The increasing complexity of data and analysis methods has created an environment where
scientists, who may not have formal training, are finding themselves playing the impromptu …

Background The complex nature of biological data has driven the development of
specialized software tools. Scientific workflow management systems simplify the assembly of …

Analyzing biological data (eg, annotating genomes, assembling NGS data...) may involve
very complex and interlinked steps where several tools are combined together. Scientific …

Biobanks store and catalog human biological material that is increasingly being digitized
using next-generation sequencing (NGS). There is, however, a computational bottleneck, as …

Across many fields of science, primary data sets like sensor read-outs, time series, and
genomic sequences are analyzed by complex chains of specialized tools and scripts …

Plant phenotyping consists in the observation of physical and biochemical traits of plant
genotypes in response to environmental conditions. Challenges, in particular in context of …

Motivation A challenge for computational biologists is to make our analyses reproducible—
ie to rerun, combine, and share, with the assurance that equivalent runs will generate …

Background Gene regulation is one of the most important cellular processes, indispensable
for the adaptability of organisms and closely interlinked with several classes of pathogenesis …

Cuneiform is a minimal functional programming language for large-scale scientific data
analysis. Implementing a strict black-box view on external operators and data, it allows the …

The analysis of next-generation sequencing (NGS) data requires complex computational
workflows consisting of dozens of autonomously developed yet interdependent processing …