Challenges in proteogenomics: a comparison of analysis methods with the case study of the DREAM proteogenomics sub-challenge.

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ID: 94302
2019
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Abstract
Proteomic measurements, which closely reflect phenotypes, provide insights into gene expression regulations and mechanisms underlying altered phenotypes. Further, integration of data on proteome and transcriptome levels can validate gene signatures associated with a phenotype. However, proteomic data is not as abundant as genomic data, and it is thus beneficial to use genomic features to predict protein abundances when matching proteomic samples or measurements within samples are lacking.We evaluate and compare four data-driven models for prediction of proteomic data from mRNA measured in breast and ovarian cancers using the 2017 DREAM Proteogenomics Challenge data. Our results show that Bayesian network, random forests, LASSO, and fuzzy logic approaches can predict protein abundance levels with median ground truth-predicted correlation values between 0.2 and 0.5. However, the most accurately predicted proteins differ considerably between approaches.In addition to benchmarking aforementioned machine learning approaches for predicting protein levels from transcript levels, we discuss challenges and potential solutions in state-of-the-art proteogenomic analyses.
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eicher2019challengesbmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Eicher, Tara;Patt, Andrew;Kautto, Esko;Machiraju, Raghu;Mathé, Ewy;Zhang, Yan;
Journal BMC Bioinformatics
Year 2019
DOI
10.1186/s12859-019-3253-z
URL
Keywords
Fuzzy logic proteogenomics random forests bayesian networks mrna

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