petal: Co-expression network modelling in R
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Authors
Petereit, Juli
Smith, Sebastian
Harris, Frederick C. Jr.
Schlauch, Karen A.
Issue Date
2016
Type
Article
Language
en_US
Keywords
Parameter-free algorithm , R , Small-world , Scale-free , Whole omics-approach
Alternative Title
Abstract
Networks provide effective models to study complex biological systems, such as gene and protein interaction networks. With the advent of new sequencing technologies, many life scientists are grasping for user-friendly methods and tools to examine biological components at the whole-systems level. Gene co-expression network analysis approaches are frequently used to successfully associate genes with biological processes and demonstrate great potential to gain further insights into the functionality of genes, thus becoming a standard approach in Systems Biology. Here the objective is to construct biologically meaningful and statistically strong co-expression networks, the identification of research dependent subnetworks, and the presentation of self-contained results. Results: We introduce petal, a novel approach to generate gene co-expression network models based on experimental gene expression measures. petal focuses on statistical, mathematical, and biological characteristics of both, input data and output network models. Often over-looked issues of current co-expression analysis tools include the assumption of data normality, which is seldom the case for hight-throughput expression data obtained from RNA-seq technologies. petal does not assume data normality, making it a statistically appropriate method for RNA-seq data. Also, network models are rarely tested for their known typical architecture: scale-free and small-world. petal explicitly constructs networks based on both these characteristics, thereby generating biologically meaningful models. Furthermore, many network analysis tools require a number of user-defined input variables, these often require tuning and/or an understanding of the underlying algorithm
petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets
this way, petal's network models represent as much of the entire system as possible to provide a whole-system approach. Conclusion: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.
petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets
this way, petal's network models represent as much of the entire system as possible to provide a whole-system approach. Conclusion: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.
Description
Citation
Petereit, J., Smith, S., Harris, F. C., & Schlauch, K. A. (2016). petal: Co-expression network modelling in R. BMC Systems Biology, 10(S2). doi:10.1186/s12918-016-0298-8
Publisher
BMC Systems Biology
License
Attribution 4.0 International
Journal
Volume
Issue
PubMed ID
ISSN
1752-0509