Transfer of regulatory knowledge from human to mouse for functional genomics analysis

• Verfasst von: Holland, Christian H. [VerfasserIn]
• Verfasst von: Szalai, Bence [VerfasserIn]
• Verfasst von: Sáez Rodríguez, Julio [VerfasserIn]
• Titel: Transfer of regulatory knowledge from human to mouse for functional genomics analysis
• Verf.angabe: Christian H. Holland, Bence Szalai, Julio Saez-Rodriguez
• Jahr: 2020
• Umfang: 8 S.
• Fussnoten: Available online: 13 September 2019 ; Gesehen am 04.06.2020
• Titel Quelle: Enthalten in: Biochimica et biophysica acta. Gene regulatory mechanisms
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 2008
• Jahr Quelle: 2020
• Band/Heft Quelle: 1863(2020,6) Artikel-Nummer 194431, 8 Seiten
• ISSN Quelle: 1876-4320
• DOI: doi:10.1016/j.bbagrm.2019.194431
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: cancer
• Sach-SW: datasets
• Sach-SW: expression
• Sach-SW: Functional genomics
• Sach-SW: Pathway activity
• Sach-SW: Signaling footprints
• Sach-SW: Transcription factor activity
• K10plus-PPN: 1699797374

Abstract

Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Functional genomics tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space into a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for humans and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics.