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  description: Evolutionary simulation framework that combines a powerful engine for
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    evolutionary scenarios.  Includes a graphical modeling environment.
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  - note: The manual for SLiM itself
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  - note: The manual for Eidos, the scripting language used by SLiM
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    - User manual
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  - note: Quick reference sheets for SLiM and Eidos
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    - Quick start guide
    url: http://benhaller.com/slim/SLiMEidosRefSheets.zip
  download:
  - note: A source archive for the command-line `slim` tool only.  Complete source
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      chapter 2, for installation instructions.
    type: Source code
    url: http://benhaller.com/slim/SLiM.zip
    version: null
  - note: The GitHub page for the current release version, to obtain full source code.
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    note: Run individual-based eco-evolutionary simulations with explicit genetics
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  license: GPL-3.0
  link:
  - note: SLiM home page in the Messer Lab website
    type:
    - Software catalogue
    url: https://messerlab.org/slim/
  - note: GitHub repository for SLiM
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    - Repository
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  - note: Discussion forum for SLiM questions
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    - Discussion forum
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  - note: Announcements mailing list
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  maturity: Mature
  name: SLiM
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  publication:
  - doi: 10.1093/molbev/msy228
    metadata:
      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.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 378
      date: '2019-03-01T00:00:00Z'
      journal: Molecular Biology and Evolution
      title: 'SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model'
    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.'
    pmcid: null
    pmid: null
    type:
    - Other
    version: null
  - doi: 10.1093/molbev/msy237
    metadata:
      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.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 6
      date: '2019-05-01T00:00:00Z'
      journal: Molecular Biology and Evolution
      title: Evolutionary Modeling in SLiM 3 for Beginners
    note: B.C. Haller, P.W. Messer. (2019). Evolutionary modeling in SLiM 3 for beginners.
      Molecular Biology and Evolution 36(5), 1101–1109.
    pmcid: null
    pmid: null
    type:
    - Usage
    version: null
  - doi: 10.1111/1755-0998.12968
    metadata: 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.
    pmcid: null
    pmid: null
    type:
    - Method
    version: null
  - doi: 10.1086/723601
    metadata:
      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.
      authors:
      - name: Haller B.C.
      - name: Messer P.W.
      citationCount: 10
      date: '2023-05-01T00:00:00Z'
      journal: American Naturalist
      title: 'SLiM 4: Multispecies Eco-Evolutionary Modeling'
    note: 'B.C. Haller, P.W. Messer. (2023). SLiM 4: Multispecies eco-evolutionary
      modeling. The American Naturalist 201(5), E127–E139.'
    pmcid: null
    pmid: null
    type:
    - Primary
    version: null
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  - term: Molecular interactions, pathways and networks
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  - term: Genetic variation
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  - term: Evolutionary biology
    uri: http://edamontology.org/topic_3299
  validated: 0
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  description: Prediction of the presence and location of signal peptide cleavage
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    note: predicts the presence and location of signal peptide cleavage sites in amino
      acid sequences from different organisms
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      uri: http://edamontology.org/operation_0418
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  license: Other
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  maturity: Mature
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    metadata:
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  toolType:
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  topic:
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    uri: http://edamontology.org/topic_3510
  validated: 1
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- accessibility: null
  additionDate: '2022-03-25T08:59:55.062522Z'
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  description: Pipeline for scaffolding and breaking a genome assembly using 10x genomics
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  function:
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  owner: Chan019
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  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0622
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  validated: 1
  version:
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- accessibility: null
  additionDate: '2015-05-04T12:32:36Z'
  biotoolsCURIE: biotools:spindlep
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  description: Neural networks prediction of whether a human gene is located at the
    meiotic spindle.
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    note: network ensemble
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  publication:
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    metadata:
      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.
      authors:
      - name: Rojas A.M.
      - name: Santamaria A.
      - name: Malik R.
      - name: Jensen T.S.
      - name: Korner R.
      - name: Morilla I.
      - name: de Juan D.
      - name: Krallinger M.
      - name: Hansen D.A.
      - name: Hoffmann R.
      - name: Lees J.
      - name: Reid A.
      - name: Yeats C.
      - name: Wehner A.
      - name: Elowe S.
      - name: Clegg A.B.
      - name: Brunak S.
      - name: Nigg E.A.
      - name: Orengo C.
      - name: Valencia A.
      - name: Ranea J.A.G.
      citationCount: 10
      date: '2012-03-09T00:00:00Z'
      journal: PLoS ONE
      title: Uncovering the molecular machinery of the human spindle-an integration
        of wet and dry systems biology
    note: null
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    pmid: '22427808'
    type:
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  toolType:
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  topic:
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    uri: http://edamontology.org/topic_0140
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  additionDate: '2017-05-12T10:55:31Z'
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  description: This program will download sequences en masse from several NCBI databases
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  homepage: https://github.com/StuntsPT/NCBI_Mass_Downloader
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      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
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next: null
previous: null
