Biology subjects

Lähnemann, D.

Publications and source records attributed to Lähnemann, D..

3 recordsLinked to original sources

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↗