Protein structure-based gene expression signatures

Rayees Rahman, Nicole Zatorski, Jens Hansen, Yuguang Xiong, J. G.Coen Van Hasselt, Eric A. Sobie, Marc R. Birtwistle, Evren U. Azeloglu, Ravi Iyengar, Avner Schlessinger

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Original languageEnglish
Article numbere2014866118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number19
DOIs
StatePublished - 11 May 2021

Keywords

  • Gene expression signatures
  • Reproducibility
  • Structural bioinformatics

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