One of the challenges that biology-related sciences are facing is the exponential increase of data. Nowadays, thanks to all the sequencing techniques which are available, we are generating more data than the amount we can study. We all love all the genomic, epigenomic, transcriptomic, proteomic, … , glycomic, lipidomic, and metagenomic studies because of the rich they are. However, most of the times, the analysis of the results uses only a fraction of all the generated data. For example, it is quite frequent to study the transcriptome of an organism in different environments and then just focus on identifying which 2 or 3 genes are upregulated. This type of analyses do not exploit the data to its maximum extent and here is where network analysis makes its appearance!
The output of most experiments with a set up as the mentioned above can be represented as networks. For example, gene coexpression networks are networks in which nodes correspond to genes and edges represent correlations in their expression across multiple samples. Approaches like this can help us to better study, visualise and understand omics data.
Unfortunately, the continuing increase of biological information has an additional problem: its accuracy and relevance (since most of the times in biology more does not mean better). For this reason, it is important to be able to construct robust networks (i.e. gene coexpression networks) without exogenous information known a priori. This networks should depend on a threshold that keeps edges only between pairs of nodes (genes, proteins, metabolites…) with strong correlation in their expression/occurrence to get a network that keeps invariant to small changes in the original data.
By applying this simple idea: representing the result of omics experiments as stable networks, we will be able to have a more rich and complete vision of the data. Now we only need to have a standard methodology for generating these networks so that results from different groups are comparable… But I guess this will have to wait, at least until the next blog post!
