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            "name": "SLiM",
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                            "term": "Sequence generation"
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                            "term": "Ecological modelling"
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                    "input": [],
                    "output": [],
                    "note": "Run individual-based eco-evolutionary simulations with explicit genetics",
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                "Desktop application"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0610",
                    "term": "Ecology"
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                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
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                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
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                    "uri": "http://edamontology.org/topic_3299",
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                    "url": "https://messerlab.org/slim/",
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                    "note": "SLiM home page in the Messer Lab website"
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                    "note": "GitHub repository for SLiM"
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                        "Discussion forum"
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                    "note": "Discussion forum for SLiM questions"
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                    "url": "http://benhaller.com/slim/SLiM.zip",
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                    "note": "A source archive for the command-line `slim` tool only.  Complete source code is on GitHub, but most platforms have an installer anyway; see the manual, chapter 2, for installation instructions.",
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                    "note": "The manual for Eidos, the scripting language used by SLiM"
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                    "doi": "10.1093/molbev/msy228",
                    "pmid": 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.",
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                        "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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                    "doi": "10.1093/molbev/msy237",
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                    "type": [
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                    "version": null,
                    "note": "B.C. Haller, P.W. Messer. (2019). Evolutionary modeling in SLiM 3 for beginners. Molecular Biology and Evolution 36(5), 1101–1109.",
                    "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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                    "version": null,
                    "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.",
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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.",
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                        "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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                                "name": "Messer P.W."
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                        "title": "Uncovering the molecular machinery of the human spindle-an integration of wet and dry systems biology",
                        "abstract": "The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called \"hidden spindle hub\", proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system. © 2012 Rojas et al.",
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                        "authors": [
                            {
                                "name": "Rojas A.M."
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                            {
                                "name": "Santamaria A."
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                                "name": "Malik R."
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                            {
                                "name": "Jensen T.S."
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                            {
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                            {
                                "name": "Morilla I."
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                            {
                                "name": "de Juan D."
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                                "name": "Yeats C."
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                            {
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                            {
                                "name": "Elowe S."
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                                "name": "Brunak S."
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                            {
                                "name": "Orengo C."
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                                "name": "Ranea J.A.G."
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            "name": "NCBI Mass Sequence Downloader",
            "description": "This program will download sequences en masse from several NCBI databases (at the user's choice). After the downloading is finished, the program will check the resulting file for any missing sequences and continuously retry the download until all sequences are present in the local file. NCBI Data Usage Policies and Disclaimers may apply to downloaded data.",
            "homepage": "https://github.com/StuntsPT/NCBI_Mass_Downloader",
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                            "uri": "http://edamontology.org/operation_2422",
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                    ],
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0850",
                                "term": "Sequence set"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
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                "Command-line tool",
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                    "term": "Computational biology"
                }
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                "Linux",
                "Windows",
                "Mac"
            ],
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            ],
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            "download": [
                {
                    "url": "https://github.com/StuntsPT/NCBI_Mass_Downloader/releases",
                    "type": "Software package",
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                },
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                    "url": "https://github.com/StuntsPT/NCBI_Mass_Downloader/releases",
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                {
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                    "metadata": {
                        "title": "NCBI Mass Sequence Downloader–Large dataset downloading made easy",
                        "abstract": "© 2016 The Author(s)Sequence databases, such as NCBI, are a very important resource in many areas of science. Downloading small amounts of sequences to local storage can easily be performed using any recent web browser, but downloading tens of thousands of sequences is not as simple. NCBI Mass Sequence Downloader is an open source program aimed at simplifying obtaining large amounts of sequence data from NCBI databases to local storage. It is written in python (can be run under both python 2 and python 3), and uses PyQt5 for the GUI. The program can be run in either graphical or command line mode. Source code is licensed under the GPLv3, and is supported on Linux, Windows and Mac OSX. Available at https://github.com/ElsevierSoftwareX/SOFTX-D-15-00072.git, https://github.com/StuntsPT/NCBI_Mass_Downloader",
                        "date": "2015-10-15T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Pina-Martins F."
                            },
                            {
                                "name": "Paulo O.S."
                            }
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                        "journal": "SoftwareX"
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                    "url": "https://github.com/StuntsPT/NCBI_Mass_Downloader/issues",
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            "additionDate": "2017-05-12T10:55:31Z",
            "lastUpdate": "2018-12-10T12:58:41Z",
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}