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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Evaluating feature-selection stability in next-generation proteomics.

Wilson Wen Bin Goh^1,2, Limsoon Wong^1,2

¹1 School of Pharmaceutical Science and Technology, Tianjin University, 92 Weijin Road, Tianjin 300072, China.

Journal of Bioinformatics and Computational Biology

|September 20, 2016

Summary

This summary is machine-generated.

Reproducible feature selection in proteomics is challenging. Novel ranked-based network algorithms (RBNAs) demonstrate high stability and reproducibility, outperforming traditional methods and improving feature selection quality in proteomics data analysis.

Keywords:

Proteomics biostatistics networks translational research

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Area of Science:

Proteomics
Bioinformatics
Systems Biology

Background:

Reproducible and relevant feature identification is a significant challenge in biological research, particularly in genomics.
This challenge extends to proteomics, where common feature selection methods often yield unreliable results.
High test variability in proteomics can hinder the reproducibility of selected features, even for abundant proteins.

Purpose of the Study:

To evaluate the performance of feature selection methods in proteomics.
To investigate the utility of ranked-based network algorithms (RBNAs) for proteomics data.
To highlight the importance of stability and reproducibility in feature selection.

Main Methods:

Assessment of common feature selection methods (e.g., t-test, recursive feature elimination) using reliability benchmarks.
Evaluation of hypergeometric enrichment for protein subnet analysis.
Application and validation of ranked-based network algorithms (RBNAs) on proteomics data.

Main Results:

Traditional feature selection methods in proteomics show poor reproducibility.
Hypergeometric enrichment performs poorly due to unstable pre-selection steps.
Ranked-based network algorithms (RBNAs) demonstrate high stability and reproducibility in proteomics.
RBNAs effectively select relevant features from proteomics data.

Conclusions:

Statistical feature testing in proteomics requires caution; network-based approaches are not universally robust.
Ranked-based network algorithms (RBNAs) offer a stable and reproducible solution for feature selection in proteomics.
Augmenting feature selection with stability and reproducibility analyses is recommended for improved feature quality before experimental validation.