Biology subjects

Ohler, U.

Publications and source records attributed to Ohler, U..

At least 37 records · Page 2Linked to original sources

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

The BTB transcription factors ZBTB11 and ZFP131 maintain pluripotency by pausing POL II atpro-differentiation genes

In pluripotent cells, a delicate activation-repression balance maintains pro-differentiation genes ready for rapid activation. The identity of transcription factors (TFs) that specifically repress pro-differentiation genes remains obscure. By targeting ~1,700 TFs with CRISPR loss-of-function screen, we found that ZBTB11 and ZFP131 are required for embryonic stem cell (ESC) pluripotency. ZBTB11 and ZFP131 maintain promoter-proximally paused Polymerase II at pro-differentiation genes in ESCs. ZBTB11 or ZFP131 loss leads to NELF pausing factor release, an increase in H3K4me3, and transcriptional upregulation of genes associated with all three germ layers. Together, our results suggest that ZBTB11 and ZFP131 maintain pluripotency by preventing premature expression of pro-differentiation genes and present a generalizable framework to maintain cellular potency. One-sentence summaryA Transcription Factor-wide CRISPR screen identifies ZBTB11 and ZFP131 maintaining pluripotency by pausing POL II at pro-differentiation genes

developmental biology↗

bioRxiv2020

Spatio-temporal mRNA dynamics in the early zebrafish embryo

Early stages of embryogenesis depend heavily on subcellular localization and transport of maternally deposited mRNA. However, systematic analysis of these processes is currently hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combined spatially-resolved transcriptomics and single-cell RNA labeling to study the spatio-temporal dynamics of the transcriptome during the first few hours of zebrafish development. We measured spatial localization of mRNA molecules with sub-single-cell resolution at the one-cell stage, which allowed us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we established a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enabled us to follow the fate of individual maternal transcripts until gastrulation. This approach revealed that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquired spatial transcriptomes of two xenopus species, and we compared evolutionary conservation of localized genes as well as enriched sequence motifs. In summary, we established sub-single-cell spatial transcriptomics and single-cell RNA labeling to reveal principles of mRNA localization in early vertebrate development.

systems biology↗

bioRxiv2020

SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

Identification of markers is an essential step in single-cell analytic. Current marker identification strategies typically rely on cluster assignments of cells. Cluster assignment, in particular of development data, is non-trivial, potentially arbitrary and commonly relies on prior knowledge. Yet, cluster uncertainty is not commonly taken into account. In response, we present SEMITONES, a principled method for cluster-free marker identification. We showcase its application on healthy haematopoiesis data as 1) a robust alternative to highly variable gene selection, 2) for marker gene and regulatory region identification, and 3) for the construction of co-enrichment networks that reveal regulators of cell identity.

systems biology↗

bioRxiv2020

A single cell Arabidopsis root atlas reveals developmental trajectories in wild type and cell identity mutants

SummaryCell fate acquisition is a fundamental developmental process in all multicellular organisms. Yet, much is unknown regarding how a cell traverses the pathway from stem cell to terminal differentiation. Advances in single cell genomics1 hold promise for unraveling developmental mechanisms2–3 in tissues4, organs5–6, and organisms7–8. However, lineage tracing can be challenging for some tissues9 and integration of high-quality datasets is often necessary to detect rare cell populations and developmental states10,11. Here, we harmonized single cell mRNA sequencing data from over 110,000 cells to construct a comprehensive atlas for a stereotypically developing organ with indeterminate growth, the Arabidopsis root. To test the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single cell resolution, shortroot and scarecrow. Although both transcription factors are required for early specification of cell identity12, our results suggest the existence of an alternative pathway acting in mature cells to specify endodermal identity, for which SHORTROOT is required. Uncovering the architecture of this pathway will provide insight into specification and stabilization of the endodermis, a tissue analogous to the mammalian epithelium. Thus, the atlas is a pivotal advance for unraveling the transcriptional programs that specify and maintain cell identity to regulate organ development in space and time.Competing Interest StatementPNB is the co-founder and Chair of the Scientific Advisory Board of Hi Fidelity Genetics, Inc, a company that works on crop root growth.View Full Text

plant biology↗

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↗

bioRxiv2020

The m6A reader Ythdf restricts axonal growth in Drosophila through target selection modulation of the Fragile X mental retardation protein

N6-methyladenosine (m6A) regulates a variety of physiological processes through modulation of RNA metabolism. The modification is particularly enriched in the nervous system of several species, and its dysregulation has been associated with neurodevelopmental defects and neural dysfunctions. In Drosophila, loss of m6A alters fly behavior albeit the underlying mechanism and the role of m6A during nervous system development have remained elusive. Here we find that impairment of the m6A pathway leads to axonal overgrowth and misguidance at larval neuromuscular junctions as well as in the adult mushroom bodies. We identify Ythdf as the main m6A reader in the nervous system being required for limiting axonal growth. Mechanistically, we show that Ythdf directly interacts with Fragile X mental retardation protein to inhibit the translation of key transcripts involved in axonal growth regulation. Altogether, this study demonstrates that the m6A pathway controls development of the nervous system by modulating Fmr1 target selection.

molecular biology↗

bioRxiv2019

The Chloroplast RNA binding protein CP31A shows cold-dependent RNA association and mediates cold-resistance via its acidic domain

Chloroplast RNA metabolism is characterized by long-lived mRNAs that undergo a multitude of post-transcriptional processing events. Chloroplast RNA accumulation responds to environmental cues, foremost light and temperature. A large number of nuclear-encoded RNA-binding proteins (RBPs) are required for chloroplast RNA metabolism, but we do not yet know how chloroplast RBPs convert abiotic signals into gene expression changes. Previous studies showed that the chloroplast ribonucleoprotein 31A (CP31A) is required for the stabilization of multiple chloroplast mRNAs in the cold, and that the phosphorylation of CP31A at various residues within its N-terminal acidic domain (AD) can alter its affinity for RNA in vitro. Loss of CP31A leads to cold sensitive plants that exhibit bleached tissue at the center of the vegetative rosette. Here, by applying RIP-Seq, we demonstrated that CP31A shows increased affinity for a large number of chloroplast RNAs in vivo in the cold. Among the main targets of CP31A were RNAs encoding subunits of the NDH complex and loss of CP31A lead to reduced accumulation of ndh transcripts. Deletion analyses revealed that cold-dependent RNA binding and cold resistance of chloroplast development both depend on the AD of CP31A. Together, our analysis established the AD of CP31A as a key mediator of cold acclimation of the chloroplast transcriptome. One sentence summaryCold exposure induces increased RNA association of the RRM protein CP31A, which mediates cold-resistance of Arabidopsis thaliana via its acidic domain

plant biology↗

bioRxiv2019

Integrated Proteogenomic Deep Sequencing and Analytics Accurately Identify Non-Canonical Peptides in Tumor Immunopeptidomes

Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides that derive from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides and of an immunogenic peptide from a downstream reading frame in the melanoma stem cell marker gene ABCB5. It holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

cancer biology↗

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↗

bioRxiv2019

SaTAnn quantifies translation on the functionally heterogeneous transcriptome

At the center of the gene expression cascade, translation is fundamental in defining the fate of much of the transcribed genome. RNA sequencing enables the quantification of complex transcript mixtures, often detecting several splice isoforms of unknown functions for one gene. We have developed ORFquant, a new approach to annotate and quantify translation at the single open reading frame (ORF) level, using information from Ribo-seq data. Relying on a novel approach for transcript filtering, we quantify translation on thousands of ORFs, showing the power of Ribo-seq in revealing alternative ORFs on multiple isoforms per gene. While we find that one ORF represents the dominant translation product for most genes, we also detect genes with translated ORFs on multiple transcript isoforms, including targets of RNA surveillance mechanisms. Assessing the translation output across human cell lines reveals the extent of gene-specific differences in protein production, which are supported by steady-state protein abundance estimates. Computational analysis of Ribo-seq data with ORFquant (available at https://github.com/lcalviell/ORFquant) provides a window into the heterogeneous functions of complex transcriptomes.

genomics↗

bioRxiv2019

Ribo-seQC: comprehensive analysis of cytoplasmic and organellar ribosome profiling data

SummaryRibosome profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq data, providing in-depth insights on data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects.\n\nAvailabilityRibo-seQC is available at https://github.com/ohlerlab/RiboseQC and submitted to Bioconductor.\n\nContactuwe.ohler@mdc-berlin.de

bioinformatics↗

bioRxiv2019

Deep neural networks for interpreting RNA binding protein target preferences

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP binding preferences. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors of RBPs and can provide new insights about the regulatory functions of RBPs.

bioinformatics↗

bioRxiv2018

Global identification of functional microRNA::mRNA interactions in Drosophila

MicroRNAs (miRNAs) are key mediators of post-transcriptional gene expression silencing. Although Drosophila has been of critical importance for miRNA discovery, biogenesis and function, there has been no comprehensive experimental annotation of functional miRNA target sites. To close this gap, we generated the first in vivo map of miRNA::mRNA interactions in Drosophila melanogaster, making use of crosslinked nucleotides in Argonaute (AGO) crosslinking and immunoprecipitation (CLIP) experiments that enable an unambiguous assignment of miRNAs to AGO binding sites at much higher signal-to-noise ratio than computational predictions alone.\n\nAbsolute quantification of cellular miRNA levels showed the miRNA pool in Drosophila cell lines to be more diverse than previously reported. Benchmarking two different CLIP approaches, we identified a similar predictive potential to unambiguously assign thousands of miRNA::mRNA pairs from AGO1 interaction data at unprecedented depth. Quantitative RNA-Seq and subcodon-resolution ribosomal footprinting data upon AGO1 depletion enabled the determination of miRNA-mediated effects on target expression and translation. We thus provide the first comprehensive resource of miRNA target sites as well as their quantitative functional impact in Drosophila.

systems biology↗

bioRxiv2018

Purification of Cross-linked RNA-Protein Complexes by Phenol-Toluol Extraction

Recent methodological advances allowed the identification of an increasing number of RNA-binding proteins (RBPs) and their RNA-binding sites. Most of those methods rely, however, on capturing proteins associated to polyadenylated RNAs which neglects RBPs bound to non-adenylate RNA classes (tRNA, rRNA, pre-mRNA) as well as the vast majority of species that lack poly-A tails in their mRNAs (including all archea and bacteria). To overcome these limitations, we have developed a novel protocol, Phenol Toluol extraction (PTex), that does not rely on a specific RNA sequence or motif for isolation of cross-linked ribonucleoproteins (RNPs), but rather purifies them based entirely on their physicochemical properties. PTex captures RBPs that bind to RNA as short as 30 nt, RNPs directly from animal tissue and can be used to simplify complex workflows such as PAR-CLIP. Finally, we provide a first global RNA-bound proteome of human HEK293 cells and Salmonella Typhimurium as a bacterial species.

molecular biology↗

bioRxiv2018

Deciphering human ribonucleoprotein regulatory networks

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP-RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules encoded functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP-RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

genomics↗

bioRxiv2018

SSMART: Sequence-structure motif identification for RNA-binding proteins

MotivationRNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized.\n\nResultsWe developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3 UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP.\n\nAvailabilitySSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/\n\nContactuwe.ohler@mdc-berlin.de

bioinformatics↗

bioRxiv2018

Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets via protocol-specific bias modeling

DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBS) in regulatory regions via footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impacts the discrimination of footprint from background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.

genomics↗