A5084108745- Genomics and Chromatin Dynamics
- Single-cell and spatial transcriptomics
- RNA and protein synthesis mechanisms
- Epigenetics and DNA Methylation
- Cell Image Analysis Techniques
- Genomics and Phylogenetic Studies
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Immunotherapy and Immune Responses
- Zebrafish Biomedical Research Applications
- Pluripotent Stem Cells Research
- RNA Research and Splicing
- Metabolomics and Mass Spectrometry Studies
- CRISPR and Genetic Engineering
- Biochemical and Molecular Research
- Molecular Biology Techniques and Applications
- Machine Learning in Healthcare
- Sepsis Diagnosis and Treatment
- Cancer-related molecular mechanisms research
- DNA and Nucleic Acid Chemistry
- Plant Molecular Biology Research
- Voice and Speech Disorders
- Gene expression and cancer classification
- COVID-19 diagnosis using AI
- Reproductive Biology and Fertility
Max Delbrück Center
2019-2024
Universitätsmedizin Göttingen
2023
Max Planck Institute for Molecular Genetics
2017-2019
Zebrafish, a popular organism for studying embryonic development and modeling human diseases, has so far lacked systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium created central repository store process zebrafish developmental genomic data. Our data coordination center ( https://danio-code.zfin.org ) combines total of 1,802 sets unpublished re-analyzed published data, which used improve existing...
Abstract In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding biological processes. However, most tools developed so far are designed to address a specific question on fixed dataset and/or by model architecture. Here we present Janggu, python library facilitates genomics applications, aiming ease data acquisition and evaluation. Among its key features special objects, which form unified flexible pre-processing framework that...
Abstract Consumer wearables and sensors are a rich source of data about patients’ daily disease symptom burden, particularly in the case movement disorders like Parkinson’s (PD). However, interpreting these complex into so-called digital biomarkers requires complicated analytical approaches, validating sufficient unbiased evaluation methods. Here we describe use crowdsourcing to specifically evaluate benchmark features derived from accelerometer gyroscope two different datasets predict...
Abstract Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens thousands single cells, elucidating gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality resulting data continue to pose challenges its computational analysis, specifically integration from different sources. We have developed a dedicated approach: variational auto-encoder using noise model designed ATAC-seq (assay...
Abstract In a pandemic with novel disease, disease-specific prognosis models are available only delay. To bridge the critical early phase, built for similar diseases might be applied. test accuracy of such knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can predicted by model trained on non-COVID-19 viral pneumonia patients. We gradient boosted decision tree 718 (245 deceased) to predict individual ICU mortality and applied it 1054 (369 Our...
Abstract The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of multiomics data. Meticulous integration are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce new model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts...
Abstract Motivation Transcription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized transcription factor can be described terms of position frequency matrices. When scanning for matches matrix, one needs determine cut-off, which then turn results certain number hits. In this paper we describe how compute the distribution match scores and motif hits, are prerequisites perform hit enrichment analysis. Results We put...
Abstract Zebrafish, a popular model for embryonic development and modelling human diseases, has so far lacked systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium created first central repository store process zebrafish developmental genomic data. Our Data Coordination Center ( https://danio-code.zfin.org ) combines total of 1,802 sets unpublished reanalysed published genomics data, which used...
Abstract Motivation In recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding biological processes. However, most tools developed so far are designed to address a specific question on fixed dataset and/or by model architecture. Adapting these models integrate new datasets or different hypotheses can lead considerable software engineering effort. To this aspect we built Janggu , python library that facilitates genomics applications....
Abstract The recent advances in high-throughput single-cell sequencing has significantly required computational models which can address the high complexity of multiomics data. Meticulous integration are to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce new model, Single-cell Multiomics Autoencoder Integration (scMaui) based on stacked variational encoders adversarial learning....
DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many the genes that regulate embryonic development have been described, underlying CRE dynamics controlling their expression remain largely unknown. To address this, we applied single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq) to whole 24 hours post fertilization (hpf) stage zebrafish embryos developed a new computational tool, ScregSeg,...
Abstract Transcription factors (TFs) are important contributors to gene regulation. They specifically bind short DNA stretches known as transcription factor binding sites (TFBSs), which contained in regulatory regions (e.g. promoters), and thereby influence a target gene’s expression level. Computational biology has contributed substantially understanding by developing numerous tools, including for discovering de novo motif. While those tools primarily focus on determining studying TFBSs,...
Transcription factors (TFs) play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized TF can be described terms of position frequency matrices. Searching for motif matches with given matrix is achieved employing predefined score cutoff and subsequently counting the number above this cutoff. In article, we approximate distribution based on novel dynamic programming approach, which accounts higher order background (e.g., as...
Introduction Current studies demonstrate the existence of various molecularly and genetically defined subtypes pancreatic cancer, but their clinical relevance therapeutic potential are still largely unknown. An exception tumors with gBRCA1/2 mutations, which characterized by insufficient DNA repair candidates for platinum-based therapy PARP inhibitors.
Abstract Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens thousands single cells, shedding light on gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality resulting data continue to pose challenges its computational analysis, specifically integration from different sources. We have developed a dedicated approach, variational auto-encoder using noise model designed ATAC-seq data,...