Readability
With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Each rule describes a step in an analysis defining how to obtain output files from input files. Dependencies between rules are determined automatically.
Portability
By integration with the Conda package manager and container virtualization , all software dependencies of each workflow step are automatically deployed upon execution.
Modularization
Rapidly implement analysis steps via direct script and jupyter notebook integration. Easily create and employ re-usable tool wrappers and split your data analysis into well-separated modules.
Transparency
Automatic, interactive, self-contained reports ensure full transparency from results down to used steps, parameters, code, and software.
Scalability
Workflows scale seamlessly from single to multicore, clusters or the cloud, without modification of the workflow definition and automatic avoidance of redundant computations.
workstation

compute server

cluster

grid computing

cloud computing
