QuanTP: A Software Resource for Quantitative Proteo-Transcriptomic Comparative Data Analysis and Informatics

Praveen Kumar, Priyabrata Panigrahi, James E Johnson, Wanda J Weber, Subina Mehta, Ray Sajulga, Caleb Easterly, Brian A Crooker, Mohammad Heydarian, Krishanpal Anamika, Timothy J Griffin, Pratik D Jagtap

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)782-790
Number of pages9
JournalJournal of Proteome Research
Volume18
Issue number2
DOIs
StatePublished - Feb 1 2019

Keywords

  • concordance
  • integrative analysis
  • mass spectrometry
  • multiomics
  • proteo-transcriptomics
  • proteomics
  • quantitation
  • systems biology
  • transcriptomics
  • visualization

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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