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                    "term": "Data identity and mapping"
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                    "metadata": {
                        "title": "AMICI: high-performance sensitivity analysis for large ordinary differential equation models",
                        "abstract": "Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.",
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                        "authors": [
                            {
                                "name": "Frohlich F."
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                            {
                                "name": "Weindl D."
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                            {
                                "name": "Schalte Y."
                            },
                            {
                                "name": "Pathirana D."
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                            {
                                "name": "Paszkowski L."
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                            {
                                "name": "Lines G.T."
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                            {
                                "name": "Stapor P."
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                            {
                                "name": "Hasenauer J."
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                        "journal": "Bioinformatics"
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            "name": "Galaxy Ecology",
            "description": "Galaxy Ecology is mainly developed by the French Data Terra research infrastructure Biodiversity data hub (PNDB). Its dedication is to provide tools for biodiversity data management and analysis for the Galaxy platform. Tools are useable through dedicated Galaxy instances hosted by Galaxy Europe (ecology.usegalaxy.eu) and Galaxy France (ecology.usegalaxy.fr).",
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                    "uri": "http://edamontology.org/topic_3855",
                    "term": "Environmental sciences"
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                    "term": "Ecology"
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                        "abstract": "Numerous conceptual frameworks exist for best practices in research data and analysis (e.g., Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as \"atoms\"composing an analytical procedure. When generalized, \"atoms\"can be used in more than a single case analysis. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomized and generalized.",
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                                "name": "Mihoub J.-B."
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                                "name": "Masse G."
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                            {
                                "name": "Martin A."
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                                "name": "Bas Y."
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                            {
                                "name": "Virgoulay T."
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                            {
                                "name": "Chambon V."
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                            {
                                "name": "Arnaud E."
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                            {
                                "name": "Michon E."
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                            {
                                "name": "Urfer C."
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                                "name": "Trigodet E."
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                            {
                                "name": "Lois G."
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            "description": "The main focus lays on the integrated inference algorithms, concluding the proteins from a set of identified spectra. But it also allows you to integrate results of various search engines, inspect your peptide spectrum matches, calculate FDR values across different results and visualize the correspondence between PSMs, peptides and proteins.",
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                            "term": "Protein identification"
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                            "uri": "http://edamontology.org/operation_3649",
                            "term": "Target-Decoy"
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                                "term": "Mass spectrometry spectra"
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                                    "term": "TSV"
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                        "title": "PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface",
                        "abstract": "Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results. PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.",
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                                "name": "Uszkoreit J."
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                                "name": "Perez-Riverol Y."
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                            {
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                        "title": "Protein Inference Using PIA Workflows and PSI Standard File Formats",
                        "abstract": "Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass-spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published data set for isoform detection. PIA is open source and available for download on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the community extensions inside the KNIME analytics platform.",
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                        "abstract": "Epimutations are rare alterations of the normal DNA methylation pattern at specific loci, which can lead to rare diseases. Methylation microarrays enable genome-wide epimutation detection, but technical limitations prevent their use in clinical settings: methods applied to rare diseases’ data cannot be easily incorporated to standard analyses pipelines, while epimutation methods implemented in R packages (ramr) have not been validated for rare diseases. We have developed epimutacions, a Bioconductor package (https://bioconductor.org/packages/release/bioc/html/epimutacions.html). epimutacions implements two previously reported methods and four new statistical approaches to detect epimutations, along with functions to annotate and visualize epimutations. Additionally, we have developed an user-friendly Shiny app to facilitate epimutations detection (https://github.com/isglobal-brge/epimutacionsShiny) to non-bioinformatician users. We first compared the performance of epimutacions and ramr packages using three public datasets with experimentally validated epimutations. Methods in epimutacions had a high performance at low sample sizes and outperformed methods in ramr. Second, we used two general population children cohorts (INMA and HELIX) to determine the technical and biological factors that affect epimutations detection, providing guidelines on how designing the experiments or preprocessing the data. In these cohorts, most epimutations did not correlate with detectable regional gene expression changes. Finally, we exemplified how epimutacions can be used in a clinical context. We run epimutacions in a cohort of children with autism disorder and identified novel recurrent epimutations in candidate genes for autism. Overall, we present epimutacions a new Bioconductor package for incorporating epimutations detection to rare disease diagnosis and provide guidelines for the design and data analyses.",
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                        "abstract": "© 2016 Nature America, Inc. All rights reserved.High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.",
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                        "title": "Bracken: Estimating species abundance in metagenomics data",
                        "abstract": "Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomalRNAgene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.",
                        "date": "2017-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Lu J."
                            },
                            {
                                "name": "Breitwieser F.P."
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                            {
                                "name": "Thielen P."
                            },
                            {
                                "name": "Salzberg S.L."
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                    "doi": "10.1093/bioinformatics/btp259",
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                    "metadata": {
                        "title": "Application and evaluation of automated semantic annotation of gene expression experiments",
                        "abstract": "Motivation: Many microarray datasets are available online with formalized standards describing the probe sequences and expression values. Unfortunately, the description, conditions and parameters of the experiments are less commonly formalized and often occur as natural language text. This hinders searching, high-throughput analysis, organization and integration of the datasets. Results: We use the lexical resources and software tools from the Unified Medical Language System (UMLS) to extract concepts from text. We then link the UMLS concepts to classes in open biomedical ontologies. The result is accessible and clear semantic annotations of gene expression experiments. We applied the method to 595 expression experiments from Gemma, a resource for re-use and meta-analysis of gene expression profiling data. We evaluated and corrected all stages of the annotation process. The majority of missed annotations were due to a lack of cross-references. The most error-prone stage was the extraction of concepts from phrases. Final review of the annotations in context of the experiments revealed 89% precision. A naive system, lacking the phrase to concept corrections is 68% precise. We have integrated this annotation pipeline into Gemma. © 2009 The Author(s).",
                        "date": "2009-06-09T00:00:00Z",
                        "citationCount": 9,
                        "authors": [
                            {
                                "name": "French L."
                            },
                            {
                                "name": "Lane S."
                            },
                            {
                                "name": "Law T."
                            },
                            {
                                "name": "Xu L."
                            },
                            {
                                "name": "Pavlidis P."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
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                {
                    "doi": "10.1093/database/baab006",
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                    "metadata": {
                        "title": "Curation of over 10 000 transcriptomic studies to enable data reuse",
                        "abstract": "Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html",
                        "date": "2021-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Lim N."
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                            {
                                "name": "Tesar S."
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                            {
                                "name": "Belmadani M."
                            },
                            {
                                "name": "Poirier-Morency G."
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                            {
                                "name": "Mancarci B.O."
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                            {
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                            {
                                "name": "Jacobson M."
                            },
                            {
                                "name": "Leong J."
                            },
                            {
                                "name": "Tan P."
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                            {
                                "name": "Pavlidis P."
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                        "journal": "Database"
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                                    "uri": "http://edamontology.org/format_3751",
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                    ],
                    "note": "Convert SGB-based profile to GTDB taxonomy",
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            ],
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                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
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                    "uri": "http://edamontology.org/topic_0194",
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            "publication": [
                {
                    "doi": "10.1038/nmeth.2066",
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                    "version": null,
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                    "metadata": {
                        "title": "Metagenomic microbial community profiling using unique clade-specific marker genes",
                        "abstract": "Metagenomic shotgun sequencing data can identify microbes populating a microbial community and their proportions, but existing taxonomic profiling methods are inefficient for increasingly large data sets. We present an approach that uses clade-specific marker genes to unambiguously assign reads to microbial clades more accurately and >50Ã-faster than current approaches. We validated our metagenomic phylogenetic analysis tool, MetaPhlAn, on terabases of short reads and provide the largest metagenomic profiling to date of the human gut. It can be accessed at http://huttenhower.sph.harvard.edu/ metaphlan/. © 2012 Nature America, Inc. All rights reserved.",
                        "date": "2012-08-01T00:00:00Z",
                        "citationCount": 1300,
                        "authors": [
                            {
                                "name": "Segata N."
                            },
                            {
                                "name": "Waldron L."
                            },
                            {
                                "name": "Ballarini A."
                            },
                            {
                                "name": "Narasimhan V."
                            },
                            {
                                "name": "Jousson O."
                            },
                            {
                                "name": "Huttenhower C."
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                        ],
                        "journal": "Nature Methods"
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                    "term": "Protein sites, features and motifs"
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                        "title": "Cleavage site analysis in picornaviral polyproteins: Discovering cellular targets by neural networks",
                        "abstract": "Picornaviral proteinases are responsible for maturation cleavages of the viral polyprotein, but also catalyze the degradation of cellular targets. Using graphical visualization techniques and neural network algorithms, we have investigated the sequence specificity of the two proteinases 2A(pro) and 3C(pro). The cleavage of VP0 (giving rise to VP2 and VP4), which is carried out by a so-far unknown proteinase, was also examined. In combination with a novel surface exposure prediction algorithm, our neural network approach successfully distinguishes known cleavage sites from noncleavage sites and yields a more consistent definition of features common to these sites. The method is able to predict experimentally determined cleavage sites in cellular proteins. We present a list of mammalian and other proteins that are predicted to be possible targets for the vital proteinases. Whether these proteins are indeed cleaved awaits experimental verification. Additionally, we report several errors detected in the protein databases.",
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                    "version": null
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                    "note": "B.C. Haller, P.W. Messer. (2019). SLiM 3: Forward genetic simulations beyond the Wright–Fisher Model. Molecular Biology and Evolution 36(3), 632–637.",
                    "metadata": {
                        "title": "SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model",
                        "abstract": "With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright-Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or \"nonWF\" model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.",
                        "date": "2019-03-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Haller B.C."
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                            {
                                "name": "Messer P.W."
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                    "metadata": {
                        "title": "Evolutionary Modeling in SLiM 3 for Beginners",
                        "abstract": "The SLiM forward genetic simulation framework has proved to be a powerful and flexible tool for population genetic modeling. However, as a complex piece of software with many features that allow simulating a diverse assortment of evolutionary models, its initial learning curve can be difficult. Here we provide a step-by-step demonstration of how to build a simple evolutionary model in SLiM 3, to help new users get started. We will begin with a panmictic neutral model, and build up to a model of the evolution of a polygenic quantitative trait under selection for an environmental phenotypic optimum.",
                        "date": "2019-05-01T00:00:00Z",
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                        "authors": [
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                                "name": "Haller B.C."
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                                "name": "Messer P.W."
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                    }
                },
                {
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                    "pmid": "30565882",
                    "pmcid": "PMC6393187",
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                    "note": "B.C. Haller, J. Galloway, J. Kelleher, P.W. Messer, P.L. Ralph. (2019). Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. Molecular Ecology Resources 19(2), 552–566.",
                    "metadata": {
                        "title": "Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes",
                        "abstract": "There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree-sequence recording method (Kelleher, Thornton, Ashander, & Ralph, 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using “recapitation”; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a population's genealogy that can be analysed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modelling and exploration of evolutionary processes.",
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                            {
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                            {
                                "name": "Messer P.W."
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                            {
                                "name": "Ralph P.L."
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                    "note": "B.C. Haller, P.W. Messer. (2023). SLiM 4: Multispecies eco-evolutionary modeling. The American Naturalist 201(5), E127–E139.",
                    "metadata": {
                        "title": "SLiM 4: Multispecies Eco-Evolutionary Modeling",
                        "abstract": "The SLiM software framework for genetically explicit forward simulation has been widely used in population genetics. However, it has been largely restricted to modeling only a single species, which has limited its broader utility in evolutionary biology. Indeed, to our knowledge no general-purpose, flexible modeling framework exists that provides support for simulating multiple species while also providing other key features, such as explicit genetics and continuous space. The lack of such software has limited our ability to model higher biological levels such as communities, eco-systems, coevolutionary and eco-evolutionary processes, and bio-diversity, which is crucial for many purposes, from extending our basic understanding of evolutionary ecology to informing conservation and management decisions. We here announce the release of SLiM 4, which fills this important gap by adding support for multiple species, including ecological interactions between species such as predation, parasitism, and mutualism, and illustrate its new features with examples.",
                        "date": "2023-05-01T00:00:00Z",
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                        "authors": [
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                                "name": "Haller B.C."
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                            {
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                        "title": "Molecular phylogenetics by direct comparison of tandem mass spectra",
                        "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.",
                        "date": "2012-04-15T00:00:00Z",
                        "citationCount": 30,
                        "authors": [
                            {
                                "name": "Palmblad M."
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                            {
                                "name": "Deelder A.M."
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                        "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets",
                        "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.",
                        "date": "2023-02-03T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Marissen R."
                            },
                            {
                                "name": "Varunjikar M.S."
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                            {
                                "name": "Laros J.F.J."
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                            {
                                "name": "Rasinger J.D."
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                            {
                                "name": "Neely B.A."
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                            {
                                "name": "Palmblad M."
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                        "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics",
                        "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.",
                        "date": "2021-10-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Neely B.A."
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                            {
                                "name": "Palmblad M."
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                    "metadata": {
                        "title": "The exposome and health: Where chemistry meets biology",
                        "abstract": "Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The “exposome” concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.",
                        "date": "2020-01-24T00:00:00Z",
                        "citationCount": 597,
                        "authors": [
                            {
                                "name": "Vermeulen R."
                            },
                            {
                                "name": "Schymanski E.L."
                            },
                            {
                                "name": "Barabasi A.-L."
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                            {
                                "name": "Miller G.W."
                            }
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                        "title": "Developing the building blocks to elucidate the impact of the urban exposome on cardiometabolic-pulmonary disease: The EU EXPANSE project",
                        "abstract": "By 2030, more than 80% of Europe's population will live in an urban environment. The urban exposome, consisting of factors such as where we live and work, where and what we eat, our social network, and what chemical and physical hazards we are exposed to, provides important targets to improve population health. The EXPANSE (EXposome Powered tools for healthy living in urbAN SEttings) project will study the impact of the urban exposome on the major contributors to Europe's burden of disease: Cardio-Metabolic and Pulmonary Disease. EXPANSE will address one of the most pertinent questions for urban planners, policy makers, and European citizens: \"How to maximize one's health in a modern urban environment?\" EXPANSE will take the next step in exposome research by (1) bringing together exposome and health data of more than 55 million adult Europeans and OMICS information for more than 2 million Europeans; (2) perform personalized exposome assessment for 5,000 individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution of the exposome and health through the life course; and (5) evaluating the impact of changes in the urban exposome on the burden of cardiometabolic and pulmonary disease. EXPANSE will translate its insights and innovations into research and dissemination tools that will be openly accessible via the EXPANSE toolbox. By applying innovative ethics-by-design throughout the project, the social and ethical acceptability of these tools will be safeguarded. EXPANSE is part of the European Human Exposome Network.",
                        "date": "2021-08-01T00:00:00Z",
                        "citationCount": 38,
                        "authors": [
                            {
                                "name": "Vlaanderen J."
                            },
                            {
                                "name": "De Hoogh K."
                            },
                            {
                                "name": "Hoek G."
                            },
                            {
                                "name": "Peters A."
                            },
                            {
                                "name": "Probst-Hensch N."
                            },
                            {
                                "name": "Scalbert A."
                            },
                            {
                                "name": "Melen E."
                            },
                            {
                                "name": "Tonne C."
                            },
                            {
                                "name": "De Wit G.A."
                            },
                            {
                                "name": "Chadeau-Hyam M."
                            },
                            {
                                "name": "Katsouyanni K."
                            },
                            {
                                "name": "Esko T."
                            },
                            {
                                "name": "Jongsma K.R."
                            },
                            {
                                "name": "Vermeulen R."
                            }
                        ],
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                    "metadata": {
                        "title": "MINERVA—A platform for visualization and curation of molecular interaction networks",
                        "abstract": "Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.",
                        "date": "2016-01-01T00:00:00Z",
                        "citationCount": 60,
                        "authors": [
                            {
                                "name": "Gawron P."
                            },
                            {
                                "name": "Ostaszewski M."
                            },
                            {
                                "name": "Satagopam V."
                            },
                            {
                                "name": "Gebel S."
                            },
                            {
                                "name": "Mazein A."
                            },
                            {
                                "name": "Kuzma M."
                            },
                            {
                                "name": "Zorzan S."
                            },
                            {
                                "name": "McGee F."
                            },
                            {
                                "name": "Otjacques B."
                            },
                            {
                                "name": "Balling R."
                            },
                            {
                                "name": "Schneider R."
                            }
                        ],
                        "journal": "npj Systems Biology and Applications"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btz286",
                    "pmid": "31074494",
                    "pmcid": "PMC6821317",
                    "type": [
                        "Primary"
                    ],
                    "version": "12.2.3",
                    "note": null,
                    "metadata": {
                        "title": "MINERVA API and plugins: Opening molecular network analysis and visualization to the community",
                        "abstract": "Summary: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform's visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. Availability and implementation: Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.",
                        "date": "2019-11-01T00:00:00Z",
                        "citationCount": 24,
                        "authors": [
                            {
                                "name": "Hoksza D."
                            },
                            {
                                "name": "Gawron P."
                            },
                            {
                                "name": "Ostaszewski M."
                            },
                            {
                                "name": "Smula E."
                            },
                            {
                                "name": "Schneider R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/bib/bbz067",
                    "pmid": "31273380",
                    "pmcid": "PMC7373180",
                    "type": [
                        "Primary"
                    ],
                    "version": "13.1.1",
                    "note": null,
                    "metadata": {
                        "title": "Closing the gap between formats for storing layout information in systems biology",
                        "abstract": "The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.",
                        "date": "2019-07-10T00:00:00Z",
                        "citationCount": 15,
                        "authors": [
                            {
                                "name": "Hoksza D."
                            },
                            {
                                "name": "Gawron P."
                            },
                            {
                                "name": "Ostaszewski M."
                            },
                            {
                                "name": "Hasenauer J."
                            },
                            {
                                "name": "Schneider R."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                },
                {
                    "doi": "10.1089/big.2015.0057",
                    "pmid": "27441714",
                    "pmcid": "PMC4932659",
                    "type": [
                        "Usage"
                    ],
                    "version": "10.0",
                    "note": null,
                    "metadata": {
                        "title": "Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases",
                        "abstract": "Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services - tranSMART, a Galaxy Server, and a MINERVA platform - are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.",
                        "date": "2016-06-01T00:00:00Z",
                        "citationCount": 38,
                        "authors": [
                            {
                                "name": "Satagopam V."
                            },
                            {
                                "name": "Gu W."
                            },
                            {
                                "name": "Eifes S."
                            },
                            {
                                "name": "Gawron P."
                            },
                            {
                                "name": "Ostaszewski M."
                            },
                            {
                                "name": "Gebel S."
                            },
                            {
                                "name": "Barbosa-Silva A."
                            },
                            {
                                "name": "Balling R."
                            },
                            {
                                "name": "Schneider R."
                            }
                        ],
                        "journal": "Big Data"
                    }
                },
                {
                    "doi": "10.1016/j.envpol.2019.04.005",
                    "pmid": "30991279",
                    "pmcid": null,
                    "type": [],
                    "version": "13.1.1",
                    "note": null,
                    "metadata": {
                        "title": "Genes associated with Parkinson's disease respond to increasing polychlorinated biphenyl levels in the blood of healthy females",
                        "abstract": "Polychlorinated biphenyls (PCBs) are a class of widespread environmental pollutants, commonly found in human blood, that have been suggested to be linked to the occurrence of sporadic Parkinson's disease (PD). It has been reported that some non-coplanar PCBs accumulate in the brains of female PD patients. To improve our understanding of the association between PCB exposure and PD risk we have applied whole transcriptome gene expression analysis in blood cells from 594 PCB-exposed subjects (369 female, 225 male). Interestingly, we observe that in females, blood levels of non-coplanar PCBs appear to be associated with expression levels of PD-specific genes. However, no such association was detected in males. Among the 131 PD-specific genes affected, 39 have been shown to display similar changes in expression levels in the substantia nigra of deceased PD patients. Especially among the down-regulated genes, transcripts of genes involved in neurotransmitter vesicle-related functions were predominant. Capsule: Plasma PCB levels are associated with gene expression changes in females only, resulting in brain-related genes changing in blood cells of healthy individuals exposed to PCBs.",
                        "date": "2019-07-01T00:00:00Z",
                        "citationCount": 5,
                        "authors": [
                            {
                                "name": "Bohler S."
                            },
                            {
                                "name": "Krauskopf J."
                            },
                            {
                                "name": "Espin-Perez A."
                            },
                            {
                                "name": "Gebel S."
                            },
                            {
                                "name": "Palli D."
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                            {
                                "name": "Rantakokko P."
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                            {
                                "name": "Kiviranta H."
                            },
                            {
                                "name": "Kyrtopoulos S.A."
                            },
                            {
                                "name": "Balling R."
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                            {
                                "name": "Kleinjans J."
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                        ],
                        "journal": "Environmental Pollution"
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                }
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                            {
                                "name": "Guillou L."
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                            {
                                "name": "Bachar D."
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                            {
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                            {
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                            {
                                "name": "De Vargas C."
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                            {
                                "name": "Decelle J."
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                            {
                                "name": "Del Campo J."
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                            {
                                "name": "Dolan J.R."
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                            {
                                "name": "Dunthorn M."
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                            {
                                "name": "Edvardsen B."
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                            {
                                "name": "Holzmann M."
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                                "name": "Kooistra W.H.C.F."
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                                "name": "Lara E."
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                            {
                                "name": "Le Bescot N."
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                                "name": "Logares R."
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                            {
                                "name": "Mahe F."
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                            {
                                "name": "Massana R."
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                            {
                                "name": "Montresor M."
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                            {
                                "name": "Morard R."
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                            {
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                            {
                                "name": "Pawlowski J."
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                            {
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                                "name": "Sauvadet A.-L."
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                            {
                                "name": "Stoeck T."
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                            {
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                            {
                                "name": "Zimmermann P."
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                            {
                                "name": "Christen R."
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                    ],
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                {
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                    "uri": "http://edamontology.org/topic_0621",
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            ],
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                    "doi": "10.1093/NAR/GKX1002",
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                    "metadata": {
                        "title": "PLAZA 4.0: An integrative resource for functional, evolutionary and comparative plant genomics",
                        "abstract": "PLAZA (https://bioinformatics.psb.ugent.be/plaza) is a plant-oriented online resource for comparative, evolutionary and functional genomics. The PLAZA platform consists of multiple independent instances focusing on different plant clades, while also providing access to a consistent set of reference species. Each PLAZA instance contains structural and functional gene annotations, gene family data and phylogenetic trees and detailed gene colinearity information. A user-friendly web interface makes the necessary tools and visualizations accessible, specific for each data type. Here we present PLAZA 4.0, the latest iteration of the PLAZA framework. This version consists of two new instances (Dicots 4.0 and Monocots 4.0) providing a large increase in newly available species, and offers access to updated and newly implemented tools and visualizations, helping users with the ever-increasing demands for complex and in-depth analyzes. The total number of species across both instances nearly doubles from 37 species in PLAZA 3.0 to 71 species in PLAZA 4.0, with a much broader coverage of crop species (e.g. wheat, palm oil) and species of evolutionary interest (e.g. spruce, Marchantia). The new PLAZA instances can also be accessed by a programming interface through a RESTful web service, thus allowing bioinformaticians to optimally leverage the power of the PLAZA platform.",
                        "date": "2018-01-01T00:00:00Z",
                        "citationCount": 338,
                        "authors": [
                            {
                                "name": "Van Bel M."
                            },
                            {
                                "name": "Diels T."
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                            {
                                "name": "Vancaester E."
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                            {
                                "name": "Kreft L."
                            },
                            {
                                "name": "Botzki A."
                            },
                            {
                                "name": "Van De Peer Y."
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                            {
                                "name": "Coppens F."
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                            {
                                "name": "Vandepoele K."
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                        ],
                        "journal": "Nucleic Acids Research"
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                }
            ],
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        },
        {
            "name": "CellBinDB",
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