MOTIVATION: The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. RESULTS: Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. AVAILABILITY AND IMPLEMENTATION: The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

MOTIVATION: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. RESULTS: Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor's main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc's rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. AVAILABILITY AND IMPLEMENTATION: ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Immunoinformatics has become a crucial part in biomedical research. Yet many immunoinformatics tools have command line interfaces only and can be difficult to install. Web-based immunoinformatics tools, on the other hand, are difficult to integrate with other tools, which is typically required for the complex analysis and prediction pipelines required for advanced applications. RESULT: We present ImmunoNodes, an immunoinformatics toolbox that is fully integrated into the visual workflow environment KNIME. By dragging and dropping tools and connecting them to indicate the data flow through the pipeline, it is possible to construct very complex workflows without the need for coding. CONCLUSION: ImmunoNodes allows users to build complex workflows with an easy to use and intuitive interface with a few clicks on any desktop computer.

UNLABELLED: Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications. AVAILABILITY AND IMPLEMENTATION: FRED 2 is available at http://fred-2.github.io CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. RESULT: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications.