Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Benchmark Dataset DIA Clinical Proteomics LymphNodes E.coli

ABSTRACT: Real patient variability Benchmark Dataset for Optimization of DIA data analysis workflows

PROVIDER: EGAD00010002223 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Benchmarking DIA-type Proteomics Using Large-Scale Inter-Patient Heterogeneity Dataset

Project description:Benchmarking Proteomics Quantitation in DIA-type data using real patient material to create a benchmark dataset comprising inter-patient heterogeneity

| EGAS00001005589 | EGA

LiP-MS data analysis workflows with a hybrid LiP proteome benchmark

Project description:To evaluate informatics workflows for DIA-based LiP-MS, we generated a high-quality benchmark data set comprising more than 170,000 LiP peptides with defined composition. We then performed comprehensive assessment of major DIA analysis platforms incorporating different spectral libraries, and introduced DIA-LiPQuan, an informatics pipeline tailored to DIA LiP-MS quantification and downstream analysis. Data re-analysis by DIA-LiPQuan with in silico libraries allows sensitive and robust detection of both site-specific structural remodeling of proteins and drug-bound protein targets from the cellular proteome. Collectively, our study provides a valuable benchmark resource and informatics package for LiP-MS data mining, which would facilitate its broader applications in structural proteomics and drug discovery.

| PXD077171 |

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients - Optimized Astral dataset

Project description:Recent advancements in liquid chromatography-mass spectrometry (LC-MS) have increasingly focused on high-throughput workflows, leveraging rapid chromatographic gradients and minimal sample input to maximize proteome coverage from limited material. This shift is particularly driven by the rise of single-cell proteomics, where sensitivity and reproducibility are critical. Building on our previous benchmark dataset (PXD028735), we now present an expanded study utilizing the latest generation of LC-MS platforms optimized for high-throughput proteomics. This study features shorter LC gradients and lower sample input to address the growing need for rapid and sensitive proteome analysis. Using a standardized hybrid proteome mixture with defined ratios of Human, Yeast, and E. coli, we generated a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset across multiple state-of-the-art LC-MS platforms. The updated dataset incorporates the latest acquisition methodologies and extends coverage across an even broader range of data formats, including enhanced ion mobility-enabled and scanning quadrupole-based acquisitions. Our results providea detailed assessment of the impact of technological advancements and demonstrate how shortening LC gradients influence proteome coverage, quantitative precision, and data consistency across instruments

2026-02-03 | PXD071205 | Pride

PIA - iPRG2008 Benchmark Dataset

Project description:This dataset is no actual new study but the iPRG2008 benchmark dataset used in the PIA manuscript.

2015-05-08 | PXD000793 | Pride

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

2026-02-03 | PXD070049 | Pride

Metagenome benchmark

Project description:Benchmark of metagenome analysis tools

| PRJEB8286 | ENA

PIA - Yeast Gold Standard Benchmark Dataset

Project description:This Dataset is no actual new study but the Yeast Gold Standard benchmark dataset used in the PIA manuscript.

2015-05-08 | PXD000792 | Pride

LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Project description:Recent advances in liquid chromatography–mass spectrometry (LC-MS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) that was generated using next-generation LC-MS platforms optimized for rapid proteome analysis. With shorter LC gradients and lower sample amounts, we generated an extensive DDA/DIA dataset on a standardized human-yeast-E. coli hybrid proteome. This new dataset includes data acquired by the Orbitrap Astral, which combines an Orbitrap with a time-of-flight (TOF) mass analyzer, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and support the continued standardization of short-gradient, high-throughput LC-MS-based proteomics. 

2025-10-31 | PXD070173 | panorama