  The analysis of next-generation sequencing (NGS) data requires complex
computational workflows consisting of dozens of autonomously developed yet
interdependent processing steps. Whenever large amounts of data need to be
processed, these workflows must be executed on a parallel and/or distributed
systems to ensure reasonable runtime. Porting a workflow developed for a
particular system on a particular hardware infrastructure to another system or
to another infrastructure is non-trivial, which poses a major impediment to the
scientific necessities of workflow reproducibility and workflow reusability. In
this work, we describe our efforts to port a state-of-the-art workflow for the
detection of specific variants in whole-exome sequencing of mice. The workflow
originally was developed in the scientific workflow system snakemake for
execution on a high-performance cluster controlled by Sun Grid Engine. In the
project, we ported it to the scientific workflow system SaasFee that can
execute workflows on (multi-core) stand-alone servers or on clusters of
arbitrary sizes using the Hadoop. The purpose of this port was that also owners
of low-cost hardware infrastructures, for which Hadoop was made for, become
able to use the workflow. Although both the source and the target system are
called scientific workflow systems, they differ in numerous aspects, ranging
from the workflow languages to the scheduling mechanisms and the file access
interfaces. These differences resulted in various problems, some expected and
more unexpected, that had to be resolved before the workflow could be run with
equal semantics. As a side-effect, we also report cost/runtime ratios for a
state-of-the-art NGS workflow on very different hardware platforms: A
comparably cheap stand-alone server (80 threads), a mid-cost, mid-sized cluster
(552 threads), and a high-end HPC system (3784 threads).
