Motivation: Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed.Results: We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.

Mercatelli D., Lopez-Garcia G., Giorgi F.M. (2020). Corto: A lightweight R package for gene network inference and master regulator analysis. BIOINFORMATICS, 36(12), 3916-3917 [10.1093/bioinformatics/btaa223].

Corto: A lightweight R package for gene network inference and master regulator analysis

Mercatelli D.;Giorgi F. M.
2020

Abstract

Motivation: Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed.Results: We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.
2020
Mercatelli D., Lopez-Garcia G., Giorgi F.M. (2020). Corto: A lightweight R package for gene network inference and master regulator analysis. BIOINFORMATICS, 36(12), 3916-3917 [10.1093/bioinformatics/btaa223].
Mercatelli D.; Lopez-Garcia G.; Giorgi F.M.
File in questo prodotto:
File Dimensione Formato  
OP-CBIO200223 3916..3917.pdf

accesso aperto

Descrizione: file editoriale
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 238.79 kB
Formato Adobe PDF
238.79 kB Adobe PDF Visualizza/Apri
bioinformatics_36_12_3916_s2.zip

accesso aperto

Descrizione: Supplementary data
Tipo: File Supplementare
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 32.84 MB
Formato Zip File
32.84 MB Zip File Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/907818
Citazioni
  • ???jsp.display-item.citation.pmc??? 26
  • Scopus 42
  • ???jsp.display-item.citation.isi??? 37
social impact