A5028204414- Cancer-related molecular mechanisms research
- Single-cell and spatial transcriptomics
- RNA and protein synthesis mechanisms
- CRISPR and Genetic Engineering
- Epigenetics and DNA Methylation
- Genomics and Chromatin Dynamics
- MicroRNA in disease regulation
- Gaussian Processes and Bayesian Inference
- Bacterial Genetics and Biotechnology
- Model Reduction and Neural Networks
- Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
- RNA Research and Splicing
- Gene expression and cancer classification
- Cancer Genomics and Diagnostics
- RNA modifications and cancer
- Statistical Methods and Inference
- Generative Adversarial Networks and Image Synthesis
- Immune cells in cancer
Helmholtz Zentrum München
2021-2024
Max Planck Institute for Molecular Genetics
2016-2019
Abstract CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing depletion genome-wide essentiality screens, with surprising discovery that gene-specific features substantially impact prediction. mixed-effect random forest regression model provides better estimates of efficiency....
To initiate X-Chromosome inactivation (XCI), the long noncoding RNA Xist mediates chromosome-wide gene silencing of one X Chromosome in female mammals to equalize dosage between sexes. The efficiency is highly variable across genes, with some genes even escaping XCI somatic cells. A gene's susceptibility Xist-mediated appears be determined by a complex interplay epigenetic and genomic features; however, underlying rules remain poorly understood. We have quantified kinetics at level nascent...
Abstract Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell subcellular resolution by measuring expression a predefined set genes. The selection an optimal probed genes is crucial for capturing signals present tissue. This requires selecting most informative, yet minimal, to profile (gene selection) which it possible build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting...
DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity using intrinsic read-level information. Although diverse deconvolution methods were developed to infer composition based on bulk sequencing-based methylomes, systematic evaluation not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele...
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on combination additive regression models and networks. Our encompasses (1) modular neural network building system learning library TensorFlow for fusion various statistical approaches, (2) an orthogonalization cell allow interpretable different subnetworks, as well (3) pre-processing steps necessary set up such models. The software...
Abstract Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- subcellular resolution by measuring expression a predefined set genes. The selection an optimal probed genes is crucial for capturing interpreting signals present tissue. However, current selections often rely on marker genes, precluding them from detecting continuous or novel states. We Spapros, end-to-end probe pipeline that optimizes both specificity type...
Abstract CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is leading technique silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable interrogation, pathway manipulation, and high-throughput screens. Here we develop best-in-class prediction algorithm guide silencing efficiency by systematically investigating factors influencing depletion multiple genome-wide essentiality...
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on combination additive regression models and networks. Our encompasses (1) modular neural network building system learning library \pkg{TensorFlow} for fusion various statistical approaches, (2) an orthogonalization cell allow interpretable different subnetworks, as well (3) pre-processing steps necessary set up such models. The...
microRNAs are small, non-coding RNAs involved in post-transcriptional gene regulation. Since the dysregulation of only a few miRNAs can affect many biological pathways, thought to play key role cancer development and be used as biomarkers for diagnosis prognosis. In order understand how miRNA leads phenotype it is important determine basic regulatory mechanisms that drive expression. Although much known about miRNA-mediated regulation, little epigenetic control miRNAs. Here, we performed...
microRNAs are small, non-coding RNAs involved in post-transcriptional gene regulation. Since the dysregulation of only a few miRNAs can affect many biological pathways, thought to play key role cancer development and be used as biomarkers for diagnosis prognosis. In order understand how miRNA leads phenotype it is important determine basic regulatory mechanisms that drive expression. Although much known about miRNA-mediated regulation, little epigenetic control miRNAs. Here, we performed...
Abstract To initiate X-chromosome inactivation (XCI), the long non-coding RNA Xist mediates chromosome-wide gene silencing of one X chromosome in female mammals to equalize dosage between sexes. The efficiency silencing, however is highly variable across genes, with some genes even escaping XCI somatic cells. A susceptibility Xist-mediated appears be determined by a complex interplay epigenetic and genomic features; however, underlying rules remain poorly understood. We have quantified...
Abstract DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair resolution. It has tremendous potential for cell-type heterogeneity with intrinsic read-level information. Although diverse deconvolution methods were developed to infer composition based on bulk sequencing-based methylomes, the systematic evaluation not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection (BED),...