Berkeley Lab


ComputationENIGMA’s Computation component is tasked with data management analysis and dissemination for the ENIGMA Scientific Focus Area.

The goal is to extract meaningful and statistically significant patterns from large and complex datasets by distinguishing signal from noise and integrating evidence across different data types. To this end, members of the group are developing and maintaining widely used computational tools, including the MicrobesOnline, RegTransBase, and RegPrecise databases, plus data integration and visualization tools, such as Gaggle, that support a wide external user base.

Analytical efforts in the ENIGMA Computation component are
focused on regulatory network predictions and inferences, gene functional annotation, and statistical approaches for environmental genomics. These efforts are integrated with all of the projects in ENIGMA. In addition to developing algorithms to find patterns within such noisy biological data as transcriptome structure, peptide atlas, protein-DNA interactions, and biodiversity from NextGen sequencing data, members of the ENIGMA Computation component are integrating diverse data types for the study of modular microbial architectures across scales, such as community assemblages and regulons – groups of genes regulated by the same protein. They are performing similar tasks for the study of microbial community networks, genetic regulatory networks, and other genetic and environmental influences that encode dynamical interrelationships across these modules.