Biology subjects

Albrecht, S.

Publications and source records attributed to Albrecht, S..

10 recordsLinked to original sources

bioRxiv2025

Integrating the ENCODE blocklist for machine learning quality control of ChIP-seq with seqQscorer

MotivationQuality assessment of next-generation sequencing data is a complex but important task to ensure correct conclusions from experiments in molecular biology, biomedicine, and biotechnology. We previously introduced seqQscorer, a quality assessment tool using machine learning to support this process. To improve seqQscorer in terms of accuracy and processing time, we integrated the ENCODE blocklist* to derive a new type of quality-related features, supposed to be more informative and faster in generation than those conventionally used by seqQscorer. ResultsThe novel seqQscorer extension, called seqBLQ, allows us to improve the quality assessment for ChIP-seq data derived from human tissues and cell lines. Furthermore, seqBLQ enhances the usability of the tool by simplifying the installation procedure and reducing the computational resources required for feature generation. Availability and implementationhttps://github.com/salbrec/seqQscorer

bioinformatics↗

bioRxiv2025

Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models

This study explores the genetic regulators of microRNAs (miRNAs) using an ensemble of machine learning models to predict miRNA expression levels from gene expression data. Employing ridge regression, we accurately predicted the expression of 353 human miRNAs (R2 > 0.5), revealing robust miRNA-gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA-gene networks. To refine these insights, we filtered the gene-miRNA interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems.

bioinformatics↗

bioRxiv2023

Spiking neural networks provide accurate, efficient and robust models for whisker stimulus classification and allow for inter-individual generalization

With the help of high-performance computing, we benchmarked a selection of machine learning classification algorithms on the tasks of whisker stimulus detection, stimulus classification and behavior prediction based on electrophysiological recordings of layer-resolved local field potentials from the barrel cortex of awake mice. Machine learning models capable of accurately analyzing and interpreting the neuronal activity of awake animals during a behavioral experiment are promising for neural prostheses aimed at restoring a certain functionality of the brain for patients suffering from a severe brain injury. The liquid state machine, a highly efficient spiking neural network classifier that was designed for implementation on neuromorphic hardware, achieved the same level of accuracy compared to the other classifiers included in our benchmark study. Based on application scenarios related to the barrel cortex and relevant for neuroprosthetics, we show that the liquid state machine is able to find patterns in the recordings that are not only highly predictive but, more importantly, generalizable to data from individuals not used in the model training process. The generalizability of such models makes it possible to train a model on data obtained from one or more individuals without any brain lesion and transfer this model to a prosthesis required by the patient. Author SummaryA neural prosthesis is a computationally driven device that restores the functionality of a damaged brain region for locked-in patients suffering from the aftereffects of a brain injury or severe stroke. As such devices are chronically implanted, they rely on small, low-powered microchips with limited computational resources. Based on recordings describing the neural activity of awake mice, we show that spiking neural networks, which are especially designed for microchips, are able to provide accurate classification models in application scenarios relevant in neuroprosthetics. Furthermore, models were generalizable across mice, corroborating that it will be possible to train a model on recordings from healthy individuals and transfer it to the patients prosthesis.

neuroscience↗

bioRxiv2021

Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography

We propose a pipeline for a synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular Deep Learning denoising approaches, wavelets-based methods, methods based on Mumford-Shah denoising etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel probabilistic Mumford-Shah denoising model (PMS), showing that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop the proposed algorithm for PMS allows a cheap and robust (with the Multiscale Structural Similartity index > 90%) denoising of very large 2D videos and 3D images (with over 107 voxels) that are subject to ultra-strong Gaussian and various non-Gaussian noises, also for Signal-to-Noise Ratios much below 1.0. The code is provided for open access. One-sentence summaryProbabilisitc formulation of Mumford-Shah principle (PMS) allows a cheap quality-preserving denoising of ultra-noisy 3D images and 2D videos.

bioinformatics↗

bioRxiv2021

CHOmpact: a reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Metabolic modelling has emerged as a key tool for the characterisation of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimise the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are, therefore, difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy multi-objective optimisation and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and, with further development, will serve as a platform to develop dynamic metabolic models for the control and optimisation of biopharmaceutical cell culture processes.

systems biology↗

bioRxiv2021

SKAP2 as a new regulator of oligodendroglial migration and myelin sheath formation

Oligodendroglial progenitor cells (OPC) are highly proliferative and migratory bipolar cells, which differentiate into complex myelin forming and axon ensheathing mature oligodendrocytes during myelination. Recent studies indicate that the oligodendroglial cell population is heterogeneous on transcriptional and functional level depending on the location in the CNS. Here, we compared intrinsic properties of OPC from spinal cord and brain on functional and transcriptional level. Spinal cord OPC demonstrated increased migration as well as differentiation capacity. Moreover, transcriptome analysis revealed differential expression of several genes between both OPC populations. In spinal cord OPC we confirmed upregulation of SKAP2, a cytoplasmatic adaptor protein known for its implication in cytoskeletal remodelling and migration in other cell types. Recent findings suggest that actin dynamics determine not only oligodendroglial migration, but also differentiation: Whereas actin polymerization is important for process extension, actin destabilization and depolymerization is required for myelin sheath formation. Downregulation or complete lack of SKAP2 in OPC resulted in reduced migration and impaired morphological maturation in oligodendrocytes. In contrast, overexpression of SKAP2 as well as constitutively active SKAP2 increased OPC migration suggesting that SKAP2 function is dependent on activation by phosphorylation. Furthermore, lack of SKAP2 enhanced the positive effect on OPC migration after integrin activation suggesting that SKAP2 acts as modulator of integrin dependent migration. In summary, we demonstrate the presence of intrinsic differences between spinal cord and brain OPC and identified SKAP2 as a new regulator of oligodendroglial migration and sheath formation. Significance statementOPC play an important role in many still incurable diseases, such as multiple sclerosis, leukodystrophies or neurodegenerative diseases. Their heterogeneity in different CNS regions has recently been identified. Here, we observed increased migration and differentiation capabilities of OPC isolated from the spinal cord compared to brain OPC and confirmed differences in the transcriptome between these two cell populations. Furthermore, we identified SKAP2 as potential modulator of actin dynamics in oligodendrocytes. Whereas knockdown or lack of SKAP2 impairs migration and myelin sheath formation in mouse and human oligodendrocytes, overexpression of wildtype or constitutively active SKAP2 enhances the migratory capacity of murine oligodendrocytes. In summary, we present SKAP2 as modulator of cytoskeletal dynamics regulating OPC migration, differentiation and myelin sheath formation.

neuroscience↗

bioRxiv2020

m6A RNA methylation regulates promoter proximal pausing of RNA Polymerase II

RNA Polymerase II (RNAP II) pausing is essential to precisely control gene expression and is critical for development of metazoans. Here, we show that the m6A RNA modification regulates promoter-proximal RNAP II pausing. The m6A methyltransferase complex (MTC), with the nuclear reader Ythdc1, are recruited to gene promoters. Depleting the m6A MTC leads to a decrease in RNAP II pause release and in Ser2P occupancy on the gene body, and affects nascent RNA transcription. Tethering Mettl3 to a heterologous gene promoter is sufficient to increase RNAP II pause release, an effect that relies on its m6A catalytic domain. Collectively, our data reveal an important link between RNAP II pausing and the m6A RNA modification, thus adding another layer to m6A-mediated gene regulation.

molecular biology↗

bioRxiv2019

SPARTA imputes sparse single-cell ChIP-seq signals leveraging epigenomic bulk ENCODE data

MotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors. ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real data. Results on simulated data show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways. An imputation method that allows the interpretation of the underlying models facilitates users to gain an even deeper understanding of individual cells and, consequently, of sparse scChIP-seq datasets. Availability and implementationOur interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA

bioinformatics↗

bioRxiv2019

Computational identification of cell-specific variable regions in ChIP-seq data

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7, and in mouse embryonic stem cells (mESCs). These variable-occupancy target regions (VOTs) are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow up experiments.

bioinformatics↗

bioRxiv2019

Machine Learning in Quality Assessment of Early Stage Next-Generation Sequencing Data

Controlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following URL: https://github.com/salbrec/seqQscorer.

bioinformatics↗