Biology subjects

Schröder, C.

Publications and source records attributed to Schröder, C..

7 recordsLinked to original sources

bioRxiv2025

Aberrant splicing prediction during human organ development

Developmental disorders constitute a major class of genetic diseases, yet tools to identify splicing-disruptive variants during development are lacking. To address this need we extended the AbSplice framework to incorporate splicing dynamics across developmental stages. Moreover, we introduced several improvements including a refined ground truth from the aberrant splicing caller FRASER2, a continuous representation of splice site usage, and integration of the rich set of predictions from the sequence-based model Pangolin. These advances improve the performance of the original model at predicting aberrant splicing events and enable predictions across embryonic, childhood, and adult tissues. Genome-wide scores for all single-nucleotide variants and a web interface to score indels are available to facilitate the exploration of the predictions. Our genome-wide predictions reveal a class of variants with splicing-disruptive effects confined to early development, particularly abundant in the brain (>18,000 variants). These variants are enriched in loss-of-function intolerant genes and neurodevelopmental disorder genes. Within the rare-disease cohort Solve-RD high-impact brain-specific predictions are significantly enriched in individuals affected by neurodevelopmental disorders (NDD) in NDD-linked genes. Furthermore, the predicted developmental timing of the splicing disruption correlates with the clinical age of onset. Analysis of genomes of individuals with a suspected Mendelian disorder from Genomics England identified 26 unique variants in disease-linked genes, with stronger predicted effects during development than in adulthood, including a candidate new diagnosis in the gene FGFR1. Altogether, these results improve the accuracy of splice-disruptive variant prediction and provide tissue and developmental context to aid interpretation in rare disease diagnostics.

genomics↗

bioRxiv2024

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

neuroscience↗

bioRxiv2024

Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience--JO_SCPLOWAXLEYC_SCPLOW--which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JO_SCPLOWAXLEYC_SCPLOW opens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that JO_SCPLOWAXLEYC_SCPLOW can learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that JO_SCPLOWAXLEYC_SCPLOW makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that JO_SCPLOWAXLEYC_SCPLOW dramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.

neuroscience↗

bioRxiv2022

Insane in the vembrane: filtering and transforming VCF/BCF files

Data from sequencing of DNA or RNA samples is routinely scanned for variation. Such variation data is stored in the standardized VCF/BCF format with additional annotations. Analyses of variants usually involve steps where filters are applied to narrow down the list of candidates for further analysis. A number of tools for this task exist, differing in functionality, speed, syntax and supported annotations. Thus, users have to switch between tools depending on the filtering task, and have to adapt to the respective filtering syntax. We present vembrane as a command line VCF/BCF filtering tool that consolidates and extends the filtering functionality of previous software to meet any imaginable filtering use case. To this end, vembrane exposes the VCF/BCF file type specification and its inofficial extensions by the annotation tools VEP and SnpEff as Python data structures. vembrane filter enables filtration by arbitrary Python expressions over (combinations of) annotations, requiring only basic knowledge of the Python programming language. vembrane table allows users to generate tables from subsets of annotations or functions thereof. Finally, it is fast, thanks to pysam, a Python wrapper around htslib, and by relying on Pythons lazy evaluation. Availability and ImplementationSource code and installation instructions are available at github.com/vembrane/vembrane, DOI: 10.5281/zen-odo.7003981.

bioinformatics↗

bioRxiv2020

Near-optimal rotation of colour space by zebrafish cones in vivo

For colour vision, retinal circuits separate information about intensity and wavelength. This requires comparison of at least two spectrally distinct photoreceptors, as in the case of most mammals. However, many vertebrates use the full complement of four ancestral cone-types ( red, green, blue, UV), and in those cases the nature and implementation of this computation remains poorly understood. Here, we establish the complete circuit architecture of outer retinal circuits underlying colour processing in larval zebrafish, which involves the full ancestral complement of four cone- and three horizontal cell types. Our findings reveal that the synaptic outputs of red- and green-cones efficiently rotate the encoding of natural daylight in a principal component analysis (PCA)-like manner to yield primary achromatic and spectrally-opponent axes, respectively. Together, these two cones capture 91.3% of the spectral variance in natural light. Next, blue-cones are tuned so as to capture most remaining variance when opposed to green-cones. Finally, UV-cones present a UV-achromatic axis for prey capture. We note that fruit flies - the only other tetrachromat species where comparable circuit-level information is available - use essentially the same strategy to extract spectral information from their relatively blue-shifted terrestrial visual world. Together, our results suggest that rotating colour space into primary achromatic and chromatic axes at the eyes first synapse may be a fundamental principle of colour vision when using more than two spectrally well-separated photoreceptor types.

neuroscience↗

bioRxiv2020

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retinas cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal func{-}tion are black-box CNN models which are agnostic to such biological knowledge. In particular, these models typically neglect the role of the many inhibitory circuits involving amacrine cells and the biophysical mechanisms underlying synaptic release. Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms. Fit to the responses of bipolar cells, the model generalized well to new stimuli including natural movie sequences, performing on par with or better than a benchmark black-box model. In pharmacology experiments, the model replicated in silico the effect of blocking specific amacrine cell populations with high fidelity, indicating that it had learned key circuit functions. Also, more in depth comparisons showed that connectivity patterns learned by the model were well matched to connectivity patterns extracted from connectomics data. Thus, our model provides a biologically interpretable data-driven account of temporal processing in the inner retina, filling the gap between purely black-box and detailed biophysical modeling.

neuroscience↗

bioRxiv2019

wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

BackgroundAnalysing whole genome bisulfite sequencing datasets is a data-intensive task that requires comprehensive and reproducible workflows to generate valid results. While many algorithms have been developed for tasks such as alignment, comprehensive end-to-end pipelines are still sparse. Furthermore, previous pipelines lack features or show technical deficiencies, thus impeding analyses. ResultsWe developed wg-blimp (whole genome bisulfite sequencing methylation analysis pipeline) as an end-to-end pipeline to ease whole genome bisulfite sequencing data analysis. It integrates established algorithms for alignment, quality control, methylation calling, detection of differentially methylated regions, and methylome segmentation, requiring only a reference genome and raw sequencing data as input. Comparing wg-blimp to previous end-to-end pipelines reveals similar setups for common sequence processing tasks, but shows differences for post-alignment analyses. We improve on previous pipelines by providing a more comprehensive analysis workflow as well as an interactive user interface. To demonstrate wg-blimps ability to produce correct results we used it to call differentially methylated regions for two publicly available datasets. We were able to replicate 112 of 114 previously published regions, and found results to be consistent with previous findings. We further applied wg-blimp to a publicly available sample of embryonic stem cells to showcase methylome segmentation. As expected, unmethylated regions were in close proximity of transcription start sites. Segmentation results were consistent with previous analyses, despite different reference genomes and sequencing techniques. Conclusionswg-blimp provides a comprehensive analysis pipeline for whole genome bisulfite sequencing data as well as a user interface for simplified result inspection. We demonstrated its applicability by analysing multiple publicly available datasets. Thus, wg-blimp is a relevant alternative to previous analysis pipelines and may facilitate future epigenetic research.

bioinformatics↗