It is well-known how sequencing technologies propelled cel- lular biology research in the latest years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequenc- ing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Therefore, this work presents a meta-analysis of the Gene Activity matrix based on the Genomic-Annotated Gene Ac- tivity Matrix model, aiming to investigate the different influences of its contributions on the activity and their correlation with the expression. This allows having a better grasp on how the different functional regions of the genome affect not only the activity but also the expression of the genes.

Meta-analysis of Gene Activity (MAGA) Contributions and Correlation with Gene Expression, Through GAGAM / Martini, L.; Bardini, R.; Savino, A.; Di Carlo, S.. - ELETTRONICO. - 13920:(2023), pp. 193-207. (Intervento presentato al convegno 10th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2023) tenutosi a Meloneras, Gran Canaria (ESP) nel 12-14 July 2023) [10.1007/978-3-031-34960-7_14].

Meta-analysis of Gene Activity (MAGA) Contributions and Correlation with Gene Expression, Through GAGAM

Martini L.;Bardini R.;Savino A.;Di Carlo S.
2023

Abstract

It is well-known how sequencing technologies propelled cel- lular biology research in the latest years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequenc- ing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Therefore, this work presents a meta-analysis of the Gene Activity matrix based on the Genomic-Annotated Gene Ac- tivity Matrix model, aiming to investigate the different influences of its contributions on the activity and their correlation with the expression. This allows having a better grasp on how the different functional regions of the genome affect not only the activity but also the expression of the genes.
2023
978-3-031-34959-1
978-3-031-34960-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2981392