Next-generation sequencing technologies, coupled to advances in mass-spectrometry-based proteomics, have facilitated system-wide quantitative profiling of expressed mRNA transcripts and proteins. Proteo-transcriptomic analysis compares the relative abundance levels of transcripts and their corresponding proteins, illuminating discordant gene product responses to perturbations. These results reveal potential post-transcriptional regulation, providing researchers with important new insights into underlying biological and pathological disease mechanisms. To carry out proteo-transcriptomic analysis, researchers require software that statistically determines transcript-protein abundance correlation levels and provides results visualization and interpretation functionality, ideally within a flexible, user-friendly platform. As a solution, we have developed the QuanTP software within the Galaxy platform. The software offers a suite of tools and functionalities critical for proteo-transcriptomics, including statistical algorithms for assessing the correlation between single transcript-protein pairs as well as across two cohorts, outlier identification and clustering, along with a diverse set of results visualizations. It is compatible with analyses of results from single experiment data or from a two-cohort comparison of aggregated replicate experiments. The tool is available in the Galaxy Tool Shed through a cloud-based instance and a Docker container. In all, QuanTP provides an accessible and effective software resource, which should enable new multiomic discoveries from quantitative proteo-transcriptomic data sets.
Bibliographical noteFunding Information:
We thank Professor Cavan Reilly (Division of Biostatistics; University of Minnesota) for his inputs in statistical data analysis for this study and Bjoern Gruening (University of Freiburg) for his guidance on HTML output support on Galaxy framework. The Galaxy-P project is funded by NCI-ITCR grant 1U24CA199347 (PI: T.J.G.). The Jetstream instance is supported via an XSEDE research allocation TG-BIO170096 (PI: P.D.J.) and is maintained by researchers at the Indiana University.
Copyright © 2018 American Chemical Society.
- integrative analysis
- mass spectrometry
- systems biology