International Cell Senescence Association
Discover Genes Related to Senescence

SeneQuest: A resource site for discovering genes related to senescence

About SeneQuest


Information on gene-to-senescence associations is crucial for increasing our understanding of the molecular mechanisms driving senescence. Currently this information seems to be scattered in small databases as well as in the literature. We believe that the creation of a central data hub gathering all the available information, is of paramount importance and will greatly facilitate information retrieval as well as the formation of more robust evidence driven research hypotheses. This was the reason for the creation of Senequest, a literature-based evidence database of genes related to senescence. This data resource is made freely available to the scientific community through


The Quertle Artifical Intelligence AI-powered literature search engine [] was used to identify publications that contained genes that are up- or down-regulated in senescence. Quertle uses AI to recognize various entities such as genes, proteins, diseases, etc and the relationships among them for unstructured data sources such as the biomedical literature. Given that the AI has yet to match the human capability for information retrieval from such unstructured and diverse sources as the biomedical literature, it is expected that the identification of all relevant literature through such tools will be to some extent incomplete. Apart from Quertle a second list was created out of Entrez GeneRIF entries [] that included the keyword "senescence". Approximately 3300 publications containing at least one link between the expression status of the factor under investigation (gene-protein or miRNA) and senescence were extracted and manually curated. In this list all high-throughput studies available during the preparation of this manuscript were analyzed. Additional information regarding cell-lines applied, tissue types and disease status mentioned in the publication were included. If more than one gene were found related to senescenceper examined publication, this information was also extracted and utilized. 13,015such associations (up- or down-regulation) with senescence were identified. Filtering for unique relations resulted in 7,969 genes encoding proteins and miRNAs. Each gene in the database is connected with multiple literature evidence, which is displayedin the form of PubMed IDs, describing the relationship of the gene’s expression status(up- or down-regulation)with senescence.Interactions of genes are also stored in the database and the user can search for interactants of a specific gene that are also connected with senescence. Additionaly, Gene Ontology (GO) codes are associated with each gene. SeneQuest provides the ability for the user to search for senescence-associated genes that are linked to a specific GO-term or any of its descendants. All evidence is linked to one or multiple PubMed IDs that the user can immediately view by selecting the corresponding links. Finally cell-line and tissue type information are also stored in the database for each gene-to-senescence association and is searchable through the the user interface. Given its dynamic nature, SeneQuest will be continuously updatedby members of the International Cell Senescence Associationand new information from additional databases (such as be constantly incorporated. Database updates will be made public twice a year.

The site will be updated twice a year by members of the International Cell Senescence Association
-ICSA consortium

SeneQuest ver3, release 4-August-2021 Prepared and curated for the International Cell Senescence Association - ICSA ( by:


Vassilis G. Gorgoulis: Professor (
Konstantinos Evangelou: Associate Professor
Athanassios Kotsinas: Assistant Professor
Sofia Havaki: Assistant Professor

Panagiotis Vasileiou
Dimosthenis Chrysikos

PhD students
Dimitris Veroutis
Sophia Rizou
Sophia Theodorou
Eleni Sertedaki
Romanos Georgios Foukas
Andreas Dargaras

MSc students
Panagiotis-Georgios Passias, MD
Eleni Kardala
Eleni Damianidou
Artemis Stathopoulou
Antonios Giannelos
Konstantinos Kelepouras
Ioannis Chiotakakos
Camelia Sidahmet

Undergraduate Students
Menelaos Samaras
Vassiliki Spyrou
Agapi-Ilionti Skouloudaki
Konstantina-Ioanna Panagiotopoulou
Lamprini Mpounou
Ioannis Kapetanios
Konstantinos Karampinos

External Scientific Collaborators
Panagiota Tsioli

Technical advisory
Intelligencia (