiple replicates, each fold change group contains 4 replicates, so there are 20 LC-MS/MS analysis in this benchmark dataset. To our knowledge, this spike-in benchmark dataset is largest-scale ever that encompasses 5 different spike level, >500 true positive proteins, and >3000 true negative proteins (2peptide criteria, 1% protein FDR), with a wide concentration dynamic range. The dataset is ideal to test quantitative accuracy, precision, false-positive biomarker discovery and missing data level.

A Plant Proteome DataBase for Arabidopsis thaliana and maize (Zea mays). The PPDB stores experimental data from in-house proteome and mass spectrometry analysis, curated information about protein function, protein properties and subcellular localization. Importantly, proteins are particularly curated for possible (intra) plastid location and their plastid function. Protein accessions identified in published Arabidopsis (and other Brassicacea) proteomics papers are cross-referenced to rapidly determine previous experimental identification by mass spectrometry. All protein-encoding gene models in the Arabidopsis nuclear and organellar genomes, as assembled by TAIR, as well as all maize EST assemblies (ZmGI) as assembled by DFCI Maize Gene Index project. These are all uploaded in PPDB and are linked to each other via a BLAST alignment. Thus every predicted protein in both species can be searched for experimental and other information (even if not experimentally identified).

Proteomics data collected as part of the manuscript by Tanner et al (2019) Frontiers in Plant Science Abstract: The temporal pattern of accumulation of hordein storage proteins in developing barley grains was studied by enzyme-linked immunosorbent assay (ELISA), western blot and liquid chromatography tandem mass spectrometry (LC-MS/MS). Hordein accumulation was compared to the pattern seen for two abundant control proteins, serpin Z4 (an early accumulator) and lipid transferase protein (LTP1, a late accumulator). Hordeins were detected from six days post-anthesis (DPA) and peaked at 30 DPA. Changes in fresh weight indicate that desiccation begins at 20 DPA and by 37 DPA fresh weight had decreased by 35%. ELISA analysis of hordein content, expressed on a protein basis, increased to a maximum at 30 DPA followed by a 17% decrease by 37 DPA. The accumulation of 39 tryptic and 29 chymotryptic hordein peptides representing all classes of hordein was studied by LC-MS/MS. Most peptides increased to a maximum at 30 DPA, and either remained at the maximum or did not decrease significantly. Only five tryptic peptides, members of the related B1- and γ1-hordeins decreased significantly by 21-51% at 37 DPA. Thus, the concentration of some specific peptides was reduced while remaining members of the same family were not affected. The N-terminal signal region was removed by proteolysis during co-translation. In addition to a suite of previously characterised hordeins, two novel barley B-hordein isoforms mapping to wheat low molecular weight glutenins (LMW-GS-like B-hordeins), and two avenin-like proteins (ALPs) sharing homology with wheat ALPs, were identified. These identified isoforms have not previously been mapped in the barley genome. Cereal storage proteins provide significant nutritional content for human consumption and seed germination. In barley, the bulk of the storage proteins comprise the hordein family and the final hordein concentration affects the quality of baked and brewed products. It is therefore important to study the accumulation of hordeins as this knowledge may assist plant breeding for improved health outcomes (by minimizing triggering of detrimental immune responses), nutrition and food processing properties. Lineage: Proteins extracted from developing barley grain, digested with either trypsin or chymotrypsin. Peptides were analysed by LC-MS on a 6500QTRAP LC-MS system. Raw data deposited in .wiff/.wiff.scan format.

This dataset provides a simulation of proteomics data preprocessing workflows.

It focuses on the application of K-Nearest Neighbors (KNN) imputation to handle missing values.

Principal Component Analysis (PCA) is applied for dimensionality reduction and visualization of high-dimensional proteomics data.

The dataset demonstrates an end-to-end preprocessing pipeline for proteomics datasets.

Includes synthetic or real-like proteomics data suitable for educational and research purposes.

Designed to help researchers, bioinformaticians, and data scientists learn preprocessing techniques.

Highlights the impact of missing data handling and normalization on downstream analysis.

Aims to improve reproducibility of proteomics data analysis through a structured workflow.

Useful for testing machine learning models on clean and preprocessed proteomics data.

Supports hands-on learning for KNN imputation, PCA, and data visualization techniques.

Helps users understand the significance of preprocessing in high-throughput biological data analysis.

Provides code and explanations for a complete pipeline from raw data to PCA visualization.

In this experiment we wanted to compare between tumor and healthy status in breast cancer. To achieve this goal we performed a mass-spectrometry based proteomic analysis of two breast cancer cell lines (HCC) and one healthy cell line (HMEC) as control. From each cell line we created three biological replicates. Observe the proteomic data matrix, where rows are proteins and columns are samples.

1. Some of the rows contain nan values. Suggest 1-2 ways to handle missing data and write a script that performs one of them.
2. A common practice in proteomics data is to log2 normalize the data. Normalize the attached data matrix and plot the samples before and after normalization.
3. Examine the entire dataset in an unsupervised method of your choice and export a relevant figure that briefly describes the results.

Please submit a working notebook with embedded figures and results.

Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

The mass spectrometry proteomics data

This article contains consolidated proteomic data obtained from xylem sap collected from tomato plants grown in Fe- and Mn-sufficient control, as well as Fe-deficient and Mn-deficient conditions. Data presented here cover proteins identified and quantified by shotgun proteomics and Progenesis LC-MS analyses: proteins identified with at least two peptides and showing changes statistically significant (ANOVA; p ≤ 0.05) and above a biologically relevant selected threshold (fold ≥ 2) between treatments are listed. The comparison between Fe-deficient, Mn-deficient and control xylem sap samples using a multivariate statistical data analysis (Principal Component Analysis, PCA) is also included. Data included in this article are discussed in depth in "Effects of Fe and Mn deficiencies on the protein profiles of tomato (Solanum lycopersicum) xylem sap as revealed by shotgun analyses", Ceballos-Laita et al., J. Proteomics, 2018. This dataset is made available to support the cited study as well to extend analyses at a later stage. Resources in this dataset:Resource Title: ProteomeExchange submission PXD007517. Xylem sap shotgun proteomics from Fe- and Mn-deficient and Mn-toxic tomato plants. . File Name: Web Page, url: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD007517 The MS proteomics data have been deposited to the ProteomeXchange Consortium via the Pride partner repository with the data set identifier PXD007517. Also includes FTP location. Files available at https://www.ebi.ac.uk/pride/archive/projects/PXD007517 via HTML, FTP, or Fast (Aspera) download : 1 SEARCH.xml file, 1 Peak file, 24 RAW files, 1 Mascot information.xlsx file. Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.dib.2018.01.034

Centralized, standards compliant, public data repository for proteomics data, including protein and peptide identifications, post-translational modifications and supporting spectral evidence. Originally it was developed to provide a common data exchange format and repository to support proteomics literature publications. This remit has grown with PRIDE, with the hope that PRIDE will provide a reference set of tissue-based identifications for use by the community. The future development of PRIDE has become closely linked to HUPO PSI. PRIDE encourages and welcomes direct user submissions of protein and peptide identification data to be published in peer-reviewed publications. Users may Browse public datasets, use PRIDE BioMart for custom queries, or download the data directly from the FTP site. PRIDE has been developed through a collaboration of the EMBL-EBI, Ghent University in Belgium, and the University of Manchester.