  Background: With the rapid growth of massively parallel sequencing
technologies, still more laboratories are utilizing sequenced DNA fragments for
genomic analyses. Interpretation of sequencing data is, however, strongly
dependent on bioinformatics processing, which is often too demanding for
clinicians and researchers without a computational background. Another problem
represents the reproducibility of computational analyses across separated
computational centers with inconsistent versions of installed libraries and
bioinformatics tools.
  Results: We propose an easily extensible set of computational pipelines,
called SnakeLines, for processing sequencing reads; including mapping,
assembly, variant calling, viral identification, transcriptomics, metagenomics,
and methylation analysis. Individual steps of an analysis, along with methods
and their parameters can be readily modified in a single configuration file.
Provided pipelines are embedded in virtual environments that ensure isolation
of required resources from the host operating system, rapid deployment, and
reproducibility of analysis across different Unix-based platforms.
  Conclusion: SnakeLines is a powerful framework for the automation of
bioinformatics analyses, with emphasis on a simple set-up, modifications,
extensibility, and reproducibility.
  Keywords: Computational pipeline, framework, massively parallel sequencing,
reproducibility, virtual environment
