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Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

: Figueiredo, Rebeca Quiroz; Raschka, Tamara; Kodamullil, Alpha Tom; Hofmann-Apitius, Martin; Mubeen, Sarah; Domingo-Fernández, Daniel

Fulltext urn:nbn:de:0011-n-6364297 (5.2 MByte PDF)
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Created on: 6.7.2021

Nucleic Acids Research 49 (2021), No.14, pp.7939-7953
ISSN: 0305-1048
ISSN: 0301-5610
ISSN: 1362-4962
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Journal Article, Electronic Publication
Fraunhofer SCAI ()
pathway; transcriptomics; co-expression networks; Knowledge Graphs; disease mechanisms

We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node- level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.