title: petal: Co-expression network modelling in R.
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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: University of Nevada, Reno, 1664 N. Virginia Street, Reno, 89557, USA. julipetereit@gmail.com.
authors.forename: Juli
authors.initials: J
authors.lastname: Petereit
authors.affiliation: University of Nevada, Reno, 1664 N. Virginia Street, Reno, 89557, USA.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Smith
authors.affiliation: University of Nevada, Reno, 1664 N. Virginia Street, Reno, 89557, USA.
authors.forename: Frederick C
authors.initials: FC
authors.lastname: Harris
authors.affiliation: University of Nevada, Reno, 1664 N. Virginia Street, Reno, 89557, USA.
authors.forename: Karen A
authors.initials: KA
authors.lastname: Schlauch
mesh_terms: D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D012984:Software
keywords: Parameter-free algorithm; R; Scale-free; Small-world; Whole omics-approach