Biology subjects

Kopp, W.

Publications and source records attributed to Kopp, W..

6 recordsLinked to original sources

bioRxiv2023

Decoding Single-Cell Multiomics: scMaui - A Deep Learning Framework for Uncovering Cellular Heterogeneity in Presence of Batch Effects and Missing Data

The recent advances in high-throughput single-cell sequencing has significantly required computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome the sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on stacked variational encoders and adversarial learning. scMaui reduces the dimensionality of integrated data modalities to a latent space which outlines cellular heterogeneity. It can handle multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover various assays and preprocessing pipelines. We show that scMaui accomplishes superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

bioinformatics↗

bioRxiv2021

Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish

Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.

developmental biology↗

bioRxiv2021

Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, shedding light on gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach, a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase both its individual advantages on carefully chosen real and simulated data sets, as well as the benefits for detailed cell type characterization via integrating multiple complex datasets.

bioinformatics↗

bioRxiv2020

Single-cell-resolved dynamics of chromatin architecture delineate cell and regulatory states in wildtype and cloche/npas4l mutant zebrafish embryos

DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many of the genes that regulate embryonic development have been described, the 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 and developed a new computational tool, ScregSeg, that selects informative genome segments and classifies complex accessibility dynamics. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization, expanding knowledge of regulatory principles between chromatin modalities. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos revealed novel cellular roles for this hemato-vascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work constitutes a valuable resource for future studies in developmental, molecular, and computational biology.

genomics↗

bioRxiv2019

Janggu: Deep Learning for Genomics

MotivationIn recent years, numerous applications have demonstrated the potential of deep learning for an improved understanding of biological processes. However, most deep learning tools developed so far are designed to address a specific question on a fixed dataset and/or by a fixed model architecture. Adapting these models to integrate new datasets or to address different hypotheses can lead to considerable software engineering effort. To address this aspect we have built Janggu, a python library that facilitates deep learning for genomics applications.\n\nResultsJanggu aims to ease data acquisition and model evaluation in multiple ways. Among its key features are special dataset objects, which form a unified and flexible data acquisition and pre-processing framework for genomics data that enables streamlining of future research applications through reusable components. Through a numpy-like interface, the dataset objects are directly compatible with popular deep learning libraries, including keras. Furthermore, Janggu offers the possibility to visualize predictions as genomic tracks or by exporting them to the BIGWIG format. We illustrate the functionality of Janggu on several deep learning genomics applications. First, we evaluate different model topologies for the task of predicting binding sites for the transcription factor JunD. Second, we demonstrate the framework on published models for predicting chromatin effects. Third, we show that promoter usage measured by CAGE can be predicted using DNase hyper-sensitivity, histone modifications and DNA sequence features. We improve the performance of these models due to a novel feature in Janggu that allows us to include high-order sequence features. We believe that Janggu will help to significantly reduce repetitive programming overhead for deep learning applications in genomics, while at the same time enabling computational biologists to assess biological hypotheses more rapidly.\n\nAvailabilityJanggu is freely available under a GPL-v3 license on https://github.com/BIMSBbioinfo/janggu or via https://pypi.org/project/janggu

bioinformatics↗

bioRxiv2017

Unsupervised learning of DNA sequence features using a convolutional restricted Boltzmann machine

Transcription factors (TFs) are important contributors to gene regulation. They specifically bind to short DNA stretches known as transcription factor binding sites (TFBSs), which are contained in regulatory regions (e.g. promoters), and thereby influence a target genes expression level. Computational biology has contributed substantially to understanding regulatory regions by developing numerous tools, including for discovering de novo motif. While those tools primarily focus on determining and studying TFBSs, the surrounding sequence context is often given less attention. In this paper, we attempt to fill this gap by adopting a so-called convolutional restricted Boltzmann machine (cRBM) that captures redundant features from the DNA sequences. The model uses an unsupervised learning approach to derive a rich, yet interpretable, description of the entire sequence context. We evaluated the cRBM on a range of publicly available ChIP-seq peak regions and investigated its capability to summarize heterogeneous sets of regulatory sequences in comparison with MEME-Chip, a popular motif discovery tool. In summary, our method yields a considerably more accurate description of the sequence composition than MEME-Chip, providing both a summary of strong TF motifs as well as subtle low-complexity features.

bioinformatics↗