Biology subjects

Holtgrewe, M.

Publications and source records attributed to Holtgrewe, M..

7 recordsLinked to original sources

bioRxiv2026

Kiosc: an integrated platform for managing bioinformatics data analysis containers

In many bioinformatic data analysis projects, it is convenient to visualize plots and results through an interactive web app or dashboard. These interactive reports can then be shared with customers, collaborators, or the general public. Publishing and sharing these apps is not straightforward, becoming especially cumbersome when the number of projects and customers start growing. Docker containers offer a convenient way to package, distribute, and run interactive web apps, and their use is already widespread in the bioinformatics community. We developed Kiosc to simplify the orchestration of containerized web apps, organize them into projects, and regulate access control. We implemented it as a web server based on the Django framework, with a user- and admin-friendly interface as well as a REST API for programmatic tasks. Users can select Docker containers packaging apps like Plotly Dash, Shiny, or Quarto, and configure them to display the results of their analysis. Kiosc runs the containers with the appropriate network configuration and acts as a proxy to the web services running inside the containers. We have been maintaining a Kiosc instance for more than 5 years, serving 321 containers in 150 projects across multiple institutions. In this article, we introduce the main functionality in Kiosc and describe four use-cases that show how Kiosc can prove helpful to the broader bioinformatics community, such as configuring and running web apps for the interactive visualization of workflow results, and publishing companion apps for scientific articles. Kiosc is a self-hosted platform for publishing web apps, which doesn't require significant expertise in either Docker or network administration to be deployed. It provides a similar service to Kubernetes, but with a convenient web interface and much lower administration overhead.

bioinformatics↗

bioRxiv2026

mehari: high-performance, strict HGVS-first variant effect prediction

Variant annotation requires the precise and consistent computation of Sequence Ontology (SO) terms and Human Genome Variation Society (HGVS) nomenclature. To ensure robust synchronization between these two key facets, we present mehari, a high-performance variant effect predictor implemented in Rust that employs a strict "HGVS-first" approach. By deterministically projecting variants to transcripts before evaluating functional consequences, mehari structurally aligns HGVS notation and SO terms. Benchmarking on ClinVar demonstrates that mehari achieves exceptional processing speeds and high concordance with established tools like Ensembl VEP, while also providing refined handling for complex biological edge cases such as selenoprotein recoding.

bioinformatics↗

bioRxiv2025

Svirlpool: structural variant detection from long read sequencing by local assembly

MotivationLong-Read Sequencing (LRS), and Oxford Nanopore Technologies (ONT) in particular, has greatly improved the detection of structural genome variants (SVs). Fast alignment-based ONT callers achieve strong benchmark performance, but they necessarily reduce the read sequence to alignment-derived signals when deciding whether variants are shared across samples. This can be limiting for cohort and clinical analyses, especially for insertions and repeat regions where sequence representation matters. We present Svirlpool, a multi-sample SV caller for ONT data that builds local consensus assemblies of candidate SV regions and retains the assembled sequence up to the final joint-calling step, where merging tolerances are scaled by a reference-independent noise estimate derived from the reads. ResultsWe validated Svirlpool on two ONT family datasets: the recent high-quality HG002 Ashkenazi trio and the older Platinum Pedigree family, using the Genome in a Bottle and T2TQ100 benchmarks on the GRCh38, GRCh37, and CHM13v2 references and the Mendelian consistency of native multi-sample calls. We compare against current native joint callers and post-hoc merging workflows. Svirlpool produces highly Mendelian-consistent insertion calls in trio analyses (95.2% on GRCh38 and 95.1% on CHM13v2 at 30x), and on CHM13v2 it reaches the highest insertion and deletion consistency among all tested approaches. Sawfish and Sniffles achieve the highest SV benchmark F1 scores on recent high-quality ONT data, whereas Svirlpool enters the competition with more conservative SV calls. Svirlpool features native, sequence-aware joint calling with retained local consensus sequences and shows a very high Mendelian consistency with sequencing data from different batches and chemistries, which is a common situation in clinical application. Availability and Implementation: Source code, container images, and documentation available at https://github.com/bihealth/svirlpool. Contactvinzenz.may@bih-charite.de

bioinformatics↗

bioRxiv2022

SODAR: enabling, modeling, and managing multi-omics integration studies

Scientists employing omics in life science studies face challenges such as the modeling of multi assay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multi assay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license.

bioinformatics↗

bioRxiv2020

VarFish - Collaborative and Comprehensive Variant Analysis for Diagnosis and Research

VarFish is a user-friendly web application for the quality control, filtering, prioritization, analysis, and user-based annotation of panel and exome variant data for rare disease genetics. It is capable of processing variant call files with single or multiple samples. The variants are automatically annotated with population frequencies, molecular impact, and presence in databases such as ClinVar. Further, it provides support for pathogenicity scores including CADD, MutationTaster, and phenotypic similarity scores. Users can filter variants based on these annotations and presumed inheritance pattern and sort the results by these scores. Filtered variants are listed with their annotations and many useful link-outs to genome browsers, other gene/variant data portals, and external tools for variant assessment. VarFish allows user to create their own annotations including support for variant assessment following ACMG-AMP guidelines. In close collaboration with medical practitioners, VarFish was designed for variant analysis and prioritization in diagnostic and research settings as described in the softwares extensive manual. The user interface has been optimized for supporting these protocols. Users can install VarFish on their own in-house servers where it provides additional lab notebook features for collaborative analysis and allows re-analysis of cases, e.g., after update of genotype or phenotype databases.

bioinformatics↗

bioRxiv2019

SCelVis: Powerful explorative single cell data analysis on the desktop and in the cloud

BackgroundSingle cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication.\n\nResultsTo facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally.\n\nMethodsSCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.

bioinformatics↗

bioRxiv2018

Identification and Ranking of Recurrent Neo-Epitopes in Cancer

Neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. We propose a method for in silico selection of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. This is achieved by carefully screening 33 TCGA data sets for recurrent somatic amino acid exchanges and, for the 1,055 resulting recurrent variants, applying MHC class I binding prediction algorithms. A preliminary confirmation of epitope binding and recognition by CD8 T cells has been carried out for a couple of candidates in humanized mice. Recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

genomics↗