Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic reanalysis of co-fractionation mass spectrometry data

ABSTRACT: A total of 206 co-fractionation mass spectrometry (CF-MS) experiments were downloaded from public proteomics repositories such as PRIDE and MASSIVE, then re-analyzed with MaxQuant.

INSTRUMENT(S): Thermo Finnigan instrument model

ORGANISM(S): Homo Sapiens (human) Caenorhabditis Elegans Oryza Sativa (rice) Trypanosoma Brucei Xenopus Laevis (african Clawed Frog) Saccharomyces Cerevisiae (baker's Yeast) Zea Mays (maize) Arabidopsis Thaliana (mouse-ear Cress) Triticum Aestivum (wheat) Dictyostelium Discoideum (slime Mold) Drosophila Melanogaster (fruit Fly) Mus Musculus (mouse)

SUBMITTER: Michael Skinnider

LAB HEAD: Leonard Foster

PROVIDER: PXD022048 | Pride | 2021-04-20

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MaxQuantoutput.tar	Other
	raw-files.csv.zip	Csv

Items per page:

1 - 2 of 2

Publications

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.

Skinnider Michael A MA Foster Leonard J LJ

Nature methods 20210701 7

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computatio ...[more]

PMID: 34211188

Similar Datasets

CFdb: A harmonized resource of co-fractionation mass spectrometry data

Project description:A total of 411 co-fractionation mass spectrometry (CF-MS) experiments were downloaded from public proteomics repositories such as PRIDE and MassIVE, then re-analyzed with MaxQuant. This repository provides complete MaxQuant outputs from all 411 experiments.

2024-01-26 | PXD042664 | Pride

Reanalysis of Pandey lab's draft of the human proteome

Project description:The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides was lacking. To this end, Akhilesh Pandey's lab reported a draft map of the human proteome based on high resolution Fourier transform mass spectrometry-based proteomics technology, which included an in-depth proteomic profiling of 30 histologically normal human samples including 17 adult tissues, 7 fetal tissues and 6 purified primary hematopoietic cells ( http://dx.doi.org/10.1038/nature13302 ). The profiling resulted in identification of proteins encoded by greater than 17,000 genes accounting for ~84% of the total annotated protein-coding genes in humans. This large human proteome catalog (available as an interactive web-based resource at http://www.humanproteomemap.org) complements available human genome and transcriptome data to accelerate biomedical research in health and disease. Pandey's lab and collaborators request that those considering use of this primary dataset for commercial purposes contact pandey@jhmi.edu. The full details of this study can be found in the PRIDE database: www.ebi.ac.uk/pride/archive/projects/PXD000561/. This ArrayExpress entry represents a top level summary of the metadata only which formed the basis of the reanalysis performed by Joyti Choudhary's team ( jc4@sanger.ac.uk ), results of which are presented in the Expression Atlas at EMBL-EBI : http://www.ebi.ac.uk/gxa/experiments/E-PROT-1.

2015-03-10 | E-PROT-1 | biostudies-arrayexpress

Proteogenomics reanalysis of human testis data

Project description:Reanalysis of the human testis tissue dataset acquired for the Chromosome-centric Human Proteome Project (C-HPP; PRIDE project PXD002179), with a focus on identifying novel peptides for genome annotation.

2019-06-13 | MSV000083973 | MassIVE

Validating pyAscore, A Python Package for the Localization of Protein Modifications

Project description:The submitted dataset contains results of validation experiments for pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. In order to test the versatility of the package, three samples from three pride accessions were re-analyzed: label-free phosphoprotome samples were downloaded from PXD007740, TMT-labeled phosphoproteome samples were downloaded from PXD007145, and label free acetylome samples were downloaded from MSV000079068. We also wanted to evaluate the performance of pyAscore's PTM localizations on data where the modification site of peptides was known. Thus, we downloaded and re-analyzed synthetic phosphopeptide samples which were analyzed with 3 different fragmentation approaches: HCD and ETD synthetic phosphopeptide data were downloaded from PXD000138 and CID data were downloaded from PXD000759.

2022-10-31 | PXD032908 | Pride

PeptideShaker - A complex standard for protein identification, designed by evolution

Project description:Mass spectrometry-based proteomics data, obtained experimentally or via public repositories, remains challenging to analyze. This challenge is particularly felt when re-analysing or re-purposing public data sets, endeavours that otherwise hold great potential. PeptideShaker is an open source system that greatly simplifies the interpretation and dissemination of proteomics data, and that automates the re-analysis of public data, thus completing the proteomics data cycle (http://peptide-shaker.googlecode.com).

2014-06-24 | PXD001077 | Pride

Proteogenomics reanalysis of human testis data

2016-11-28 | PXD004785 | Pride

Systematic screening of chromatin factors identifies divergent roles across haematopoiesis and leukaemia [scRNA-Seq]

Project description:Cellular differentiation-trajectories require extensive alterations in chromatin structure and function, elicited by a diverse array of chromatin-factors (CFs). Using haematopoiesis as a model-system, we combined bulk ex vivo and single-cell in vivo CRISPR screens to comprehensively characterise the role of CF families in cell-fate decisions. We uncover marked lineage specificities for many CFs, dissecting specific CF-dependencies in vivo at single-cell resolution. This highlights functional diversity among related CFs (i.e. BAF-subcomplexes, MLL-methyltransferases), but also reveals functional redundancy among unrelated Repressive-complexes that restrain excessive myeloid differentiation. Epigenetic-profiling, molecularly explains CF lineage-dependencies, identifying differentiation stage-specific interactions between individual CFs and lineage-determining Transcription Factors (TFs). Intriguingly, non-canonical BAF remodeler disruption abrogates myeloid-TF function generating a pre-leukaemia phenotype. Screening CF functions in leukemia, we show that leukaemia cells abrogate normal CF function, restraining differentiation trajectories by engaging in novel CF-TF interactions that suggest potential therapeutic avenues. Together, our work greatly elucidates the role of CFs and their TF-partners across normal and malignant haematopoiesis.

2023-05-23 | GSE213511 | GEO

Global transcriptome analysis in fast-growing Arabidopsis thaliana with elevated level of ATP [shoots]

Project description:Higher ATP supply in AtPAP2 overexpression line is the driving force for its fast-growth phenotypes, leading to massive changes in its transcriptome and metabolome. The OE lines are previously described in Sun et al., New Phytol. 2012 Apr;194(1):206-19.

2014-08-25 | GSE40306 | GEO

Global transcriptome analysis in fast-growing Arabidopsis thaliana with elevated level of ATP [roots]

2014-08-25 | GSE40305 | GEO

Microenvironment Cell Populations Affymetrix 133 A

Project description:Human tissues harbour a wide array of cellular populations, often including both immune and lymphoid cell populations, endothelial cells and fibroblasts. The goal of this study was to identify transcriptomic markers of these cell populations, in order to quantify their abundances using transcriptomic data. The present super-series is composed of a large number of transcriptomic profiles of these cell populations. These profiles were obtained from public repositories, normalized using the fRMA R package (which allows a consistent normalization of profiles across public series), and consistently annotated. This series corresponds to Affymetrix 133 A profiles. Sample's annotation is available as supplementary material.

2016-09-02 | GSE86363 | GEO