Abstract
Simple SummaryWith the influx of multi-omics profiling, effective integration of these data remains the bottleneck for omics-driven discovery. Thus, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis of cancer datasets. Our tool enables the exploration of multi-omics data by providing a simple user interface that minimizes the need for computational experience. Furthermore, the interface can be deployed locally or on a webserver to facilitate scientific collaboration and discovery.High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.
Highlights
Multi-omics profiling of cancer patient samples and cell lines is becoming a staple of cancer research [1]
We previously identified that USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) reduces the activity of
RNA-seq sample grouping was assessed by unsupervised hierarchical clustering (Figure 2A)
Summary
Multi-omics profiling of cancer patient samples and cell lines is becoming a staple of cancer research [1]. These applications tend to have limited features for analyzing complex heterogeneous phenotypes in cell lines and patients, such as mutation of genomic drivers, cell line characteristics, sex, or metastasis status None of these tools provides a streamlined pipeline to assess similarities and differences between omics datasets, such as transcriptome and proteome comparisons, or comparisons between mouse and human cancer models. To address these challenges, we have developed DRPPM-EASY, a Shiny app built with an open-source R programming language that can be run as a local instance or deployed online. The source code of our application can be downloaded from https://github.com/shawlab-moffitt/DRPPM-EASYExprAnalysisShinY (accessed on 1 February 2022)
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