Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Youn_et_al_RNAbodies_SAINT3108_MSPLIT_DDA (PAIRED with MSV000081419)

ABSTRACT: This set of submissions contains the mass spectrometry files acquired using Data-Dependent Acquisition mode (DDA), which were paired with Data-Independent Acquisition mode (DIA) files (MSV000081419) for the manuscript by Ji-Young Youn et al. that describes the high-density proximity mapping of RNA bodies. These DDA files were used to generate spectral library in MSPLIT analysis of both DDA and DIA files. The paired SAINT 3108 set entails BioID experiments of stress granule and Processing-body baits expressed in Flp-In T-REx HEK293 cells, treated or untreated with sodium arsenite during labeling period.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Anne-Claude Gingras

PROVIDER: MSV000081948 | MassIVE | Wed Jan 17 13:31:00 GMT 2018

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Publications

High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies.

Youn Ji-Young JY Dunham Wade H WH Hong Seo Jung SJ Knight James D R JDR Bashkurov Mikhail M Chen Ginny I GI Bagci Halil H Rathod Bhavisha B MacLeod Graham G Eng Simon W M SWM Angers Stéphane S Morris Quaid Q Fabian Marc M Côté Jean-François JF Gingras Anne-Claude AC

Molecular cell 20180125 3

mRNA processing, transport, translation, and ultimately degradation involve a series of dedicated protein complexes that often assemble into large membraneless structures such as stress granules (SGs) and processing bodies (PBs). Here, systematic in vivo proximity-dependent biotinylation (BioID) analysis of 119 human proteins associated with different aspects of mRNA biology uncovers 7424 unique proximity interactions with 1,792 proteins. Classical bait-prey analysis reveals connections of hundr ...[more]

PMID: 29395067

Similar Datasets

Youn_et_al_RNAbodies_SAINT3090_MSPLIT_DDA (PAIRED with MSV000081414)

Project description:This set of submissions contains the mass spectrometry files acquired using Data-Dependent Acquisition mode (DDA), which were paired with Data-Independent Acquisition mode (DIA) files (MSV000081414) for the manuscript by Ji-Young Youn et al. that describes the high-density proximity mapping of RNA bodies. These DDA files were used to generate spectral library in MSPLIT analysis of both DDA and DIA files. The paired SAINT 3090 set entails APMS experiments of stress granule baits treated or untreated briefly with sodium arsenite and these were were performed from Flp-In T-REx HEK293 cells and MS files were acquired on AbSciex Triple TOF 6600 instrument in data-independent acquisition mode.

2018-01-17 | MSV000081947 | MassIVE

Youn_et_al_RNAbodies_SAINT3073_MSPLIT_DDA (Paired with MSV000081422)

2018-01-17 | MSV000081943 | MassIVE

Youn_et_al_RNAbodies_SAINT3112_MSPLIT_DDA (PAIRED with MSV000081426)

2018-01-17 | MSV000081945 | MassIVE

iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control

Project description:Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metric exhibit higher sensitivity compared to DDA-based QC metric in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.

2024-11-08 | PXD052871 | Pride

A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer

Project description:Generation of a new library of targeted mass spectrometry assays for accurate protein quantification in triple negative breast cancer (TNBC) tissues. Primary tumor tissue lysates from 105 TNBC patients treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic, were used to generate the spectral library. This project covers raw files from data-dependent acquisition (DDA) – parallel accumulation-serial fragmentation (PASEF) measurements of 12 hydrophilic chromatography (HILIC) fractions of aliquot pool from complete set of 105 samples measured on timsTOF Pro; raw files of 16 individual samples measured in data-independent acquisition (DIA) – PASEF mode and used for hybrid library generation and for demonstrative quantitative DIA data extraction; Pulsar archive generated in Spectronaut 16.0 from 12 DDA-PASEF measurements of HILIC fractions and from 16 data-independent acquisition DIA-PASEF measurements of individual samples. The 16 DIA-PASEF runs of individual samples used for library generation were analyzed using newest versions of Spectronaut (version 18.5) and DIA-NN (version 1.8.1) software tools in library-based setting using the newly generated library as well as in library-free setting showing library-based method to outperform the use of predicted libraries in the terms of identification numbers.

2024-05-31 | PXD047793 | Pride

Comparison of Full-Scan, Data-Dependent, and Data-Independent Acquisition Modes in Liquid Chromatography-Mass Spectrometry Based Untargeted Metabolomics

Project description:Full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA) are the three common data acquisition modes in high resolution mass spectrometry-based untargeted metabolomics. It is an important yet underrated research topic on which acquisition mode is more suitable for a given untargeted metabolomics application. In this work, we compared the three data acquisition techniques using a standard mixture of 134 endogenous metabolites and a human urine sample. Both hydrophilic interaction and reversed-phase liquid chromatographic separation along with positive and negative ionization modes were tested. Both the standard mixture and urine samples generated consistent results. Full-scan mode is able to capture the largest number of metabolic features, followed by DIA and DDA (53.7% and 64.8% respective features fewer on average in urine than full-scan). Comparing the MS2 spectra in DIA and DDA, spectra quality is higher in DDA with average dot product score 83.1% higher than DIA in Urine(H), and the number of MS2 spectra (spectra quantity) is larger in DIA (on average 97.8% more than DDA in urine). Moreover, a comparison of relative standard deviation distribution between modes shows consistency in the quantitative precision, with the exception of DDA showing a minor disadvantage (on average 19.8% and 26.8% fewer features in urine with RSD < 5% than full-scan and DIA). In terms of data preprocessing convenience, full-scan and DDA data can be processed by well-established software. In contrast, several bioinformatic issues remain to be addressed in processing DIA data and the development of more effective computational programs is highly demanded.

2020-05-21 | MTBLS1572 | MetaboLights

Specter - Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

Project description:These data cover all of the analyses described in the paper "Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics". Specifically, the data consist of - 20 DIA and DDA files from the HEK293T/synthetic phosphopeptides spike-in experiments - 10 DDA files, one for each of ten fractions of an E. coli lysate digest - 14 DIA files for the experiments involving mixtures of synthetic peptides - 11 DDA files for the isolated runs of these synthetic peptides - 84 DIA files for measurements of the phosphoproteome of perturbed PC3 cells - 10 DDA files for spectral library construction for the phosphoproteomics data - 3 DIA and 3 DDA files for analysis of an unfractionated E. coli lysate digest. See the spreadsheet "Specter Datasets Catalog.xlsx" for further descriptions and file metadata.

2018-03-19 | PXD006722 | Pride

Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics

Project description:<p>Despite the growing popularity of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, no study has yet to systematically compare the performance of different data acquisition modes in the discovery of significantly altered metabolic features, which is an important task of untargeted metabolomics for identifying clinical biomarkers and elucidating disease mechanism in comparative samples. In this work, we performed a comprehensive comparison of three most commonly used data acquisition modes, including full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA), using a metabolomics study of human plasma samples from leukemia patients before and after one-month chemotherapy. After optimization of data processing parameters, we extracted and compared statistically significant metabolic features from the results of each data acquisition mode. We found that most significant features can be consistently found in all three data acquisition modes with similar statistical performance as evaluated by Pearson correlation and receiver operating characteristic (ROC) analysis. Upon comparison, DDA mode consistently generated fewer uniquely found significant features than full-scan and DIA modes. We then manually inspected over 2000 uniquely discovered significant features in each data acquisition mode and showed that these features can be generally categorized into four major types. Many significant features were missed in DDA mode, primarily due to its low capability of detecting or extracting these features from raw LC-MS data. We thus proposed a bioinformatic solution to rescue these missing significant features from the raw DDA data with good reproducibility and accuracy. Overall, our work asserts that data acquisition modes can influence metabolomics results, suggesting room for improvement of data acquisition modes for untargeted metabolomics.</p>

2020-10-02 | MTBLS1842 | MetaboLights

DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics

Project description:This dataset consists of 44 raw MS files, comprising 27 DIA (SWATH) and 15 DDA runs on a TripleTOF 5600 and of two raw mass spectrometry files acquired on a Q Exactive. The composition of the dataset is described in the manuscript by Tsou et al., titled: "DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics", Nature Methods, in press Raw files are deposited here in ProteomeXchange and are associated with the DIA-Umpire processed data. All DIA-Umpire processed results for each sample together with DDA results are deposited in separated folders. Also see the "DataSampleID.xlsx" associated with this Readme file. Internal reference from the Gingras lab ProHits implementation: Project 94, Export version VS2 (Tsou_DIA-Umpire)

2015-01-21 | PXD001587 | Pride

Thivierge_etal_Drosha_BioID_2024

Project description:This dataset consists of the files for 22 BioID samples: .wiff and .wiff.scan raw MS files, .mzML peak files, and MSPLIT results files; all acquired on TripleTOF 6600 mass spectrometers operated in Data Independent Acquisition mode (SWATH). It also includes the sequence database used for analysis and the MSPLIT spectral library which was generated from paired samples acquired in Data Dependent Acquisition (DDA) mode. All streptavidin affinity purification, mass spectrometric acquisition, and data analysis was performed by Boris Dyakov. Samples were provided by Thomas Duchaine's lab (McGill University). The files are associated with a manuscript in Cell Reports by Thivierge et al. that explores the paraspeckle-independent co-transcriptional regulation of nuclear microRNA biogenesis by SFPQ, wherein SFPQ is identified as a proximally-associated protein for DROSHA. Only the Wildtype DROSHA bait data is used in the publication, but all files used in statistical analysis are provided. Thomas Duchaine is the corresponding author of the manuscript (thomas.duchaine@mcgill.ca) Anne-Claude Gingras should be contacted for questions about this dataset (gingras@lunenfeld.ca)

2024-03-25 | MSV000094396 | MassIVE

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