Fostering accessible online education using Galaxy as an e-learning platform.

The COVID-19 pandemic is shifting teaching to an online setting all over the world. The Galaxy framework facilitates the online learning process and makes it accessible by providing a library of high-quality community-curated training materials, enabling easy access to data and tools, and facilitates sharing achievements and progress between students and instructors. By combining Galaxy with robust communication channels, effective instruction can be designed inclusively, regardless of the students' environments.

Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization.

The Galaxy HiCExplorer provides a web service at https://hicexplorer.usegalaxy.eu. It enables the integrative analysis of chromosome conformation by providing tools and computational resources to pre-process, analyse and visualize Hi-C, Capture Hi-C (cHi-C) and single-cell Hi-C (scHi-C) data. Since the last publication, Galaxy HiCExplorer has been expanded considerably with new tools to facilitate the analysis of cHi-C and to provide an in-depth analysis of Hi-C data. Moreover, it supports the analysis of scHi-C data by offering a broad range of tools. With the help of the standard graphical user interface of Galaxy, presented workflows, extensive documentation and tutorials, novices as well as Hi-C experts are supported in their Hi-C data analysis with Galaxy HiCExplorer.

The omics discovery REST interface.

The Omics Discovery Index is an open source platform that can be used to access, discover and disseminate omics datasets. OmicsDI integrates proteomics, genomics, metabolomics, models and transcriptomics datasets. Using an efficient indexing system, OmicsDI integrates different biological entities including genes, transcripts, proteins, metabolites and the corresponding publications from PubMed. In addition, it implements a group of pipelines to estimate the impact of each dataset by tracing the number of citations, reanalysis and biological entities reported by each dataset. Here, we present the OmicsDI REST interface (www.omicsdi.org/ws/) to enable programmatic access to any dataset in OmicsDI or all the datasets for a specific provider (database). Clients can perform queries on the API using different metadata information such as sample details (species, tissues, etc), instrumentation (mass spectrometer, sequencer), keywords and other provided annotations. In addition, we present two different libraries in R and Python to facilitate the development of tools that can programmatically interact with the OmicsDI REST interface.

GLASSgo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs.

MOTIVATION: The correct prediction of bacterial sRNA homologs is a prerequisite for many downstream analyses based on comparative genomics, but it is frequently challenging due to the short length and distinct heterogeneity of such homologs. GLobal Automatic Small RNA Search go (GLASSgo) is an efficient tool for the prediction of sRNA homologs from a single input query. To make the algorithm available to a broader community, we offer a Docker container along with a free-access web service. For non-computer scientists, the web service provides a user-friendly interface. However, capabilities were lacking so far for batch processing, version control and direct interaction with compatible software applications as a workflow management system can provide. RESULTS: Here, we present GLASSgo 1.5.2, an updated version that is fully incorporated into the workflow management system Galaxy. The improved version contains a new feature for extracting the upstream regions, allowing the search for conserved promoter elements. Additionally, it supports the use of accession numbers instead of the outdated GI numbers, which widens the applicability of the tool. AVAILABILITY AND IMPLEMENTATION: GLASSgo is available at https://github.com/lotts/GLASSgo/ under the MIT license and is accompanied by instruction and application data. Furthermore, it can be installed into any Galaxy instance using the Galaxy ToolShed.

Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization.

Galaxy HiCExplorer is a web server that facilitates the study of the 3D conformation of chromatin by allowing Hi-C data processing, analysis and visualization. With the Galaxy HiCExplorer web server, users with little bioinformatic background can perform every step of the analysis in one workflow: mapping of the raw sequence data, creation of Hi-C contact matrices, quality assessment, correction of contact matrices and identification of topological associated domains (TADs) and A/B compartments. Users can create publication ready plots of the contact matrix, A/B compartments, and TADs on a selected genomic locus, along with additional information like gene tracks or ChIP-seq signals. Galaxy HiCExplorer is freely usable at: https://hicexplorer.usegalaxy.eu and is available as a Docker container: https://github.com/deeptools/docker-galaxy-hicexplorer.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

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.

BioContainers: an open-source and community-driven framework for software standardization.

MOTIVATION: BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). AVAILABILITY AND IMPLEMENTATION: The software is freely available at github.com/BioContainers/. CONTACT: yperez@ebi.ac.uk.

Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers.

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.

DOT1L Activity Promotes Proliferation and Protects Cortical Neural Stem Cells from Activation of ATF4-DDIT3-Mediated ER Stress In Vitro.

Growing evidence suggests that the lysine methyltransferase DOT1L/KMT4 has important roles in proliferation, survival, and differentiation of stem cells in development and in disease. We investigated the function of DOT1L in neural stem cells (NSCs) of the cerebral cortex. The pharmacological inhibition and shRNA-mediated knockdown of DOT1L impaired proliferation and survival of NSCs. DOT1L inhibition specifically induced genes that are activated during the unfolded protein response (UPR) in the endoplasmic reticulum (ER). Chromatin-immunoprecipitation analyses revealed that two genes encoding for central molecules involved in the ER stress response, Atf4 and Ddit3 (Chop), are marked with H3K79 methylation. Interference with DOT1L activity resulted in transcriptional activation of both genes accompanied by decreased levels of H3K79 dimethylation. Although downstream effectors of the UPR, such as Ppp1r15a/Gadd34, Atf3, and Tnfrsf10b/Dr5 were also transcriptionally activated, this most likely occurred in response to increased ATF4 expression rather than as a direct consequence of altered H3K79 methylation. While stem cells are particularly vulnerable to stress, the UPR and ER stress have not been extensively studied in these cells yet. Since activation of the ER stress program is also implicated in directing stem cells into differentiation or to maintain a proliferative status, the UPR must be tightly regulated. Our and published data suggest that histone modifications, including H3K4me3, H3K14ac, and H3K79me2, are implicated in the control of transcriptional activation of ER stress genes. In this context, the loss of H3K79me2 at the Atf4- and Ddit3-promoters appears to mark a point-of-no-return that activates the death program in NSCs.

Deciphering the Epigenetic Code of Cardiac Myocyte Transcription.

RATIONALE: Epigenetic mechanisms are crucial for cell identity and transcriptional control. The heart consists of different cell types, including cardiac myocytes, endothelial cells, fibroblasts, and others. Therefore, cell type-specific analysis is needed to gain mechanistic insight into the regulation of gene expression in cardiac myocytes. Although cytosolic mRNA represents steady-state levels, nuclear mRNA more closely reflects transcriptional activity. To unravel epigenetic mechanisms of transcriptional control, cell type-specific analysis of nuclear mRNA and epigenetic modifications is crucial. OBJECTIVE: The aim was to purify cardiac myocyte nuclei from hearts of different species by magnetic- or fluorescent-assisted sorting and to determine the nuclear and cellular RNA expression profiles and epigenetic marks in a cardiac myocyte-specific manner. METHODS AND RESULTS: Frozen cardiac tissue samples were used to isolate cardiac myocyte nuclei. High sorting purity was confirmed for cardiac myocyte nuclei isolated from mice, rats, and humans. Deep sequencing of nuclear RNA revealed a major fraction of nascent, unspliced RNA in contrast to results obtained from purified cardiac myocytes. Cardiac myocyte nuclear and cellular RNA expression profiles showed differences, especially for metabolic genes. Genome-wide maps of the transcriptional elongation mark H3K36me3 were generated by chromatin-immunoprecipitation. Transcriptome and epigenetic data confirmed the high degree of cardiac myocyte-specificity of our protocol. An integrative analysis of nuclear mRNA and histone mark occurrence indicated a major impact of the chromatin state on transcriptional activity in cardiac myocytes. CONCLUSIONS: This study establishes cardiac myocyte-specific sorting of nuclei as a universal method to investigate epigenetic and transcriptional processes in cardiac myocytes of different origins. These data sets provide novel insight into cardiac myocyte transcription.

Echinocandin B biosynthesis: a biosynthetic cluster from Aspergillus nidulans NRRL 8112 and reassembly of the subclusters Ecd and Hty from Aspergillus pachycristatus NRRL 11440 reveals a single coherent gene cluster.

BACKGROUND: Echinocandins are nonribosomal lipopeptides produced by ascommycete fungi. Due to their strong inhibitory effect on fungal cell wall biosynthesis and lack of human toxicity, they have been developed to an important class of antifungal drugs. Since 2012, the biosynthetic gene clusters of most of the main echinocandin variants have been characterized. Especially the comparison of the clusters allows a deeper insight for the biosynthesis of these complex structures. RESULTS: In the genome of the echinocandin B producer Aspergillus nidulans NRRL 8112 we have identified a gene cluster (Ani) that encodes echinocandin biosynthesis. Sequence analyses showed that Ani is clearly delimited from the genomic context and forms a monophyletic lineage with the other echinocandin gene clusters. Importantly, we found that the disjunct genomic location of the echinocandin B gene cluster in A. pachycristatus NRRL 11440 on two separate subclusters, Ecd and Hty, at two loci was likely an artifact of genome misassembly in the absence of a reference sequence. We show that both sequences can be aligned resulting a single cluster with a gene arrangement collinear compared to other clusters of Aspergillus section Nidulantes. The reassembled gene cluster (Ecd/Hty) is identical to a putative gene cluster (AE) that was previously deposited at the NCBI as a sequence from A. delacroxii NRRL 3860. PCR amplification of a part of the gene cluster resulted a sequence that was very similar (97 % identity), but not identical to that of AE. CONCLUSIONS: The Echinocandin B biosynthetic cluster from A. nidulans NRRL 8112 (Ani) is particularly similar to that of A. pachycristatus NRRL 11440 (Ecd/Hty). Ecd/Hty was originally reported as two disjunct sub-clusters Ecd and Hty, but is in fact a continuous sequence with the same gene order as in Ani. According to sequences of PCR products amplified from genomic DNA, the echinocandin B producer A. delacroxii NRRL 3860 is closely related to A. pachycristatus NRRL 11440. A PCR-product from the gene cluster was very similar, but clearly distinct from the sequence published for A. delacroxii NRRL 3860 at the NCBI (No. AB720074). As the NCBI entry is virtually identical with the re-assembled Ecd/Hty cluster, it is likely that it originates from A. pachycristatus NRRL 11440 rather than A. delacroxii NRRL 3860.

deepTools: a flexible platform for exploring deep-sequencing data.

We present a Galaxy based web server for processing and visualizing deeply sequenced data. The web server's core functionality consists of a suite of newly developed tools, called deepTools, that enable users with little bioinformatic background to explore the results of their sequencing experiments in a standardized setting. Users can upload pre-processed files with continuous data in standard formats and generate heatmaps and summary plots in a straight-forward, yet highly customizable manner. In addition, we offer several tools for the analysis of files containing aligned reads and enable efficient and reproducible generation of normalized coverage files. As a modular and open-source platform, deepTools can easily be expanded and customized to future demands and developments. The deepTools webserver is freely available at http://deeptools.ie-freiburg.mpg.de and is accompanied by extensive documentation and tutorials aimed at conveying the principles of deep-sequencing data analysis. The web server can be used without registration. deepTools can be installed locally either stand-alone or as part of Galaxy.

PyWATER: a PyMOL plug-in to find conserved water molecules in proteins by clustering.

SUMMARY: Conserved water molecules play a crucial role in protein structure, stabilization of secondary structure, protein activity, flexibility and ligand binding. Clustering of water molecules in superimposed protein structures, obtained by X-ray crystallography at high resolution, is an established method to identify consensus water molecules in all known protein structures of the same family. PyWATER is an easy-to-use PyMOL plug-in and identifies conserved water molecules in the protein structure of interest. PyWATER can be installed via the user interface of PyMOL. No programming or command-line knowledge is required for its use. AVAILABILITY AND IMPLEMENTATION: PyWATER and a tutorial are available at https://github.com/hiteshpatel379/PyWATER. PyMOL is available at http://www.pymol.org/ or http://sourceforge.net/projects/pymol/. CONTACT: stefan.guenther@pharmazie.uni-freiburg.de.

The purchasable chemical space: a detailed picture.

The screening of a reduced yet diverse and synthesizable region of the chemical space is a critical step in drug discovery. The ZINC database is nowadays routinely used to freely access and screen millions of commercially available compounds. We collected ∼125 million compounds from chemical catalogs and the ZINC database, yielding more than 68 million unique molecules, including a large portion of described natural products (NPs) and drugs. The data set was filtered using advanced medicinal chemistry rules to remove potentially toxic, promiscuous, metabolically labile, or reactive compounds. We studied the physicochemical properties of this compilation and identified millions of NP-like, fragment-like, inhibitors of protein-protein interactions (i-PPIs) like, and drug-like compounds. The related focused libraries were subjected to a detailed scaffold diversity analysis and compared to reference NPs and marketed drugs. This study revealed thousands of diverse chemotypes with distinct representations of building block combinations among the data sets. An analysis of the stereogenic and shape complexity properties of the libraries also showed that they present well-defined levels of complexity, following the tendency: i-PPIs-like < drug-like < fragment-like < NP-like. As the collected compounds have huge interest in drug discovery and particularly virtual screening and library design, we offer a freely available collection comprising over 37 million molecules under: http://pbox.pharmaceutical-bioinformatics.org , as well as the filtering rules used to build the focused libraries described herein.

StreptomeDB: a resource for natural compounds isolated from Streptomyces species.

Bacteria from the genus Streptomyces are very important for the production of natural bioactive compounds such as antibiotic, antitumour or immunosuppressant drugs. Around two-thirds of all known natural antibiotics are produced by these bacteria. An enormous quantity of crucial data related to this genus has been generated and published, but so far no freely available and comprehensive database exists. Here, we present StreptomeDB (http://www.pharmaceutical-bioinformatics.de/streptomedb/). To the best of our knowledge, this is the largest database of natural products isolated from Streptomyces. It contains >2400 unique and diverse compounds from >1900 different Streptomyces strains and substrains. In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities and synthesis routes (e.g. polyketide synthase derived and non-ribosomal peptides derived) is included. Data can be accessed through queries on compound names, chemical structures or organisms. Extraction from the literature was performed through automatic text mining of thousands of articles from PubMed, followed by manual curation. All annotated compound structures can be downloaded from the website and applied for in silico screenings for identifying new active molecules with undiscovered properties.

Regio- and stereoselective intermolecular oxidative phenol coupling in Streptomyces.

Intermolecular oxidative phenol coupling is the main process in nature for the formation of atroposelective biaryl compounds. Although well defined in plants and fungi, this type of dimerization reaction in bacteria is poorly understood. Therefore, the biosynthesis of julichromes, spectomycins, and setomimycin was investigated. The monomeric subunits of these biarylic pre-anthraquinones are derived from a common polyketidic precursor, yet the coupling reaction proceeds in a regioselective manner, with the position of attachment of the two subunits depending on the specific streptomycete strain. By using genome analysis and deletion experiments, the biosynthetic gene clusters were identified. Furthermore, it was established that cytochrome P450 enzymes are fundamentally involved during dimerization of the polyketide monomers.

Mining and evaluation of molecular relationships in literature.

MOTIVATION: Specific information on newly discovered proteins is often difficult to find in literature. Particularly if only sequences and no common names of proteins or genes are available, preceding sequence similarity searches can be crucial for the process of information collection. In drug research, it is important to know whether a small molecule targets only one specific protein or whether similar or homologous proteins are also influenced that may account for possible side effects. RESULTS: prolific (protein-literature investigation for interacting compounds) provides a one-step solution to investigate available information on given protein names, sequences, similar proteins or sequences on the gene level. Co-occurrences of UniProtKB/Swiss-Prot proteins and PubChem compounds in all PubMed abstracts are retrievable. Concise 'heat-maps' and tables display frequencies of co-occurrences. They provide links to processed literature with highlighted found protein and compound synonyms. Evaluation with manually curated drug-protein relationships showed that up to 69% could be discovered by automatic text-processing. Examples are presented to demonstrate the capabilities of prolific. AVAILABILITY: The web-application is available at http://prolific.pharmaceutical-bioinformatics.de and a web service at http://www.pharmaceutical-bioinformatics.de/prolific/soap/prolific.wsdl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Small-molecule conversion of toxic oligomers to nontoxic ß-sheet-rich amyloid fibrils.

Several lines of evidence indicate that prefibrillar assemblies of amyloid-ß (Aß) polypeptides, such as soluble oligomers or protofibrils, rather than mature, end-stage amyloid fibrils cause neuronal dysfunction and memory impairment in Alzheimer's disease. These findings suggest that reducing the prevalence of transient intermediates by small molecule-mediated stimulation of amyloid polymerization might decrease toxicity. Here we demonstrate the acceleration of Aß fibrillogenesis through the action of the orcein-related small molecule O4, which directly binds to hydrophobic amino acid residues in Aß peptides and stabilizes the self-assembly of seeding-competent, ß-sheet-rich protofibrils and fibrils. Notably, the O4-mediated acceleration of amyloid fibril formation efficiently decreases the concentration of small, toxic Aß oligomers in complex, heterogeneous aggregation reactions. In addition, O4 treatment suppresses inhibition of long-term potentiation by Aß oligomers in hippocampal brain slices. These results support the hypothesis that small, diffusible prefibrillar amyloid species rather than mature fibrillar aggregates are toxic for mammalian cells.

Compounds In Literature (CIL): screening for compounds and relatives in PubMed.

SUMMARY: Searching for certain compounds in literature can be an elaborate task, with many compounds having several different synonyms. Often, only the structure is known but not its name. Furthermore, rarely investigated compounds may not be described in the available literature at all. In such cases, preceding searches for described similar compounds facilitate literature mining. Highlighted names of proteins in selected texts may further accelerate the time-consuming process of literary research. Compounds In Literature (CIL) provides a web interface to automatically find names, structures, and similar structures in over 28 million compounds of PubChem and more than 18 million citations provided by the PubMed service. CIL's pre-calculated database contains more than 56 million parent compound-abstract relations. Found compounds, relatives and abstracts are related to proteins in a concise 'heat map'-like overview. Compounds and proteins are highlighted in their respective abstracts, and are provided with links to PubChem and UniProt. AVAILABILITY: An easy-to-use web interface with detailed descriptions, help and statistics is available from http://cil.pharmaceutical-bioinformatics.de. CONTACT: stefan.guenther@pharmazie.uni-freiburg.de.