Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

timsTOF Pro datasets for CCS prediction with Mascot-Percolator.

ABSTRACT: Tryptic digested E. coli dataset fractionated by SCX stagetip and applied to test the CCS prediction model. The Peaklist from the MS/MS spectra as mascot generic format (MGF) was created by compass DataAnalysis 5.2.216.271.5315. Protein and peptides were identified by by Mascot-Percolator.

ORGANISM(S): Escherichia Coli

SUBMITTER: Yasushi Ishihama

PROVIDER: PXD026087 | JPOST Repository | Mon Sep 06 00:00:00 BST 2021

REPOSITORIES: jPOST

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	201020ko17EColi_Fr02.txt	Txt
	201020ko17EColi_Fr02_Slot1-01_1_13272.tdf	Other
	201020ko17EColi_Fr02_Slot1-01_1_13272.tdf_bin	Other
	201020ko18EColi_Fr03.txt	Txt
	201020ko19EColi_Fr04.txt	Txt

Items per page:

1 - 5 of 20

Publications

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry.

Chang Chih-Hsiang CH Yeung Darien D Spicer Victor V Ogata Kosuke K Krokhin Oleg O Ishihama Yasushi Y

Journal of proteome research 20210616

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported model ...[more]

PMID: 34133192

Similar Datasets

timsTOF Pro datasets for CCS prediction

Project description:Tryptic digested E. coli dataset fractionated by SCX stagetip and applied to test the CCS prediction model.

2021-09-06 | PXD022800 | JPOST Repository

timsTOF Pro datasets for CCS prediction

Project description:The contribution of peptide amino-acid sequence to collision cross-section values (CCS) has been investigated using a dataset of ~134,000 peptides of four different charge states (1+ to 4+). The migration data collection was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters, we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent intrinsic size parameters were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ~0.98 for the entire population of peptides.

2021-09-06 | PXD021440 | JPOST Repository

Ionmob: A Python Package for Prediction of Peptide Collisional Cross-Section Values

Project description:Motivation: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion’s mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by post-translational modifications (PTMs) of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven and offer the possibility to be easily adapted to individual workflows for experimental design and data processing. Results: We created ionmob, a Python based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈ 21.000 unique phosphorylated peptides and ≈ 17.000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

2023-09-21 | PXD043026 | JPOST Repository

Glycoproteomic Analyses of Influenza A Viruses Using timsTOF Pro MS.

Project description:N-Linked glycosylation in hemagglutinin and neuraminidase glycoproteins of influenza viruses affects antigenic and receptor binding properties, and precise analyses of site-specific glycoforms in these proteins are critical in understanding the antigenic and immunogenic properties of influenza viruses. In this study, we developed a glycoproteomic approach by using a timsTOF Pro mass spectrometer (MS) to determine the abundance and heterogeneity of site-specific glycosylation for influenza glycoproteins. Compared with a Q Exactive HF MS, the timsTOF Pro MS method without the hydrophilic interaction liquid chromatography column enrichment achieved similar glycopeptide coverage and quantities but was more effective in identifying low-abundance glycopeptides. We quantified the distributions of intact site-specific glycopeptides in hemagglutinin of A/chicken/Wuxi/0405005/2013 (H7N9) and A/mute swan/Rhode Island/A00325125/2008 (H7N3). Results showed that hemagglutinin for both viruses had complex N-glycans at N22, N38, N240, and N483 but only high-mannose glycans at N411 and, however, that the type and quantities of glycans were distinct between these viruses. Collisional cross section (CCS) provided by the ion mobility spectrometry from the timsTOF Pro MS data differentiated sialylation linkages of the glycopeptides. In summary, timsTOF Pro MS method can quantify intact site-specific glycans for influenza glycoproteins without enrichment and thus facilitate influenza vaccine development and production.

| S-EPMC10225981 | biostudies-literature

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multi-Dimensional Prediction

Project description:Here, a workflow of 4D DIA proteomics by using the predicted multi-dimensional in silico library was established. A deep learning model Deep4D that could high-accurately predict the CCS and RT of both the unmodified and phosphorylated peptides was developed, which is the model based on self-attention14 that completely avoid the use of recurrent neural network (RNN) or LSTM. Deep4D exhibited higher accuracy in the prediction of CCS and RT of peptides than the current models based on deep learning. By using Deep4D and MS/MS prediction tool, an integrated in silico library containing CCS, RT, and fragment ion intensities of millions of peptides for 4D DIA proteomics was established based on the SwissProt H. sapiens database, which enables the deeper peptide and proteome coverage for human samples compared to using sample-specific experimental library. We further demonstrate that the introduction of in silico prediction library can greatly complement the experimental library of directly obtained phosphorylated peptides, resulting in a greater increase in the identification of phosphorylated peptides and phosphorylated proteins.

2023-04-19 | PXD034553 |

Barcode-free prediction of cell lineages from scRNA-seq datasets

Project description:Barcode-free prediction of cell lineages from scRNA-seq datasets

| PRJNA939121 | ENA

caArray_golub-00299: Chemosensitivity prediction by transcriptional profiling

2015-05-16 | GSE68936 | GEO

Genomic prediction Fagus sylvatica

Project description:Genomic prediction Fagus sylvatica

| PRJEB60881 | ENA

caArray_golub-00299: Chemosensitivity prediction by transcriptional profiling

Project description:In an effort to develop a genomics-based approach to the prediction of drug response, we have developed an algorithm for classification of cell line chemosensitivity based on gene expression profiles alone. Using oligonucleotide microarrays, the expression levels of 6,817 genes were measured in a panel of 60 human cancer cell lines (the NCI-60) for which the chemosensitivity profiles of thousands of chemical compounds have been determined. We sought to determine whether the gene expression signatures of untreated cells were sufficient for the prediction of chemosensitivity. Gene expression-based classifiers of sensitivity or resistance for 232 compounds were generated and then evaluated on independent sets of data. The classifiers were designed to be independent of the cells' tissue of origin. The accuracy of chemosensitivity prediction was considerably better than would be expected by chance. Eighty-eight of 232 expression-based classifiers performed accurately (with P < 0.05) on an independent test set, whereas only 12 of the 232 would be expected to do so by chance. These results suggest that at least for a subset of compounds genomic approaches to chemosensitivity prediction are feasible. golub-00299 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Tissue Sites: Kidney, Brain, Blood, Lung, Skin, Colon, Breast, Prostate, Ovary Material Types: cell, synthetic_RNA, whole_organism, total_RNA Disease States: Carcinoma, Glioblastoma, Lymphoma, Adenocarcinoma, Melanoma, Amelanotic Melanoma, Large Cell Carcinoma, Precursor Lymphoblastic Leukemia, Carcinosarcoma, Myeloid Leukemia, Plasma Cell Myeloma, Bronchogenic Carcinoma, Chronic Myelogenous Leukemia, Squamous Cell Carcinoma

2015-05-16 | E-GEOD-68936 | biostudies-arrayexpress

Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro.

Project description:Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

| S-EPMC8349881 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data