Biology subjects

Köster, J.

Publications and source records attributed to Köster, J..

7 recordsLinked to original sources

bioRxiv2025

Short-term but not long-term triiodothyronine treatment improved cardiac function after myocardial infarction in male wild-type mice

ObjectivesThyroid hormone (TH), especially triiodothyronine (T3), plays an important role in cardiac physiology and in the remodeling process following myocardial infarction (MI). We investigated the effects of short-term (until day 5) and long-term (until day 56) post-MI T3 treatment on cardiac function, infarct size, hypertrophy, and gene expression in mice without and with deletion of TR, the main cardiac thyroid hormone receptor (wild-type (WT) and TRKO, respectively) MethodsWT and TRKO mice underwent permanent left anterior descending coronary artery (LAD) ligation or sham surgery followed by either short-term T3 for 5 days post-MI or long-term T3 until day 56, including a subgroup in which T3 treatment commenced 14 days post-MI; all groups were followed up for 4 weeks. T3 was delivered via drinking water at 500 ng/ml. Cardiac function was studied with echocardiography (ejection fraction, EF), infarct size by histology (Sirius red), heart weight normalized to tibia length, and transcriptomic profiling (RNA-seq) in WT hearts. ResultsShort-term T3 improved EF in WT but not in TRKO mice without induction of hypertrophy or changes in infarct size in either genotype. Long-term T3 induced cardiac hypertrophy in both WT and TRKO mice. However, long-term T3 did not improve EF or reduce infarct size. In TRKO mice, baseline EF post-MI was preserved without T3, but T3 treatment decreased EF. RNA-seq in long-term treated WT mice suggested modulation of Rho-GTPase signaling, mitochondrial biogenesis, and immune activation by T3. ConclusionsT3 therapy post-MI improved cardiac function only when applied acutely and for a short term. Long-term exposure led to cardiac hypertrophy without functional improvement and may even worsen cardiac function in TR-deficient settings. Timing, duration, and receptor status are highly relevant for TH-based interventions in MI.

physiology↗

bioRxiv2025

Datavzrd: Rapid programming- and maintenance-free interactive visualization and communication of tabular data

Tabular data, often scattered across multiple tables, is the primary output of data analyses in virtually all scientific fields. Exchange and communication of tabular data is therefore a central challenge. We present Datavzrd, a tool for creating portable, visually rich, interactive reports from tabular data in any kind of scientific discipline. Datavzrd unifies the strengths of currently common generic approaches for interactive visualization like R Shiny with the portability, ease of use and sustainability of plain spreadsheets. The generated reports do not require the maintenance of a web server nor the installation of specialized software for viewing and can simply be attached to emails, shared via cloud services, or serve as manuscript supplements. They can be specified without requiring imperative programming, thereby enabling rapid development and offering accessibility for non-computational scientists, unlocking the look and feel of dedicated manually crafted web applications without the maintenance and development burden. Datavzrd reports scale from small tables to thousands or millions of rows and offer the ability to link multiple related tables, allowing to jump between corresponding rows or hierarchically explore growing levels of detail.

bioinformatics↗

bioRxiv2022

Insane in the vembrane: filtering and transforming VCF/BCF files

Data from sequencing of DNA or RNA samples is routinely scanned for variation. Such variation data is stored in the standardized VCF/BCF format with additional annotations. Analyses of variants usually involve steps where filters are applied to narrow down the list of candidates for further analysis. A number of tools for this task exist, differing in functionality, speed, syntax and supported annotations. Thus, users have to switch between tools depending on the filtering task, and have to adapt to the respective filtering syntax. We present vembrane as a command line VCF/BCF filtering tool that consolidates and extends the filtering functionality of previous software to meet any imaginable filtering use case. To this end, vembrane exposes the VCF/BCF file type specification and its inofficial extensions by the annotation tools VEP and SnpEff as Python data structures. vembrane filter enables filtration by arbitrary Python expressions over (combinations of) annotations, requiring only basic knowledge of the Python programming language. vembrane table allows users to generate tables from subsets of annotations or functions thereof. Finally, it is fast, thanks to pysam, a Python wrapper around htslib, and by relying on Pythons lazy evaluation. Availability and ImplementationSource code and installation instructions are available at github.com/vembrane/vembrane, DOI: 10.5281/zen-odo.7003981.

bioinformatics↗

bioRxiv2020

ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

Obtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls. We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population--also enabling a biologically relevant imputation of missing genotypes for the single cell. Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout. ProSolos model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at: https://github.com/prosolo/prosolo

bioinformatics↗

bioRxiv2019

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

As witnessed by various population-scale cancer genome sequencing projects, accurate discovery of somatic variants has become of central importance in modern cancer research. However, count statistics on somatic insertions and deletions (indels) discovered so far point out that large amounts of discoveries must have been missed. The reason is that the combination of uncertainties relating to, for example, gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling and strand bias are hard to accurately quantify. Here, a unifying statistical model is provided whose dependency structures enable to accurately quantify all inherent uncertainties in short time. As major consequence, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy. As demonstrated on simulated and real data, this enables to dramatically increase the amount of true discoveries while safely suppressing the FDR. Specifically supported by workflow design, our approach can be integrated as a post-processing step in large-scale projects.\n\nThe software is publicly available at https://varlociraptor.github.io and can be easily installed via Bioconda1 [Gruning et al., 2018].

bioinformatics↗

bioRxiv2017

Bioconda: A sustainable and comprehensive software distribution for the life sciences

We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multiplatform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.

bioinformatics↗

bioRxiv2017

Practical computational reproducibility in the life sciences

Many areas of research suffer from poor reproducibility. This problem is particularly acute in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but practical implementation of these practices remains difficult. This is because reproducing published computational analyses requires installing many software tools plus associated libraries, connecting tools together into the complete pipeline, and specifying parameters. Here we present a suite of recently emerged technologies which make computational reproducibility not just possible, but, finally, practical in both time and effort. By combining a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and an integrated workflow system that automatically orchestrates the composition of these packages for entire pipelines, an unprecedented level of computational reproducibility can be achieved.

bioinformatics↗