Topological effects of data incompleteness of gene regulatory networks

TL;DR

This paper investigates how data incompleteness and quality affect the topological analysis of bacterial gene regulatory networks, emphasizing the impact on modularity and the validity of previous findings.

Contribution

It highlights the influence of data quality on topological analyses of transcriptional regulatory networks and identifies key factors affecting the validity of past results.

Findings

Data incompleteness significantly impacts modularity analysis.

Heterogeneity in experimental data introduces systematic biases.

Improved data quality enhances the reliability of topological insights.

Abstract

The topological analysis of biological networks has been a prolific topic in network science during the last decade. A persistent problem with this approach is the inherent uncertainty and noisy nature of the data. One of the cases in which this situation is more marked is that of transcriptional regulatory networks (TRNs) in bacteria. The datasets are incomplete because regulatory pathways associated to a relevant fraction of bacterial genes remain unknown. Furthermore, direction, strengths and signs of the links are sometimes unknown or simply overlooked. Finally, the experimental approaches to infer the regulations are highly heterogeneous, in a way that induces the appearance of systematic experimental-topological correlations. And yet, the quality of the available data increases constantly. In this work we capitalize on these advances to point out the influence of data (in)completeness and quality on some classical results on topological analysis of TRNs, specially regarding modularity at different levels. In doing so, we identify the most relevant factors affecting the validity of previous findings, highlighting important caveats to future prokaryotic TRNs topological analysis.