Biology subjects

Doenitz, J.

Publications and source records attributed to Doenitz, J..

4 recordsLinked to original sources

bioRxiv2026

AstraKit: Customizable, reproducible workflows for biomedical research and precision medicine

MotivationThe success of precision medicine and biomedical research depends on the availability of efficient software solutions for processing and interpreting genetic variants, interpreting multi-omics data, and integrating drug screen analyses. However, fragmented bioinformatics tools compel researchers and clinicians to resort to error-prone manual pipelines. ResultsWe present AstraKit, a unified KNIME workflow suite enabling end-to-end precision medicine analytics. AstraKit introduces three transformative innovations: 1) Dynamic variant interpretation with customizable annotation and filtering for disease-specific genomic contexts; 2) Multi-layered omics analyses integrating genomic, transcriptomic, and epigenetic data; and 3) Translational drug matching that correlates in vitro drug screens with clinical outcomes. Validated across oncology cohorts, AstraKit demonstrates concordance between experimental drug sensitivity and clinical outcomes, resolving discordances to uncover resistance mechanisms. By unifying variant analysis, multi-omics, and drug response modeling on a single customizable platform, AstraKit eliminates siloed workflows, accelerating biomarker validation and enabling clinicians to directly link molecular profiles to therapeutic decisions. As all AstraKit workflows are open-source and platform-independent, we provide a versatile comprehensive software suite for a multitude of tasks in bioinformatics and precision medicine. Availability and implementationThe KNIME workflows are available at KNIME Hub https://hub.knime.com/bioinf_goe/spaces/Public/AstraKit~lfVsGBY2HnPYc1h1/. The source code is available at https://gitlab.gwdg.de/MedBioinf/mtb/astrakit.

bioinformatics↗

bioRxiv2026

PanCNV-Explorer: Deciphering copy number alterations across human cancers

Copy number variants (CNVs) are major drivers of cancer progression and genetic disorders, yet their interpretation, spanning biological mechanisms, clinical relevance, and therapeutic implications, remains fragmented across disparate resources. To bridge this gap, we present PanCNV-Explorer, a unified database integrating harmonized copy number variation data across 33 cancer types, cancer cell lines, and healthy cohorts. PanCNV-Explorer provides a genome-wide atlas of CNV frequency and functional impact, quantifying tissue-specific amplifications and deletions in both cancer and non-cancer contexts through rigorous cross-dataset normalization. The interactive web interface enables researchers to dynamically query CNVs by genomic coordinates or gene symbol, visualize cancer-type-specific frequencies with real-time comparative analysis, and explore integrated genomic features including transcripts, regulatory elements, and gene expression through a zoomable genome browser. Beyond exploration, the platform offers programmatic APIs for pan-cancer CNV analysis and visualization. A public web instance of PanCNV-Explorer is available at https://mtb.bioinf.med.uni-goettingen.de/pancnv-explorer/.

bioinformatics↗

bioRxiv2026

FusionPath: Gene fusion pathogenicity prediction using protein structural data and contextual protein embeddings

Accurate prediction of gene fusion pathogenicity is critical for understanding oncogenic mechanisms and advancing precision oncology. While existing computational methods provide valuable insights, their performance remains limited by incomplete integration of multi-scale biological features and insufficient model interpretability for clinical translation. We present FusionPath, a novel deep learning framework for gene fusion pathogenicity prediction. FusionPath uniquely integrates embeddings from multiple pretrained protein language models, including FusON-pLM and ProtBERT, alongside retained protein domains and Gene Ontology (GO) functional annotations. The model was trained and validated on a large-scale dataset of annotated pathogenic and benign fusions. The model was trained and validated on a rigorously curated dataset of 100,433 gene fusions (78,115 benign, 22,318 pathogenic) derived from FusionPDB, ChimerDB4.0, and 27 RNA-seq datasets of normal tissues. FusionPath significantly outperformed state-of-the-art methods, achieving higher AUC scores of 0.95 and 0.87 on independent test sets. By synergistically leveraging sequence, structural, and functional information with explicit modeling of wild-type sequence context, FusionPath establishes a new standard for gene fusion pathogenicity prediction by effectively leveraging complementary sequence, structural, and functional information.

bioinformatics↗

bioRxiv2021

Identifying essential genes across eukaryotes by machine learning

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60,381 genes, using 41,635 features derived from sequence, gene functions and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed a high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and validated predictions experimentally yielding similar performance. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

bioinformatics↗