Publications

Riboseq-flow: A streamlined, reliable pipeline for ribosome profiling data analysis and quality control Extended data for Use case II example from the manuscript introducing riboseq-flow, a Nextflow …w DSL2 ribo-seq analysis pipeline, including all the outputs produced by analysing ribo-seq data in human and mouse brain from Wang et al., 2020 with riboseq-flow.  Raw FASTQ files are available from ArrayExpress under accession E-MTAB-7247. Below are the accession codes and assay names for the ribo-seq samples used: ERR2812346: human_brain_ribo_1 ERR2812347: human_brain_ribo_2 ERR2812348: human_brain_ribo_3 ERR2812382: mouse_brain_ribo_1 ERR2812383: mouse_brain_ribo_2 ERR2812384: mouse_brain_ribo_3 Raw FASTQ files were downloaded with nf-core/fetchngs v.1.10.1 using download_fastq.sh. After download, they were analysed with riboseq-flow v1.1.1 (10.5281/zenodo.10558537) using run_riboseq.sh scripts in each folder for human and mouse, respectively. human_brain.zip: code to run riboseq-flow and the results produced for human brain data human_brain_Ribo-seq_multiqc_report.html: MultiQC report summarising read and mapping statistics, and ribo-seq quality control metrics for human brain data mouse_brain.zip: code to run riboseq-flow and the results produced for mouse brain data mouse_brain_Ribo-seq_multiqc_report.html: MultiQC report summarising read and mapping statistics, and ribo-seq quality control metrics for mouse brain data  

Abstract Background The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of …en insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. Findings We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the “form generation module” creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the “validation module.” Finally, the “resource module” can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive–compatible XML metadata file. Conclusions Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl.