Abstract

BackgroundVisual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose.ResultsThe OmicLoupe software was developed to facilitate visual data exploration and provides more than 15 interactive cross-dataset visualizations for omics data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three different studies, where it allowed for detection of both technical data limitations and biological trends across different omic layers. An example is an analysis of SARS-CoV-2 infection based on two previously published studies, where OmicLoupe facilitated the identification of gene products with consistent expression changes across datasets at both the transcript and protein levels.ConclusionsOmicLoupe provides fast exploration of omics data with tailored visualizations for comparisons within and across data layers. The interactive visualizations are highly informative and are expected to be useful in various analyses of both newly generated and previously published data. OmicLoupe is available at quantitativeproteomics.org/omicloupe

Highlights

  • Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends

  • Software implementation To improve the accessibility and capability of analysis of complex datasets, we developed OmicLoupe

  • OmicLoupe is built as a collection of modules, each performing a certain part of the analysis (Fig. 1)

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Summary

Introduction

Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose. Omic data are challenging to work with, in part as they often contain considerable variation within and between experiments driven by both biological and technical factors, such as differing experimental conditions or sampling procedures. This variation needs to be considered to correctly interpreting the data.

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