ArticleScientific data2023
MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- TRPA1 deficiency alters brown adipose tissue proteome and thermogenic gene expression during cold exposure in mice.Biochemistry and biophysics reports · 2026Article
- In-depth analysis of data characteristics and comparative evaluation of dda and dia accuracy in label-free quantitative proteomics of biological samples.Clinical proteomics · 2025Article
- Protrec2: tissue-specific network-based missing protein recovery method.Briefings in bioinformatics · 2025Article
- Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome.BMC veterinary research · 2025Article
- Spatial Proteomics by Parallel Accumulation-Serial Fragmentation Supported MALDI MS/MS Imaging: A First Glance Into Multiplexed and Spatial Peptide Identification.Rapid communications in mass spectrometry : RCM · 2025Article
- MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search.Nature communications · 2025Article
- Article
- MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search.bioRxiv : the preprint server for biology · 2024Article
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction.International journal of molecular sciences · 2024Article
- MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.Scientific data · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.