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  4. Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets
 
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2022
Journal Article
Title

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

Abstract
Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from coexpression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https:// zenodo. org/ record/ 58317 86 and https:// github. com/ ContN eXt/, respectively and developed ContNeXt (https:// contn ext. scai. fraun hofer. de/), a web application to explore the networks generated in this work.
Author(s)
Queiroz Figueiredo, Rebeca
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Diaz del Ser, Sara
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Raschka, Tamara  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Tom Kodamullil, Alpha
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Mubeen, Sarah  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Domingo Fernández, Daniel  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
BMC bioinformatics. Online journal  
Project(s)
Modellierung von Komorbiditäts-Prozessen durch integratives, maschinelles Transfer-Lernen für psychiatrische Erkrankungen. TP3 System-Medizinisches Wissen und Mechanismen  
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Open Access
File(s)
Download (3.03 MB)
Rights
CC BY
DOI
10.1186/s12859-022-04765-0
10.24406/publica-r-418698
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • Transcriptomic

  • Biological context

  • Co-expression networks

  • Gene expression

  • Network biology

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