Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines

By Mingcong Wang, Christina J. Herrmann, Milan Simonovic, Damian Szklarczyk, C. von Mering. Proteomics, 2015

533 citations recorded by the source corpus.

Protein quantification at proteome‐wide scale is an important aim, enabling insights into fundamental cellular biology and serving to constrain experiments and theoretical models. While proteome‐wide quantification is not yet fully routine, many datasets approaching proteome‐wide coverage are becoming available through biophysical and MS techniques. Data of this type can be accessed via a variety of sources, including publication supplements and online data repositories. However, access to the data is still fragmentary, and comparisons across experiments and organisms are not straightforward. Here, we describe recent updates to our database resource “PaxDb” (Protein Abundances Across Organisms). PaxDb focuses on protein abundance information at proteome‐wide scope, irrespective of the underlying measurement technique. Quantification data is reprocessed, unified, and quality‐scored, and then integrated to build a meta‐resource. PaxDb also allows evolutionary comparisons through precomputed gene orthology relations. Recently, we have expanded the scope of the database to include cell‐line samples, and more systematically scan the literature for suitable datasets. We report that a significant fraction of published experiments cannot readily be accessed and/or parsed for quantitative information, requiring additional steps and efforts. The current update brings PaxDb to 414 datasets in 53 organisms, with (semi‐) quantitative abundance information covering more than 300 000 proteins.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/3d12a9226f256773396d234fb80c7b97e6a37a92

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Citations in the corpus

The functional landscape of the human phosphoproteome (2019, background): “To evaluate the phosphoproteome coverage, we studied the proportion of phosphoproteins while stratifying them according to their abundance 22 .”
Integrated interactions database: tissue-specific view of the human and model organism interactomes (2015, methodology): “A protein was considered expressed in a tissue if its level based on Human Protein Atlas (30) was anything other than ‘Not detected’ or its level based on PaxDb (31) was greater than 0.”
The protein expression profile of ACE2 in human tissues (2020, unknown): “ACE2 mass spectrometry-based expression among different tissues was assessed by retrieving data in two proteomics databases: Protein abundance database (PaxDB) (Wang et al , 2015) and ProteomicsDB (Schmidt et al , 2018).”
2016 update of the PRIDE database and its related tools (2015, background): “In addition to the PX resources, there are other valuable proteomics databases and resources available, providing protein expression information derived from MS proteomics data, most notably the Global Proteome Machine Database (GPMDB) (10), ProteomicsDB (11), the Human Proteome Map (12), MaxQB (13)”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, background): “STRING shares its genome, protein-and name spaces with a number of sister projects, dedicated to orthology (eggNOG (32)), small molecules (STITCH (33)), protein abundances (PaxDB (34)), tissue expression (TISSUES (35)) and viruses (Viruses.”
The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets (2020, methodology): “Protein abundance is taken from PaxDB (60), expressed in parts-per-million (log-scale).”
Selective Autophagy: ATG8 Family Proteins, LIR Motifs and Cargo Receptors. (2020, background): “The number of NBR1 molecules in a cell is generally much lower than the number of p62 molecules [138], and there is no evidence that NBR1 prevents polymerization of p62 in vivo.”