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BMC Bioinformatics. 2019 Dec 20;20(Suppl 24):669. doi: 10.1186/s12859-019-3253-z.

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

Author information

1
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA.
2
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
3
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43210, USA. machiraju.1@osu.edu.
4
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. machiraju.1@osu.edu.
5
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. ewy.mathe@osumc.edu.
6
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. yan.zhang@osumc.edu.
7
The Ohio State University Comprehensive Cancer Center (OSUCCC - James), Columbus, OH, 43210, USA. yan.zhang@osumc.edu.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

Bayesian networks; Fuzzy logic; Proteogenomics; Random forests; mRNA

PMID:
31861998
PMCID:
PMC6923881
DOI:
10.1186/s12859-019-3253-z
[Indexed for MEDLINE]
Free PMC Article

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