MASCOT search results of 62-kDa protein purified from rice leaves

We present the adaptability of Mascot search engine for automated identification of intact glycopeptide mass spectra. The steps involved in adopting Mascot for intact glycopeptide analysis include: i) assigning unique one letter codes for monosaccharides, ii) linearizing glycan sequences and iii) preparing custom glycoprotein databases. Stepped normalized collision energy (NCE) for HCD mostly provided both the peptide and glycan information in a single MS2 spectrum. Using standard glycoproteins, we showed that Mascot can be adopted for automated annotation of both N- and O-linked glycopeptides. In a large scale validation study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in serum samples. This represent a single tool that collectively allows the i) elucidation of N- and O-linked glycopeptide spectra, ii) matching glycopeptides to known protein sequences, and iii) high-throughput, batch wise analysis of large scale glycoproteomics data sets.

Database used for MASCOT search in manuscript LARVAL X-RAY IRRADIATION INFLUENCES PROTEIN EXPRESSION IN PUPAE OF THE ORIENTAL FRUIT FLY, BACTROCERA DORSALIS

28711 Global export shipment records of Mascot with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

fasta file of amino acid sequences used as MASCOT database for protein identification

2471 Global import shipment records of Mascot with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides information about the number of properties, residents, and average property values for A Drive cross streets in Mascot, TN.

This dataset provides information about the number of properties, residents, and average property values for Staff Drive cross streets in Mascot, TN.

Tandem mass spectrometry-based proteomics experiments produce large amounts of raw data, and different database search engines are needed to reliably identify all the proteins from this data. Here, we present Compid, an easy-to-use software tool that can be used to integrate and compare protein identification results from two search engines, Mascot and Paragon. Additionally, Compid enables extraction of information from large Mascot result files that cannot be opened via the Web interface and calculation of general statistical information about peptide and protein identifications in a data set. To demonstrate the usefulness of this tool, we used Compid to compare Mascot and Paragon database search results for mitochondrial proteome sample of human keratinocytes. The reports generated by Compid can be exported and opened as Excel documents or as text files using configurable delimiters, allowing the analysis and further processing of Compid output with a multitude of programs. Compid is freely available and can be downloaded from http://users.utu.fi/lanatr/compid. It is released under an open source license (GPL), enabling modification of the source code. Its modular architecture allows for creation of supplementary software components e.g. to enable support for additional input formats and report categories.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The use of internal calibrants (the so called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics. However, the polydimethylcyclosiloxane (PCM) peaks commonly used for this purpose are quite unreliable, leading to missing calibrant peaks in spectra and correspondingly lower mass measurement accuracy. Therefore, we here introduce a universally applicable and robust internal calibrant, the tripeptide Asn3. We show that Asn3 is a substantial improvement over PCM both in terms of consistent detection and resulting mass measurement accuracy. Asn3 is also very easy to adopt in the lab, as it requires only minor adjustments to the analytical setup. Data analysis: For mass measurement accuracy (MMA) calculations and comparisons, the following Mascot workflow was used. From the MS/MS data in each LC run, Mascot Generic Files were created using Distiller software (version 2.4.3.3, Matrix Science, London, UK, www.matrixscience.com/distiller.html). These peak lists were then searched with the Mascot search engine (Matrix Science) using the Mascot Daemon interface (version 2.4.0, Matrix Science). Spectra were searched against the Swiss-Prot database (version 13_04 of UniProtKB/Swiss-Prot protein database containing 20,232 sequence entries of human proteins) concatenated with its reversed sequence database. Variable modifications were set to pyro-glutamate formation of amino terminal glutamine and acetylation of the protein N-terminus, whereas fixed modifications only included oxidation of methionine. Mass tolerance on peptide ions was set to 10 ppm (with Mascot’s C13 option set to 1), and the mass tolerance on peptide fragment ions was set to 20 millimass units (mmu), except for the space-charge effect experiment(LMA5) where an extra search was done with a setting of 3 mmu. The peptide charge was set to 1+,2+,3+ and instrument setting was put on ESI-QUAD. Enzyme was set to trypsin allowing for one missed cleavage, and cleavage was allowed when arginine or lysine is followed by proline. Only peptides that were ranked one and scored above the threshold score, set at 99% confidence, were withheld. All data was processed and managed by ms_lims.

104 Active Global Mascot buyers list and Global Mascot importers directory compiled from actual Global import shipments of Mascot.

The data and deep learning segmentation model deposited here are derived from 3D multicoloured intravital microscopy of mammary epithelial cells during development. We aimed to study in vivo cell shape dynamics in real-time in an unbiased way. This robust and deep analysis revealed that hormone-responsive breast cells are unexpectedly elongated and motile at a high frequency during duct growth. The data is associated with our publication Dawson, Milevskiy et al, Cell Reports 2024, Hormone-responsive progenitors have a unique identity and exhibit high motility during mammary morphogenesis. https://doi.org/10.1016/j.celrep.2024.115073

Deposited data- Single channel intravital movie maximum projections (File:MaSCOT-AI Max projections). These are up to 5 hours long, with timepoints every 10 minutes.- Extracted 5th time points from each movie that we used for model training (File:MaSCOT-AI t5 training)- Segmentation files generated by Cellpose 2.2.2 (File: MaSCOT-AI t5 segmentation files)

Analysis scripts:The Trackmate-Cellpose python script, R data processing scripts and example excel data sheet are on github at https://github.com/cadaws/MaSCOT-AI

Example analysis and data export:A small set of example data and resulting trackmate-Cellpose output will be uploaded at a later date.

Methods27 4D movies were acquired every 10 minutes by multiphoton microscopy of anaesthetised cell-type-specific confetti mice at different stages of development. 350 single channel, single-cell thick layers (10-30 µm sections) were isolated by 3D cropping, then flattened by max projection. The 5th time point from all movies was taken for model training in Cellpose 2.2.2, which was achieved after manual correction of segmentation for 150 images (MaSCOT-AI model).

The MaSCOT-AI model was used in a high throughput Trackmate-Cellpose script in ImageJ to track mammary cell shape over time.

Software versions:Cellpose 2.2.2 GUI with GPU was installed according to https://pypi.org/project/cellpose/ (March 2024).Trackmate v7.11.1

File name structureDate_mouse-model_developmental-stage_fluorescent-protein_z-span

Mouse models:K5: K5-rtTA/tetoCre/ConfettiElf5: Elf5-rtTA/tetoCre/ConfettiPr: PR-Cre/Confetti

Developmental stage:no label = Terminal end bud at 5 weeksduct/notpreg = duct at 6 or 9 weeks6dPreg/6dplug = 6 days pregnancy6d MPA = 6 days MPA treatmentMPAveh = 6 days MPA vehicle treatment

This dataset provides information about the number of properties, residents, and average property values for Shipe Road cross streets in Mascot, TN.

Access Mascot import export data of global countries with importers' & exporters' details, shipment date, price, hs code, ports, quantity etc.

MASCOT Model and Data Matt Luckcuckm.luckcuck@tutanota.com 06-07-2020 A Communicating Sequential Processes (CSP) model of the MASCOT v.6 Safety Sub-System and the response time data from checking various traces using FDR and our Runtime Verification toolchain Varanus. ## Description The CSP model was built from a natural-language report of the (proposed) safety sub-system for the tele-operated robotic system MASCOT. A set of traces were constructed to test the model: * Stress-Testing: increasingly large, semi-random traces to test how model-checking/Varanus response times scale, and; * Scenarios: different 'attempts' at a hypothetical mission, designed to test all of the safety functions in the model. Each trace was checked using FDR, directly; and then using our Varanus toolchain, both online and offline. The check is to determine if the trace is a valid trace of the model. Further description of our toolchain and a link to relevant paper(s) can be found in the Varanus [repository](Varanus ## Scenarios Briefly, the scenarios are: 1. Operator stays in hands on mode, speed stays below limit. 2. Operator stays in hands on mode, speed exceeds limit and tries to continue (causes a failure). - 2a Instead of the failure in Scenario 2, the system handles the broken speed limit, then resets, restarts, and finishes the mission. - 2b Instead of the failure in Scenario 2, the system handles the broken speed limit, the safe state key is removed, to allow minor servicing to the system. Then the key is returned, the system is reset, restarted, and the mission is completed. 3. Operator switches to autonomous mode after collecting tools, speed stays below limit. 4. Operator switches to autonomous mode after collecting tools, speed exceeds limit and tries to continue (causes a failure). - 4a Instead of the failure in Scenario 4, the system handles the broken speed limit, then resets, restarts, and finishes the mission. - 4b Instead of the failure in Scenario 4, the system handles the broken speed limit, then safe state key is removed, to allow minor servicing to the system. Then the key is returned, the system is reset, restarted, and the mission is completed. 5. The Safe State Key is used to trigger an emergency stop. Then the system is reset, restarted, and the mission is completed. 6. System enters Master Commissioning Mode. After some unmonitored movements (not triggering protective stop), Safe State Key is used to enter Safe State, and system is reset. 7. The Slave Commissioning Mode key is used to put the system into the Slave Commissioning Mode, where no speed events are registered. Then Slave Commissioning Mode is disabled, again using the Slave Commissioning Mode key. ## Structure * data: the raw log files ('logs') and a spreadsheet of the FDR and Varanus results * model: the CSP model of the safety sub-system, including the stress-test ('scenarios-stress-tests.csp' and scenario ('scenarios.csp') traces

Global O-GlcNAc profiling: proteome-wide purification and identification of O-GlcNAc proteins using GlcNAz metabolic labeling in combination with Click chemistry and label-free LC-MS/MS-based quantification. OGA inhibition: same workflow as 'Global O-GlcNAc profiling'; inhibition of O-GlcNAcase by a small molecule inhibitor. O-GlcNAc sites: identification of O-GlcNAc sites by metabolic labeling/Click chemistry followed by beta elimination Bioinformatics workflow of Global O-GlcNAc profiling and OGA inhibition experiments: Raw mass spectrometry files have been processed using Progenesis LC-MS (4.0) and exported as Mascot generic format for subsequent Mascot (2.3) database search. Mascot search results were processed using Mascot Percolator and the results have been imported back into Progenesis as well as imported to Scaffold (3.5.1). The Progenesis peptide quantification report has been exported as Excel file. Bioinformatics workflow of O-GlcNAc site identification: Raw mass spectrometry files have been processed using Mascot Distiller 2.3 and searched with Mascot. Mascot search results were processed using the Mascot Percolator and imported into Scaffold (3.5.1). After manual validation, peptide and protein identifications (Scaffold file) were imported into Scaffold PTM 2.0 for the estimation of site localization probabilities.

The dataset contains data from the MASCOT magnetometer during measured during the landing on asteroid Ryugu on October 3, 2018. The dataset provides archive for data analysis used to determine Ryugu's magnetic properties as presented in paper "Magnetic Properties of Asteroid (162173) Ryugu" (Hercik et al.).

The submitted dataset contains raw files from 96 synthetic peptide libraries, using either HCD or ETD as fragmentation technique. The synthesized 96 tryptic peptide libraries containing >100,000 unmodified peptides plus their corresponding >100,000 phosphorylated counterparts with precisely known sequences and modification sites. All these libraries were subjected to LC-MS/MS on an Orbitrap mass spectrometer using HCD and ETD fragmentation. The generated mass spectrometric data deposited in this database can be used in numerous ways to develop, evaluate and improve experimental and computational proteomic strategies. Raw MS data files were converted into Mascot generic format files (MGF) using Mascot Distiller (2.4.2.0, www.matrixscience.com). Important parameters included: i) signal to noise ratio of 20 for MS/MS and ii) time domain off (no merging of spectra of the same precursor). The MGF files were searched against human IPI v3.72 including the sequences of all 96 libraries,using the Mascot search engine (2.3.1, 24). Search settings: Decoy search using a randomized version of the human IPI v3.72 including the sequences of all 96 libraries was enabled; monoisotopic peptide mass (considering up to two 13C isotopes); trypsin/P as protease; a maximum of four missed cleavages; peptide charge +2 and +3; peptide tol. +/- 5 ppm; MS/MS tol. +/- 0.02 Da; instrument type ESI-Trap (for HCD data) or ETD-Trap (for ETD data) respectively; variable modifications: oxidation (M), phospho (ST), phospho (Y). The result files were exported to pepXML and Mascot XML with default options provided by Mascot.

Phosphopeptide identification is still a challenging task because fragmentation spectra obtained by mass spectrometry do not necessarily contain sufficient fragment ions to establish with certainty the underlying amino acid sequence and the precise phosphosite. To improve upon this, it has been suggested to acquire pairs of spectra from every phosphorylated precursor ion using different fragmentation modes, for example CID, ETD, and/or HCD. The development of automated tools for the interpretation of these paired spectra has however, until now, lagged behind. Using phosphopeptide samples analyzed by an LTQ-Orbitrap instrument, we here assess an approach in which, on each selected precursor, a pair of CID spectra, with or without multistage activation (MSA or MS2, respectively), are acquired in the linear ion trap. We applied this approach on phosphopeptide samples of variable proteomic complexity obtained from Arabidopsis thaliana. We present a straightforward computational approach to reconcile sequence and phosphosite identifications provided by the database search engine Mascot on the spectrum pairs, using two simple filtering rules, at the amino acid sequence and phosphosite localization levels. If multiple sequences and/or phosphosites are likely, they are reported in the consensus sequence. Using our program FragMixer, we could assess that on samples of moderate complexity, it was worth combining the two fragmentation schemes on every precursor ion to help efficiently identify amino acid sequences and precisely localize phosphosites. FragMixer can be flexibly configured, independently of the Mascot search parameters, and can be applied to various spectrum pairs, such as MSA/ETD and ETD/HCD, to automatically compare and combine the information provided by these more differing fragmentation modes. The software is openly accessible and can be downloaded from our Web site at http://proteomics.fr/FragMixer.