PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets.

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ID: 77784
2019
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Abstract
Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.
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netanely2019promobmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Netanely, Dvir;Stern, Neta;Laufer, Itay;Shamir, Ron;
Journal BMC Bioinformatics
Year 2019
DOI
10.1186/s12859-019-3142-5
URL
Keywords
personalized medicine cancer biomarkers cancer genomics gene expression analysis multi-omics cancer subtypes multi-omic clustering

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