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Some organism-specific databases about regulation in bacteria have becomelarger, accelerated by high-throughput methodologies, while others are nolonger updated or accessible. Each database homogenize its datasets, givingrise to heterogeneity across databases. Such heterogeneity mainly encompassesdifferent names for a gene and different network representations, generatingduplicated interactions that could bias network analyses. Abasy(Across-bacteria systems) Atlas consolidates information from different sourcesinto meta-curated regulatory networks in bacteria. The high-quality networks inAbasy Atlas enable cross-organisms analyses, such as benchmarking studies wheregold standards are required. Nevertheless, network incompleteness still castsdoubts on the conclusions of network analyses, and available sampling methodscannot reflect the curation process. To tackle this problem, the updatedversion of Abasy Atlas presented in this work provides historical snapshots ofregulatory networks. Thus, network analyses can be performed at differentcompleteness levels, making possible to identify potential bias and to predictfuture results. We leverage the recently found constraint in the complexity ofregulatory networks to develop a novel model to quantify the total number ofregulatory interactions as a function of the genome size. This completenessestimation is a valuable insight that may aid in the daunting task of networkcuration, prediction, and validation. The new version of Abasy Atlas provides76 networks (204,282 regulatory interactions) covering 42 bacteria (64%Gram-positive and 36% Gram-negative) distributed in 9 species, containing 8,459regulons and 4,335 modules.

computational and structural biotechnology journal

Abasy Atlas v2.2: The most comprehensive and up-to-date inventory of  meta-curated, historical, bacterial regulatory networks, their completeness  and system-level characterization