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Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.

The ERA5 meteorological reanalysis dataset, from the European Centre for Medium-Range Weather Forecasts (ECMWF), is widely used in areas such as meteorology, hydrology and land-surface modelling. The Copernicus Climate Data Store (CDS) offers two options for accessing the data: a web interface and a Python API. However, automated downloading of the data requires advanced knowledge of Python, and can prove challenging to people less familiar with programming.Many climate scientists have their own Python scripts to download data from the CDS, all responsible for their own creation and maintenance. A quick search for Python scripts that call the CDS API on GitHub yields 1802 results, and this is not even counting scripts stored privately. However, these are by and large not reusable. A few years ago we created era5cli, as a byproduct of a project we were working on, to try to break this pattern of single-use scripts. era5cli enables automated downloading of ERA5 data using a single command.It is inefficient that everyone writes their own copy of the same, or at least similar code. That why we asked ourselves whether era5cli is still filling a niche and if so, what we could do to make it easier to re-use for others. In this presentation we give an overview of our recent efforts into turning era5cli from a utility script into a reusable software package.Despite the relatively small size of era5cli, around 1000 lines of Python code and comments, maintenance is not trivial. Changes are occasionally made to ERA5 and the CDS, and new Python versions are released while old ones are deprecated. Users of era5cli have helped here by submitting fixes to issues they have found in a Github pull request, but still require guidance and/or approval of administrators. By reducing the maintenance load of era5cli, through targeted streamlining of the code and a clean-up of the repository, as well as adding to the developer instructions in the documentation, we lower the threshold for community contributions and successful future maintenance. With this, we aim to make era5cli future-proof.era5cli can be installed using Python’s pip, as well as using conda/mamba (conda install era5cli -c conda-forge). The source code for era5cli is available on https://github.com/eWaterCycle/era5cli, and the documentation can be found on https://era5cli.readthedocs.io/.

Recently there’s been a lot of enthousiasm for the concepts of digital twins, virtual research environments, serious games, and other inspiring ideas to improve “the way we do science.” With eWaterCycle, we are no stranger to the cause. We’ve worked hard to build a platform that could make the scientific process – specifically, hydrological modelling – more accessible and engaging. eWaterCycle gives users access to a centralized platform where they can perform hydrological experiments: simulating how water flows through a catchment area of choice. Complete with data, a suite of models, an interactive scripting environment, and a graphical explorer to quickly setup an experiment. It shares many characteristics with what is commonly understood of a digital twin. But is it, really? Sadly, the concept of digital twins suffers from linguistic inflation. At a recent event on the topic, the main coffee chatter was along the lines of “but what actually is it?” In an arena filled with resonating buzz, a clear image can help to regain focus and a common frame of reference. Is eWaterCycle, as a platform that supports working with each other’s models and data, a digital twin? Or is it more an incubator of digital twins? Either way, eWaterCycle can help make things concrete and specific, because it already exists. With several new projects promising to build digital twins of all sorts, we hope our experience can feed into the discussions on and development of new digital twins. Therefore, at EGU, we would like to reflect on the essence of our platform and our experience in building it. What is it (not)? What’s in it for you? What challenges did we face? And what does that mean for open science and collaborative research?

The eWaterCycle platform introduced in 2022 (https://doi.org/10.5194/gmd-15-5371-2022) provides hydrologists with an online platform to conduct numerical studies involving hydrological models. It allows hydrologists to work with each other's data and datasets directly from a webbrowser. The workflow of the experiment done is clearly visible, reproducible and easily adaptable because of how eWaterCycle separates the model (the algorithm) used from the experiment done with the model. eWaterCycle is designed such that research conducted on the platform is ‘FAIR by design’. Using eWaterCycle, studies can be done in less time, more transparently and by more junior members of the hydrological community than was possible a few years ago. In this presentation, we will explain the capabilities of the eWaterCycle platform and show them by describing recently (published) works of MSc and PhD members of our team, including a model coupling study, a large sample hydrology study and a climate impact assessment study.

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. It allows scientists to set up experiments in a standardized way and run them interactively in a Jupyter environment. Previously we have presented various components that constitute the system: a preprocessing pipeline using ESMValTool (https://www.esmvaltool.org/) to generate meteorological forcing data, containerized models implementing the Basic Model Interface (https://bmi.readthedocs.io/), gRPC4BMI (https://github.com/eWaterCycle/grpc4bmi) to communicate with these containers from a Python environment, a visual explorer that lets the user set up an experiment with a few clicks and automatically generates a notebook based on the selected settings, and utilities to work with observations and analyse results. Recently we have officially released the eWaterCycle Python package (https://ewatercycle.readthedocs.io/en/latest/) that connects these components to provide a simple and clean user interface. The core of the package is modelled after PyMT (https://pymt.readthedocs.io/en/latest/index.html), extended with convenience functions to make the interface more user friendly, e.g. using xarray (http://xarray.pydata.org/en/stable/index.html) for spatial data and providing more user-friendly time accessors. Separate modules are available to load forcing data and parameter sets from the system and configure them correctly for the target model. The package comes with comprehensive documentation, including a suite of example notebooks. It also includes setup instructions for system administrators and guidance for incorporating new models. Currently, the following models are available through the eWaterCycle system: wflow, lisflood, marrmot m01, marrmot m14 and pcrglobwb. The release of the package marks a milestone in the development towards our goal of fully reproducible, open, and FAIR Hydrological modelling.

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source and ‘FAIR by Design’ Platform where hydrologists can do computational hydrological research using their own, or other’s, models and data. Using eWaterCycle, computational hydrologist can focus on the hydrological part of their work, without the headache that often comes with the computational part.In eWaterCycle experiments are separated from models: experiments are build and run in Jupyter notebooks and models can be accessed as objects in these notebooks. The models themselves are ‘hidden’ in (Docker) containers and accessed through an easy interface. This interface and the technology behind it that we’ve build allows computational hydrologists to work with (each others) models written in different programming languages without having to access that code. Currently PCRGlobWB 2.0, Hype, LISFlood, WFLOW and MARMoT are among the models supported by eWaterCycle.Furthermore, pre-processing of atmospheric forcing data is handled transparently by ESMValTool, which separates the process of selecting and standardising variables from the steps needed to make forcing compatible with a specific model. If a source of forcing data has been made ready for one model in eWaterCycle it is easy to use it for any other. If a model has been used with one forcing data source, it is easy to swap it with another. Currently ERA5 and ERA-Interim are supported in eWaterCycle.With eWaterCycle use cases such as (but not limited to!) these are now easier to implement:Comparing two models for the same region against observation data (GRDC discharge observations are standard supported) to determine which model performs best for a given research question Coupling two models (in different programming languages) to exchange information at every timestep, for example making a Running a multi-model ensemble (including adding data assimilation of observations) Previously we have announced eWaterCycle as work in progress. At the 2022 General Assembly we will demonstrate the release of v1.0 of the eWaterCycle platform, giving the computational hydrological community access to a platform that supports fully reproducible, open, and FAIR Hydrological modelling.This work is currently under open review for publication in GMD: https://gmd.copernicus.org/preprints/gmd-2021-344/ and parts of this work have been presented at the AGU 2021 Fall Meeting.

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data. To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/). Using ESMValTool, the eWaterCycle platform can perform commonly required preprocessing steps such as cropping, re-gridding, and variable derivation in a standardized manner. If needed, it also allows for custom steps for a hydrological model. Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models.Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/). The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer.The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling. Ultimately, we hope to make it possible to fully reproduce a hydrological model experiment from data pre-processing to analysis, using only a few clicks.

Hydrological models exhibit great complexity and diversity in the exact methodologies applied, competing for hypotheses of hydrologic behaviour, technology stacks, and programming languages used in those models. The preprocessing of forcing (meteorological) data is often performed by various sets of scripts that may or may not be included with model source codes, making it hard to reproduce results. Moreover, forcing data can be retrieved from a wide variety of forcing products with discrepant variable names and frequencies, spatial and temporal resolutions, and spatial coverage. Even though there is an infinite amount of preprocessing scripts for different models, these preprocessing scripts use only a limited set of operations, mainly re-gridding, temporal and spatial manipulations, variable derivation, and unit conversion. Also, these exact same preprocessing functions are used in analysis and evaluation of output from Earth system models in climate science.Within the context of the eWaterCycle II project (https://www.ewatercycle.org/), a common preprocessing system has been created for hydrological modelling based on ESMValTool (Earth System Model Evaluation Tool). ESMValTool is a community-driven diagnostic and performance metrics tool that supports a broad range of preprocessing functions. Using a YAML script called a recipe, instructions are provided to ESMValTool: the datasets which need to be analyzed, the preprocessors that need to be applied, and the model-specific analysis (i.e. diagnostic script) which need to be run on data. ESMValTool is modular and flexible so all preprocessing functions can also be used directly in a Python script and additional analyses can easily be added.The current preprocessing pipeline of the eWaterCycle using ESMValTool consists of hydrological model-specific scripts and supports ERA5 and ERA-Interim data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts), as well as CMIP5 and CMIP6 climate model data. The pipeline starts with the downloading and CMORization (Climate Model Output Rewriter) of input data. Then a recipe is prepared to find the data and run the preprocessors. When ESMValTool runs a recipe, it produces preprocessed data that can be passed as input to a hydrological model. It will also store provenance and citation information to ensure transparency and reproducibility. This leads to less time spent on building custom preprocessing, more reproducible and comparable hydrological science.In this presentation, we will give an overview of the current preprocessing pipeline of the eWaterCycle, outline ESMValTool preprocessing functions, and introduce available hydrological recipes and diagnostic scripts for the PCRGLOB, WFLOW, HYPE, MARRMOT and LISFLOOD models.

International audience; Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

eWaterCycle is a framework in which hydrological modelers can work together in a collaborative environment. In this environment, they can, for example, compare and analyze the results of models that use different sources of (meteorological) forcing data. The final goal of eWaterCycle is to advance the state of FAIR (Findable, Accessible, Interoperable, and Reusable) and open science in hydrological modeling.Comparing hydrological models has always been a challenging task. Hydrological models exhibit great complexity and diversity in the exact methodologies applied, competing for hypotheses of hydrologic behavior, technology stacks, and programming languages used in those models. Pre-processing of forcing data is one of the roadblocks that was identified during the FAIR Hydrological Modelling workshop organized by the Lorentz Center in April 2019. Forcing data can be retrieved from a wide variety of sources with discrepant variable names and frequencies, and spatial and temporal resolutions. Moreover, some hydrological models make specific assumptions about the definition of the forcing variables. The pre-processing is often performed by various sets of scripts that may or may not be included with model source codes, making it hard to reproduce results. Generally, there are common steps in the data preparation among different models. Therefore, it would be a valuable asset to the hydrological community if the pre-processing of FAIR input data could also be done in a FAIR manner.Within the context of the eWaterCycle II project, a common pre-processing system has been created for hydrological modeling based on ESMValTool (Earth System Model Evaluation Tool). ESMValTool is a community diagnostic and performance metrics tool developed for the evaluation of Earth system models. The ESMValTool pre-processing functions cover a broad range of operations on data before diagnostics or metrics are applied; for example, vertical interpolation, land-sea masking, re-gridding, multi-model statistics, temporal and spatial manipulations, variable derivation and unit conversion. The pre-processor performs these operations in a centralized, documented and efficient way. The current pre-processing pipeline of the eWaterCycle using ESMValTool consists of hydrological model-specific recipes and supports ERA5 and ERA-Interim data provided by the ECMWF (European Centre for Medium-Range Weather Forecasts). The pipeline starts with the downloading and CMORization (Climate Model Output Rewriter) of input data. Then a recipe is prepared to find the data and run the preprocessors. When ESMValTool runs a recipe, it will also run the diagnostic script that contains model-specific analysis to derive required forcing variables, and it will store provenance information to ensure transparency and reproducibility. In the near future, the pipeline is extended to include Earth observation data, as these data are paramount to the data assimilation in eWaterCycle.In this presentation we will show how using the pre-processor from ESMValTool for Hydrological modeling leads to connecting Hydrology and Climate sciences, and increase the impact and sustainability of ESMValTool.

The eWaterCycle platform is a fully Open-Source platform built specifically to advance the state of FAIR and Open Science in hydrological Modeling. eWaterCycle builds on web technology, notebooks and containers to offer an integrated modeling experimentation environment for scientists. It allows scientists to run any supported hydrological model with ease, including setup and pre-processing of all data required. Common datasets such as ERA-Interim and ERA-5 forcing data and observations for verification of model output quality are available for usage by the models, and a Jupyter based interface is available for ease of use.As the main API for models, we use the Basic Model Interface (BMI). This allows us to support models in a multitude of languages. Our gRPC based system allows coupling of models, and running of multiple instances of the same model. Our system was designed to work with higher level interfaces such as PyMT, and we are currently integrating PyMT into our platform. During my talk I will give an overview of the different elements of the eWaterCycle platform.The BMI interface was specifically designed to make it easy to implement in any given model. During the FAIR Hydrological Modeling workshop a number of modelers worked on creating a BMI interface for their models, and making them available in the eWaterCycle system. To show the amount of effort required in common cases, I will show the BMI interface that was created for a number of these models, including SUMMA, HYPE, Marrmot, TopoFlex, LisFlood, WFLOW, and PCR-GLOBWB.

The eWaterCycle platform is a fully Open-Source platform built specifically to advance the state of FAIR and Open Science in Hydrological Modeling.eWaterCycle builds on web technology, notebooks and containers to offer an integrated modelling experimentation environment for scientists. It allows scientists to run any supported hydrological model with ease, including setup and preprocessing of all data required. eWaterCycle comes with an easy to use explorer, so the user can get started with the system in minutes, and uniquely lets the user generate a hydrological model notebook based on their preferences.The eWaterCycle platform uses Jupyter as the main interface for scientific work to ensure maximum flexibility. Common datasets such as ERA-Interim and ERA-5 forcing data and observations for verification of model output quality are available for usage by the models.To make the system capable of running any hydrological model we use docker containers coupled through gRPC. This allows us to support models in a multitude of languages, and provide fully reproducible model experiments.Based on experiences during a FAIR Hydrological Modeling workshop in Leiden in April 2019 we have created a common pre-processing system for Hydrological modeling, based on technology from the climate sciences, in particular ESMValTool and Iris. This pre-processing pipeline can create input for a number of Hydrological models directly from the source dataset such as ERA-Interim in a fully transparent and reproducible manner.During this pico presentation, we will explain how this platform supports creating reproducible results in an easy to use fashion.

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds., Author summary Most metabolomics analyses rely upon matching observed fragmentation mass spectra to library spectra for structural annotation or compare spectra with each other through network analysis. As a key part of such processes, scoring functions are used to assess the similarity between pairs of fragment spectra. No studies have so far proposed scores fundamentally different to the popular cosine-based similarity score, despite the fact that its limitations are well understood. We propose a novel spectral similarity score known as Spec2Vec which adapts algorithms from natural language processing to learn relationships between peaks from co-occurrences across large spectra datasets. We find that similarities computed with Spec2Vec i) correlate better to structural similarity than cosine-based scores, ii) subsequently gives better performance in library matching tasks, and iii) is computationally more scalable than cosine-based scores. Given the central place of similarity scoring in key metabolomics analysis tasks such as library matching and spectral networking, we expect Spec2Vec to make a broad impact in all fields that rely upon untargeted metabolomics.

SummaryMass spectrometry data is at the heart of numerable applications in the biomedical and life sciences. With growing use of high throughput techniques researchers need to analyse larger and more complex datasets. In particular through joint effort in the research community, fragmentation mass spectrometry datasets are growing in size and number. Platforms such as MassBank (Horai et al. 2010), GNPS (Wang et al. 2016) or MetaboLights (Haug et al. 2020) serve as an open-access hub for sharing of raw, processed, or annotated fragmentation mass spectrometry data (MS/MS). Without suitable tools, however, exploitation of such datasets remains overly challenging. In particular, large collected datasets contain data aquired using different instruments and measurement conditions, and can further contain a significant fraction of inconsistent, wrongly labeled, or incorrect metadata (annotations).

Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present sv-callers, a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook. This workflow supports easy deployment of software dependencies, configuration and addition of new analysis tools. Moreover, porting it to different computing systems requires minimal effort. Finally, we demonstrate the utility of the workflow by performing both somatic and germline SV analyses on different high-performance computing systems.

In this lightning talk we will describe the Research Software Directory; a content management system that is tailored to research software with the goal of enabling a qualitative assessment of software impact and improving software findability. &nbsp

Contains fulltext : 190629.pdf (Publisher’s version ) (Open Access) eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.

The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed. The use of these numbering schemes has facilitated many chemogenomics studies such as consensus binding site definition, binding site comparison, ligand repurposing (e.g. for orphan receptors), sequence-based pharmacophore generation for homology modeling or virtual screening, and class-wide chemogenomics studies of GPCRs.

Poster presented at the Analytical Tools for Cutting-edge Metabolomics meeting in London, 30 April 2014

Contains fulltext : 169642.pdf (Publisher’s version ) (Open Access) 3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.org/magma ) to automatically process and annotate the LC-MS(n) data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MS(n) spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MS(n) (n ≤ 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC-MS(n) run, a total of 116,240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC-MS(n) component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.

Contains fulltext : 162133pub.pdf (Publisher’s version ) (Open Access) This article discusses the use of 3D technologies in digital earth applications (DEAs) to study complex sites. These are large areas containing objects with heterogeneous shapes and semantic information. The study proposes that DEAs should be modular, have multi-tier architectures, and be developed as Free and Open Source Software if possible. In DEAs requiring high reliability in the 3D measurements, point clouds are proposed as basis for the 3D Digital digital earth representation. For the development of DEAs, we propose to follow a workflow with four components: data acquisition and processing, data management, data analysis and data visualization. For every component, technological challenges of using 3D technologies are identified and solutions applied for a case study are presented. The case study is a modular 3D DEA developed for the archaeological project Mapping the Via Appia. The 3D DEA allows archaeologists to virtually analyze a complex study area. 12 december 2016 18 p.

RATIONALE High-resolution multistage MSn data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation. METHODS We present an extended substructure-based approach which allows annotation of hierarchical spectral trees obtained from high-resolution multistage MSn experiments. The algorithm yields a hierarchical tree of substructures of a candidate molecule to explain the fragment peaks observed at consecutive levels of the multistage MSn spectral tree. A matching score is calculated that indicates how well the candidate structure can explain the observed hierarchical fragmentation pattern. RESULTS The method is applied to MSn spectral trees of a set of compounds representing important chemical classes in metabolomics. Based on the calculated score, the correct molecules were successfully prioritized among extensive sets of candidates structures retrieved from the PubChem database. CONCLUSIONS The results indicate that the inclusion of subsequent levels of fragmentation in the automatic annotation of MSn data improves the identification of the correct compounds. We show that, especially in the case of lower mass accuracy, this improvement is not only due to the inclusion of additional fragment ions in the analysis, but also to the specific hierarchical information present in the MSn spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MSn data. Copyright (c)proves 2012 John Wiley & Sons, Ltd.

In preparation for the XENON1T Dark Matter data acquisition, we have prototyped and implemented a new computing model. The XENON signal and data processing software is developed fully in Python 3, and makes extensive use of generic scientific data analysis libraries, such as the SciPy stack. A certain tension between modern "Big Data" solutions and existing HEP frameworks is typically experienced in smaller particle physics experiments. ROOT is still the "standard" data format in our field, defined by large experiments (ATLAS, CMS). To ease the transition, our computing model caters to both analysis paradigms, leaving the choice of using ROOT-specific C++ libraries, or alternatively, Python and its data analytics tools, as a front-end choice of developing physics algorithms. We present our path on harmonizing these two ecosystems, which allowed us to use off-the-shelf software libraries (e.g., NumPy, SciPy, scikit-learn, matplotlib) and lower the cost of development and maintenance. To analyse the data, our software allows researchers to easily create "mini-trees"; small, tabular ROOT structures for Python analysis, which can be read directly into pandas DataFrame structures. One of our goals was making ROOT available as a cross-platform binary for an easy installation from the Anaconda Cloud (without going through the "dependency hell"). In addition to helping us discover dark matter interactions, lowering this barrier helps shift the particle physics toward non-domain-specific code.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed.

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27,245 potential metabolites. All matching precursor ions in the urine LC-MS(n) data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC-MS(n) data from nutritional metabolite profiling experiments.

High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation.We present an extended substructure-based approach which allows annotation of hierarchical spectral trees obtained from high-resolution multistage MS(n) experiments. The algorithm yields a hierarchical tree of substructures of a candidate molecule to explain the fragment peaks observed at consecutive levels of the multistage MS(n) spectral tree. A matching score is calculated that indicates how well the candidate structure can explain the observed hierarchical fragmentation pattern.The method is applied to MS(n) spectral trees of a set of compounds representing important chemical classes in metabolomics. Based on the calculated score, the correct molecules were successfully prioritized among extensive sets of candidates structures retrieved from the PubChem database.The results indicate that the inclusion of subsequent levels of fragmentation in the automatic annotation of MS(n) data improves the identification of the correct compounds. We show that, especially in the case of lower mass accuracy, this improvement is not only due to the inclusion of additional fragment ions in the analysis, but also to the specific hierarchical information present in the MS(n) spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MS(n) data.

Contains fulltext : 103510.pdf (Publisher’s version ) (Closed access) We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the beta-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010. © 2011 American Chemical Society.

In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org. © 2011 The Author(s).

Introduction: To reduce continuously increasing costs in drug development, adverse effects of drugs need to be detected as early as possible in the process. In recent years, compound-induced gene expression profiling methodologies have been developed to assess compound toxicity, including Gene Ontology term and pathway over-representation analyses. The objective of this study was to introduce an additional approach, in which literature information is used for compound profiling to evaluate compound toxicity and mode of toxicity. Methods: Gene annotations were built by text mining in Medline abstracts for retrieval of co-publications between genes, pathology terms, biological processes and pathways. This literature information was used to generate compound-specific keyword fingerprints, representing over-represented keywords calculated in a set of regulated genes after compound administration. To see whether keyword fingerprints can be used for assessment of compound toxicity, we analyzed microarray data sets of rat liver treated with 11 hepatotoxicants. Results: Analysis of keyword fingerprints of two genotoxic carcinogens, two nongenotoxic carcinogens, two peroxisome proliferators and two randomly generated gene sets, showed that each compound produced a specific keyword fingerprint that correlated with the experimentally observed histopathological events induced by the individual compounds. By contrast, the random sets produced a flat aspecific keyword profile, indicating that the fingerprints induced by the compounds reflect biological events rather than random noise. A more detailed analysis of the keyword profiles of diethylhexylphthalate, dimethylnitrosamine and methapyrilene (MPy) showed that the differences in the keyword fingerprints of these three compounds are based upon known distinct modes of action. Visualization of MPy-linked keywords and MPy-induced genes in a literature network enabled us to construct a mode of toxicity proposal for MPy, which is in agreement with known effects of MPy in literature. Conclusion: Compound keyword fingerprinting based on information retrieved from literature is a powerful approach for compound profiling, allowing evaluation of compound toxicity and analysis of the mode of action. In the drug development process it is impor- tant that any toxicity of new compounds is rec- ognized as early as possible to guarantee drug safety and to reduce the costs of drug develop- ment (1). Classical methods of toxicity evalua- tion of compounds involve elaborate and time- consuming animal experiments in which a number of measurements are carried out to evaluate toxicity, including clinical chemistry

Background Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids. Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects. Results We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes. With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR. Conclusions With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.

Background G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs. Results Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method. Conclusions The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.