Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy.
Genetics and Genomics Science Department, Ichan School of Medicine at Mount Sinai, New York City, NY 10029-5674, USA.
Bioinformatics. 2020 Jun 1;36(12):3916-3917. doi: 10.1093/bioinformatics/btaa223.
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.
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.
Cross-platform and multi-threaded R package on CRAN (stable version) https://cran.r-project.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto.
Supplementary data are available at Bioinformatics online.
基因网络推断和主调控因子分析（MRA）已被广泛应用于从基因表达特征中定义特定的转录扰动。有几种工具可用于执行此类分析，但大多数工具都需要计算机集群或大量的 RAM 才能运行。
我们开发了 corto，这是一个快速而轻量级的 R 包，可从基因表达数据中推断基因网络并执行 MRA，可选地进行拷贝数变异校正，并能够在从 RNA-Seq 或 ATAC-Seq 数据生成的特征上运行。我们在 39 个人类肿瘤和 27 个正常组织数据集上对其进行了广泛的基准测试，以推断特定于上下文的基因网络。
跨平台和多线程 R 包在 CRAN（稳定版本）https://cran.r-project.org/package=corto 和 Github（开发版本）https://github.com/federicogiorgi/corto 上可用。
补充数据可在 Bioinformatics 在线获得。
