Database containing several body fluid proteomes, including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 22, 2016.A database on the proteome of rice that contains reference maps based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of proteins from rice tissues and subcellular compartments.

It archives data on more than 700 proteins that were identified by multiple mass spectrometry (MS) analyses from highly purified preparations of human nucleoli the most prominent nuclear organelle. Each protein entry is annotated with information about its corresponding gene its domain structures and relevant protein homologues across species as well as documenting its MS identification history including all the peptides sequenced by tandem MS/MS. Moreover, data showing the quantitative changes in the relative levels of 500 nucleolar proteins are compared at different timepoints upon transcriptional inhibition. Correlating changes in protein abundance at multiple timepoints highlighted by visualization means in the NOPdb provides clues regarding the potential interactions and relationships between nucleolar proteins and thereby suggests putative functions for factors within the 30% of the proteome which comprises novel/ uncharacterized proteins. The NOPdb is searchable by either gene names protein sequences Gene Ontology terms or motifs or by limiting the range for isoelectric points and/or molecular weights and links to other databases (e.g. LocusLink OMIM and PubMed).

A fasta-formatted database of 36,866,870 predicted proteins representing 4,351 unique species from 117 phyla., A database of 36,866,870 predicted proteins representing 4,351 unique species from 117 phyla (see table below) was constructed using the UniProt Reference Proteome (RP) at the 35% co-membership threshold including 4,295 Representative Proteome Groups (RPGs) (Chen et al. 2011) in addition to all taxonomically identifiable transcriptomes of the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) (Keeling et al. 2014) that were processed through WinstonCleaner (https://github.com/kolecko007/WinstonCleaner). The database also included proteins inferred from the annotated and assembled genomes of Aurantiochytrium limacinum ATCC MYA-1381, Schizochytrium aggregatum ATCC 28209, and Aplanochytrium kerguelensis PBS07 from the U.S. Department of Energy’s Joint Genome Institute (JGI), all PFAM PF00494 Aurantiochytrium sp. KH105 proteome hits from the Okinawa Institute of Science and Technology Marine Genomics Unit genome browser, all of UniProt's annotated Hondaea fermentalgiana pr...,

The missing human proteome comprises predicted protein-coding genes with no credible protein level evidence detected so far and constitutes ∼18% of the human protein coding genes (neXtProt release 19/9/2014). The missing proteins may be of pharmacological interest as many of these are membrane receptors, thus requiring comprehensive characterization. In the present study, we explored various computational parameters, crucial during protein searches from tandem mass spectrometry (MS) data, for their impact on missing protein identification. Variables taken into consideration are differences in search database composition, shared peptides, semitryptic searches, post-translational modifications (PTMs), and transcriptome guided proteogenomic searches. We used a multialgorithmic approach for protein detection from publicly available mass spectra from recent studies covering diverse human tissues and cell types. Using the aforementioned approaches, we successfully detected 24 missing proteins (22-PE2, 1-PE4, and 1-PE5). Maximum of these identifications could be attributed to differences in reference proteome databases, exemplifying use of a single standard database for human protein detection from MS data. Our results suggest that search strategies with modified parameters can be rewarding alternatives for extensive profiling of missing proteins. We conclude that using complementary spectral data searches incorporating different parameters like PTMs, against a comprehensive and compact search database, might lead to discoveries of the proteins attributed so far as the missing human proteome.

A facultative anaerobic bacterium, Bacillus cereus is one member of a larger family of microorganisms that are closely-related and which are characterized as Bacillus cereus group. Rod shaped and Gram-positive, B. cereus forms colonies with waxy appearance on blood agar. This work sought to provide fundamental information of this bacterium by parsing the UniProt proteome file of B. cereus Rock3-44 with an in-house MATLAB proteome analysis software. The ensuing proteome database comprise protein name, amino acid sequence, number of residues, molecular weight, and nucleotide sequence of all proteins of the bacterium, and should help in a variety of fundamental and applied research involving B. cereus.

These 3 fasta files are widely used proteomics contaminants. The files are

1. crap_gpm.fasta: the common Repository of Adventitious Proteins (cRAP) from the Global Proteome Machine (GPM) organisation.
2. crap_ccp.fasta: Cambridge Centre for Proteomics' own cRAP fasta database.
3. crap_maxquant.fasta.gz: MaxQuant's contaminant database.

These files are extracted from the {camprotR} package and described in the cRAP databases vignette (https://cambridgecentreforproteomics.github.io/camprotR/articles/crap.html).

The Global Proteome Machine Organization was set up so that scientists involved in proteomics using tandem mass spectrometry could use that data to analyze proteomes. The projects supported by the GPMO have been selected to improve the quality of analysis, make the results portable and to provide a common platform for testing and validating proteomics results. The Global Proteome Machine Database was constructed to utilize the information obtained by GPM servers to aid in the difficult process of validating peptide MS/MS spectra as well as protein coverage patterns. This database has been integrated into GPM server pages, allowing users to quickly compare their experimental results with the best results that have been previously observed by other scientists.

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).

Shotgun and positional proteomics study of a mouse embryonic stem cell line. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both SwissProt and RIBO-seq derived translation products, applicable for LC-MSMS spectrum identification. To record the impact of using the constructed deep proteome database we performed two alternative MS-based proteomic strategies: (I) a regular shotgun proteomic and (II) an N-terminal COFRADIC approach. The obtained fragmentation spectra were searched against the custom database (combination of UniProtKB-SwissProt and RIBO-seq derived translation sequences) using three different search engines: OMSSA (version 2.1.9), X!Tandem (TORNADO, version 2010.01.01.04) and Mascot (version 2.3). The first two were run from the SearchGUI graphical user interface (version 1.10.4). A combination of X!Tandem and Mascot was used for the N-terminal COFRADIC analysis, a combination of all three search engines for the shotgun proteome analysis. Note that OMMSA cannot cope with the protease setting semi-ArgC/P needed to analyze N-terminal COFRADIC data.For the shotgun proteome data, trypsin was set as cleavage enzyme allowing for one missed cleavage, and singly to triply charged precursors or singly to quadruple charged precursors were taken into account respectively for the Mascot or X!Tandem/OMSSA search engines, and the precursor and fragment mass tolerance were set to respectively 10 ppm and 0.5 Da. Methionine oxidation to methionine-sulfoxide, pyroglutamate formation of N-terminal glutamine and acetylation (protein N-terminus) were set as variable modifications. For the N-terminal COFRADIC analysis the protease setting semi-ArgC/P (Arg-C specificity with arginine-proline cleavage allowed) was used. No missed cleavages were allowed and the precursor and fragment mass tolerance were also set to respectively 10 ppm and 0.5 Da. Carbamidomethylation of cysteine and methionine oxidation to methionine-sulfoxide and 13C3D2-acetylation of lysines were set as fixed modifications. Peptide N-terminal acetylation or 13C3D2-acetylation and pyroglutamate formation of N-terminal glutamine were set as variable modifications and instrument setting was put on ESI-TRAP. Protein and peptide identification in addition to data interpretation was done using the PeptideShaker algorithm (http://code.google.com/p/peptide-shaker, version 0.18.3), setting the false discovery rate to 1% at all levels (protein, peptide, and peptide to spectrum matching). Aforementioned tools and algorithms (SearchGui, X!Tandem, OMSSA, and PeptideShaker) are freely available as open source.

Alternative pre-mRNA splicing has long been proposed to greatly contribute to proteome complexity. However, the extent to which mature mRNA isoforms are successfully translated into protein remains controversial. Here, we used high-throughput RNA sequencing and mass spectrometry (MS)-based proteomics to better evaluate the translation of alternatively spliced mRNAs. To increase proteome coverage and improve protein quantitation, we optimized cell fractionation and sample processing steps at both the protein and peptide level. Furthermore, we generated a custom spectral library trained on analysis of RNA-Seq data with MAJIQ, an algorithm optimized to detect differential and unannotated junction usage for a given splice site. Using this custom library to match against tandem mass spectra acquired by data independent acquisition (DIA), we improved identification of splicing-derived proteoforms by ~30% as compared to use of the SwissProt database alone. Moreover, our increased depth and detection of proteins allowed us to track changes in the transcriptome and proteome induced by T cell stimulation, as well as fluctuations in protein sub-cellular localization. In sum, our data here confirms that use of generic databases in proteomic studies under-estimates the number of spliced mRNA isoforms that are translated into protein and provides a workflow that improves isoform detection in large-scale proteomic experiments.

This work presents the proteome database of Staphylococcus lentus by parsing its annotated proteome downloaded from UniProt. Comprising protein name, amino acid sequence, number of residues, molecular weight, and nucleotide sequence of each protein in the proteome, the resource should find use in functional genomics studies seeking to unravel the mysteries of this bacterium.

The Global Proteome Machine Database was constructed to utilize the information obtained by GPM servers to aid in the difficult process of validating peptide MS/MS spectra as well as protein coverage patterns.

Database of lipid related proteins representing human and mouse proteins involved in lipid metabolism. Collection of lipid related genes and proteins contains data for genes and proteins from Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae, Caenorhabditis elegans, Escherichia coli, Macaca mulata, Drosophila melanogaster, Arabidopsis thaliana and Danio rerio.

Keeping pace with the rapid growth of proteomic data, the integration of multiproteomic data can improve biomarker identification and cancer diagnosis. However, the data integration needs to overcome substantial challenges owing to considerable variability among diverse data set sources and the extensive range of protein expression levels. In this study, with serum and urine from the same individuals, we established two in-depth paired proteome databases, including 956 serum proteins and 4730 urine proteins. To integrate multiproteomic data, we developed a proteomic-based transfer learning neural network (ProteoTransNet) to enhance the accuracy of bladder cancer diagnosis and progression monitoring. Using random forest analysis on the integrated database, we selected two panels comprising the top 10 key proteins, achieving a diagnostic AUC of 0.996 and a stage classification AUC of 0.914. ProteoTransNet integrates serum and urine proteome databases with proteomic transfer learning, significantly enhancing the diagnostic accuracy through minimizing biases and errors caused by variations in proteomic data. Our study provides insights that transfer learning of sophisticated biological information may solve complicated biological problems in disease diagnosis, prognosis, and treatment.

Proteome analysis of a novel type of virus. The virus was grown in A. castellanii amobea before being purified. Viral particules were enriched after centrifugation and proteins solubilised by SDS before being stacked in the top of a SDS-PGE gel. After in-gel digestion, resulting peptides were injected for a 120min nanoLC-MS/MS analysis using an Ultimate U3000 system and a LTQ-Orbitrap Velos pro hybrid mass spectrometer (Top 20).Data processing and bioinformatics: Data were processed automatically using Mascot Daemon software (version 2.3.2, Matrix Science). Concomitant searches against Pandoravirus and A. castellanii protein sequence databanks as well as classical contaminants database and the corresponding reversed databases were performed using Mascot (version 2.4). ESI-TRAP was chosen as the instrument, trypsin/P as the enzyme and 2 missed cleavage allowed. Precursor and fragment mass error tolerances were set respectively at 10 ppm and 0.6 Da. Peptide modifications allowed during the search were: carbamidomethyl (C, fixes) acetyl (N-ter, variable), oxidation (M, variable) and deamidation (NQ, variable). The IRMa software (Dupierris et al., Bioinformatics, 2009, 25:1980-1, version 1.30.4) was used to filter the results: selection of rank 1 peptides, peptide identification FDR < 1% (as calculated by employing the reverse database strategy), and minimum of 1 specific peptide per identified protein group.

The Proteome 2D-PAGE Database system for microbial research is a curated database for storing and investigating proteomics data. Software tools are available and for data submission, please contact the Database Curator. Established at the Max Plank Institution for Infection Biology, this system contains four interconnected databases: i.) 2D-PAGE Database: Two dimensional electrophoresis (2-DE) and mass spectrometry of diverse microorganisms and other organisms. This database currently contains 4971 identified spots and 1228 mass peaklists in 44 reference maps representing experiments from 24 different organisms and strains. The data were submitted by 84 Submitters from 24 Institutes and 12 nations. It also contains various software tools that are important in formatting and analyzing gels and mass peaks; software include: *TopSpot: Scanning the gel, editing the spots and saving the information *Fragmentation: Fragmentation of the gel image into sections *MS-Screener: Perl script to compare the similarity of MALDI-PMF peaklists *MS-Screener update: MS-Screener can be used to compare mass spectra (MALDI-MS(/MS) as well as ESI-MS/MS spectra) on the basis of their peak lists (.dta, .pkm, .pkt, or .txt files), to recalibrate mass spectra, to determine and eliminate exogenous contaminant peaks, and to create matrices for cluster analyses. *GelCali: Online calibration of the Mr- and pI-axis of 2-DE gels with mathematical regression methods ii.)Isotope Coded Affinity Tag (ICAT)-LC/MS database: Isotope Coded Affinity Tag (ICAT)-LC/MS data for Mycobacterium tuberculosis strain BCG versus H37Rv. iii.) FUNC_CLASS database: Functional classification of diverse microorganism. This database also integrates genomic, proteomic, and metabolic data. iv.) DIFF database: Presentation of differently regulated proteins obtained by comparative proteomic experiments using computerized gel image analysis.

Toxoplasma gondii is a single-cell parasite that infects human host cells during pathogenesis. This work presents the proteome database of T. gondii strain ATCC 50861 by parsing its annotated proteome downloaded from UniProt. Comprising protein name, amino acid sequence, number of residues, molecular weight, and nucleotide sequence of each protein in the proteome, the resource should be useful for gaining a deeper understanding of the pathogenesis mechanism in the parasite.

This work presents the proteome database of Serratia sp. FGI94 by parsing its annotated proteome downloaded from UniProt. Comprising protein name, amino acid sequence, number of residues, molecular weight, and nucleotide sequence of each protein in the proteome, the resource should be useful in gaining a deeper understanding of the metabolism of the organism.

A database of mitochondrial proteomics data. It includes two sets of proteins: the MitoMiner Reference Set, which has 10477 proteins from 12 species; and MitoCarta, which has 2909 proteins from mouse and human mitochondrial proteins. MitoMiner provides annotation from the Gene Ontology (GO) and UniProt databases. This reference set contains all proteins that are annotated by either of these resources as mitochondrial in any of the species included in MitoMiner. MitoMiner data via is available via Application Programming Interface (API). The client libraries are provided in Perl, Python, Ruby and Java.