Some organism-specific databases about regulation in bacteria have become larger, accelerated by high-throughput methodologies, while others are no longer updated or accessible. Each database homogenize its datasets, giving rise to heterogeneity across databases. Such heterogeneity mainly encompasses different names for a gene and different network representations, generating duplicated interactions that could bias network analyses. Abasy (Across-bacteria systems) Atlas consolidates information from different sources into meta-curated regulatory networks in bacteria. The high-quality networks in Abasy Atlas enable cross-organisms analyses, such as benchmarking studies where gold standards are required. Nevertheless, network incompleteness still casts doubts on the conclusions of network analyses, and available sampling methods cannot reflect the curation process. To tackle this problem, the updated version of Abasy Atlas presented in this work provides historical snapshots of regulatory networks. Thus, network analyses can be performed at different completeness levels, making possible to identify potential bias and to predict future results. We leverage the recently found constraint in the complexity of regulatory networks to develop a novel model to quantify the total number of regulatory interactions as a function of the genome size. This completeness estimation is a valuable insight that may aid in the daunting task of network curation, prediction, and validation. The new version of Abasy Atlas provides 76 networks (204,282 regulatory interactions) covering 42 bacteria (64% Gram-positive and 36% Gram-negative) distributed in 9 species (Mycobacterium tuberculosis, Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pyogenes, Streptococcus pneumoniae, and Streptomyces coelicolor), containing 8459 regulons and 4335 modules.

Database URL: https://abasy.ccg.unam.mx/.

通过高通量方法，某些有关细菌调控的特定于生物的数据库已变得更大，而另一些则不再更新或无法访问。每个数据库均化其数据集，从而导致跨数据库的异构性。这种异质性主要包含一个基因的不同名称和不同的网络表示形式，从而产生重复的交互作用，可能会使网络分析产生偏差。Abasy（甲横BA cteria SY词干）Atlas将来自不同来源的信息整合到细菌中的meta-curated调控网络中。Abasy Atlas中的高质量网络使跨生物分析成为可能，例如需要黄金标准的基准研究。然而，网络不完备性仍然使人们对网络分析的结论产生怀疑，并且可用的采样方法无法反映策展过程。为了解决这个问题，本工作中介绍的Abasy Atlas的更新版本提供了监管网络的历史快照。因此，可以在不同的完整性级别执行网络分析，从而有可能识别潜在的偏差并预测未来的结果。我们利用最近发现的调控网络复杂性的制约因素，开发出一种新颖的模型来量化作为基因组大小的函数的调控相互作用的总数。这种完整性评估是宝贵的见解，可帮助完成网络管理，预测和验证的艰巨任务。新版本的Abasy Atlas提供了76个网络（204,282个调控相互作用），涵盖了分布于9个物种（42个革兰氏阳性和36％革兰氏阴性）的42种细菌（结核分枝杆菌，枯草芽孢杆菌，大肠杆菌，谷氨酸棒状杆菌，金黄色葡萄球菌，铜绿假单胞菌，化脓性链球菌，肺炎链球菌和coelicolor链霉菌），包含8459个调节子和4335个模块。

数据库URL： https ： //abasy.ccg.unam.mx/