Biology subjects

Twardziok, S. O.

Publications and source records attributed to Twardziok, S. O..

3 recordsLinked to original sources

bioRxiv2026

The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics

Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.

bioinformatics↗

bioRxiv2018

Chromosome-scale comparative sequence analysis unravels molecular mechanisms of genome evolution between two wheat cultivars

BackgroundRecent improvements in DNA sequencing and genome scaffolding have paved the way to generate high-quality de novo assemblies of pseudomolecules representing complete chromosomes of wheat and its wild relatives. These assemblies form the basis to compare the evolutionary dynamics of wheat genomes on a megabase-scale.\n\nResultsHere, we provide a comparative sequence analysis of the ~700-megabase chromosome 2D between two bread wheat genotypes - the old landrace Chinese Spring and the elite Swiss spring wheat line CH Campala Lr22a. There was a high degree of sequence conservation between the two chromosomes. Analysis of large structural variations revealed four large insertions/deletions (InDels) of >100 kb. Based on the molecular signatures at the breakpoints, unequal crossing over and double-strand break repair were identified as the evolutionary mechanisms that caused these InDels. Three of the large InDels affected copy number of NLRs, a gene family involved in plant immunity. Analysis of single nucleotide polymorphism (SNP) density revealed three haploblocks of ~8 Mb, ~9 Mb and ~48 Mb with a 35-fold increased SNP density compared to the rest of the chromosome.\n\nConclusionsThis comparative analysis of two high-quality chromosome assemblies enabled a comprehensive assessment of large structural variations. The insight obtained from this analysis will form the basis of future wheat pan-genome studies.

genomics↗

bioRxiv2017

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

MotivationGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.\n\nResultsHere, we present an extension of the gene prediction tool GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.\n\nAvailabilityGeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.\n\nContactjens.keilwagen@julius-kuehn.de

bioinformatics↗