Here we propose a new algorithm for computing Atomic Regulons (ARs), which combines many of the advantages of the existing data-driven approaches, but integrates new evidence types including gene context and functional relationships to more quickly converge on a complete set of biologically meaningful ARs. Our algorithm is unique from other approaches in that it begins by constructing draft ARs using a combination of operon predictions and SEED subsystem technology.
Code to compute ARs can be found here: https://github.com/jplfaria/atomic_regulons/blob/master/lib/Bio/KBase/atomic_regulons/atomic_regulonsImpl.pm
- Download RASTtk 1.3.0 available at: https://github.com/TheSEED/RASTtk-Distribution/releases/
Note: The RASTtk/KBase environment is necessary for access to the SEED Subsystems and Functional roles
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Launch RASTtk distribution to prompt the RASTtk interactive shell
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Clone the atomic_regulons repository:
git clone https://github.com/jplfaria/atomic_regulons.git
- In the RASTtk interactive shell run the following cmd in atomic_regulons/test-service :
e.g., for Escherichia coli data, run:
perl -I ../lib testARserviceImpl.pl "kb|g.0" e.coli_expression.tab
Parameters:
- Genome ID: "kb|g.0"
- Expression Data: e.coli_expression.tab (provided in /test-service)
Note: Search for genome ID for genome of interest here: https://narrative.kbase.us/functional-site/#/search/?q=ecoli%20k12&category=genomes
- Output Atomic Regulons are available at:
/test-service/atomic_regulons.out