DOT1L promotes progenitor proliferation and primes neuronal layer identity in the developing cerebral cortex

Cortical development is controlled by transcriptional programs, which are orchestrated by transcription factors. Yet, stable inheritance of spatiooral activity of factors influencing cell fate and localization in different layers is only partly understood. Here we find that deletion of Dot1l in the murine telencephalon leads to cortical layering defects, indicating DOT1L activity and chromatin methylation at H3K79 impact on the cell cycle, and influence transcriptional programs conferring upper layer identity in early progenitors. Specifically, DOT1L prevents premature differentiation by increasing expression of genes that regulate asymmetric cell division (Vangl2, Cenpj). Loss of DOT1L results in reduced numbers of progenitors expressing genes including SoxB1 gene family members. Loss of DOT1L also leads to altered cortical distribution of deep layer neurons that express either TBR1, CTIP2 or SOX5, and less activation of transcriptional programs that are characteristic for upper layer neurons (Satb2, Pou3f3, Cux2, SoxC family members). Data from three different mouse models suggest that DOT1L balances transcriptional programs necessary for proper neuronal composition and distribution in the six cortical layers. Furthermore, because loss of DOT1L in the pre-neurogenic phase of development impairs specifically generation of SATB2-expressing upper layer neurons, our data suggest that DOT1L primes upper layer identity in cortical progenitors.Fil: Franz, Henriette. Universität Freiburg Im Breisgau; AlemaniaFil: Villarreal, Alejandro. Universität Freiburg Im Breisgau; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Heidrich, Stefanie. Universität Freiburg Im Breisgau; AlemaniaFil: Videm, Pavankumar. Universität Freiburg Im Breisgau; AlemaniaFil: Kilpert, Fabian. Max Planck Institute Of Immunobiology And Epigenetics; AlemaniaFil: Mestres, Ivan. Technical University Dresden; AlemaniaFil: Calegari, Federico. Technical University Dresden; AlemaniaFil: Backofen, Rolf. Universidad de Copenhagen; Dinamarca. Universität Freiburg Im Breisgau; AlemaniaFil: Manke, Thomas. Max Planck Institute Of Immunobiology And Epigenetics; AlemaniaFil: Vogel, Tanja. Universität Freiburg Im Breisgau; Alemani

The RNA workbench: Best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis

Community-driven ELIXIR activities in single-cell omics

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research. Keywor

Community-Driven Data Analysis Training for Biology

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable

FOXG1 Regulates PRKAR2B Transcriptionally and Posttranscriptionally via miR200 in the Adult Hippocampus

Rett syndrome is a complex neurodevelopmental disorder that is mainly caused by mutations in MECP2. However, mutations in FOXG1 cause a less frequent form of atypical Rett syndrome, called FOXG1 syndrome. FOXG1 is a key transcription factor crucial for forebrain development, where it maintains the balance between progenitor proliferation and neuronal differentiation. Using genome-wide small RNA sequencing and quantitative proteomics, we identified that FOXG1 affects the biogenesis of miR200b/a/429 and interacts with the ATP-dependent RNA helicase, DDX5/p68. Both FOXG1 and DDX5 associate with the microprocessor complex, whereby DDX5 recruits FOXG1 to DROSHA. RNA-Seq analyses of Foxg1cre/+ hippocampi and N2a cells overexpressing miR200 family members identified cAMP-dependent protein kinase type II-beta regulatory subunit (PRKAR2B) as a target of miR200 in neural cells. PRKAR2B inhibits postsynaptic functions by attenuating protein kinase A (PKA) activity; thus, increased PRKAR2B levels may contribute to neuronal dysfunctions in FOXG1 syndrome. Our data suggest that FOXG1 regulates PRKAR2B expression both on transcriptional and posttranscriptional levels.Fil: Weise, Stefan C.. Institute Of Anatomy And Cell Biology; AlemaniaFil: Arumugam, Ganeshkumar. Institute Of Anatomy And Cell Biology; AlemaniaFil: Villarreal, Alejandro. Institute Of Anatomy And Cell Biology; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Videm, Pavankumar. Universität Freiburg Im Breisgau; AlemaniaFil: Heidrich, Stefanie. Institute Of Anatomy And Cell Biology; AlemaniaFil: Nebel, Nils. Institute Of Anatomy And Cell Biology; AlemaniaFil: Dumit, Veronica Ines. Universität Freiburg Im Breisgau; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sananbenesi, Farahnaz. Deutsches Zentrum Für Neurodegenerative Erkrankungen E.v.; AlemaniaFil: Reimann, Viktoria. Albert Ludwigs University Of Freiburg; AlemaniaFil: Craske, Madeline. Active Motif Incorporation; Estados UnidosFil: Schilling, Oliver. Universität Freiburg Im Breisgau; AlemaniaFil: Hess, Wolfgang R.. Universität Freiburg Im Breisgau; AlemaniaFil: Fischer, Andre. Universitätsmedizin Göttingen; Alemania. Deutsches Zentrum Für Neurodegenerative Erkrankungen E.v.; AlemaniaFil: Backofen, Rolf. Universidad de Copenhagen; DinamarcaFil: Vogel, Tanja. Universität Freiburg Im Breisgau; Alemani

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench

deeptools/deepTools: 3.5.4

error handling + cases for bwAverage with > 2 samples (@lldelisle ) Tick.label deprecated for matplotlib 3.8 (@lldelisle ) matplotlib minimal version is now 3.5 cicd update for pypi pus