Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

Abstract

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables -and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Comparing geographical genetic differentiation between candidate and noncandidate loci for adaptation strengthens support for parallel ecological divergence in the marine snail Littorina saxatilis

“…2008), such methodology has a considerably high rate of false positives in outlier loci under multiple testing. The familywise error rate has been traditionally controlled by the Bonferroni correction (Rice 1989), although this method has been avoided when the number of comparisons is large, as in such circumstances its statistical power is low (Perco et al . 2006).…”

Characterization of protein-interaction networks in tumors

“…We have recently outlined a computational analysis workflow aimed at characterizing cellular events at a functional level, which includes the use of differential gene expression and proteomics data, analysis of transcriptional control, and coregulation via joint transcription factor modules, further complemented by protein interaction and functional pathway data [5]. A major goal of such analysis workflows is to decipher biological functioning at the level of protein interactions [6,7]; that is, to elucidate concerted processes by integrating diverse data sources that by themselves do not provide a functional context.…”

Biomarker discovery using high-dimensional lipid analysis

“…Data coming directly from a high-content analytical platform are often not ready for analysis or interpretation and require a sequence of preprocessing steps [23 ]. The amount of preprocessing required is dependent on the assay.…”