Evidence map›Paper›PMID 41171287›Full record

ArticleDatabase : the journal of biological databases and curation2025

ROSBASE1.0: a comprehensive database of reactive oxygen species (ROS): categorization of cell organelles, proteins, taxonomy, and diseases based on ROS-related activities.

Sharayu Ghodeswar, Debashree Bandyopadhyay

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Sharayu GhodeswarDepartment of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Jawahar Nagar, Kapra Mandal, Medchal District, Hyderabad, Telangana, 500078, India.
Debashree BandyopadhyayDepartment of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Jawahar Nagar, Kapra Mandal, Medchal District, Hyderabad, Telangana, 500078, India.ORCID 0000-0003-4131-907X

Funding

Council for Scientific and Industrial Research 09/1026(0032)-2020-EMR-I
6 · The paper itself

Abstract

The reactive oxygen species (ROS) produced by various cell organelles often relate to pathophysiology or pathogenesis. Information on ROS-producing, scavenging, and regulatory proteins from various taxonomic kingdoms and organelles, enzyme classes, and diseases was scattered across the literature. Consolidation of all information would facilitate the researchers to understand ROS-related pathology and possible therapeutics. For the first time, we developed a secondary database, ROSBASE1.0, consolidating ROS protein features, taxonomy, diseases, and interactome data. Data sources were PubMed, UniProt, STRING, and Google Scholar databases. Notably, 81.5% of the reported data were experimentally curated. The ROS mechanism was elucidated for 79.2% of entries based on text-mining from the literature. A total of 2494 experimentally annotated entries (UniProt IDs) were curated in the database. Multiple PDB IDs correspond to individual UniProt IDs, resulting higher number of PDB entries (n = 9174). The database reported 2447 genes, 16 cell organelles, 395 organisms, and 218 disease entries (corresponding to 143 proteins) classified into 14 categories. In this database, proteins were classified based on taxonomic kingdoms, enzyme classes, and diseases. The keywords to search the database were-UniProt ID, PDB ID, cell organelle, organism name, and gene name. The tabular results depict all the above features, in addition to the interactome network. The database (https://rosbase.bits-hyderabad.ac.in/) for the first time quantitatively demonstrated the preferences of lower organisms towards ROS-scavenging versus ROS production by higher organisms. Categorization of different diseases according to their ROS involvement was also reported here for the first time. This database could potentially serve as a user guide to ROS Biology.

Indexed as

Databases, ProteinOrganellesProteinsReactive Oxygen SpeciesAnimalsData MiningDiseaseHumansProteinsReactive Oxygen Species

Identifiers

PMID41171287
PMCPMC12576790

What Socratic holds

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LicenceCC BY
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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 Socratic graph.