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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": "© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.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",
                        "citationCount": 143,
                        "authors": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Messer P.W."
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                    "type": [
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                    "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": "© 2019 The Author(s).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": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Messer P.W."
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                        "journal": "Molecular Biology and Evolution"
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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": "© 2018 John Wiley & Sons LtdThere 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.",
                        "date": "2019-03-01T00:00:00Z",
                        "citationCount": 33,
                        "authors": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Galloway J."
                            },
                            {
                                "name": "Kelleher J."
                            },
                            {
                                "name": "Messer P.W."
                            },
                            {
                                "name": "Ralph P.L."
                            }
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                        "journal": "Molecular Ecology Resources"
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                        "title": "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data",
                        "abstract": "Copyright: © 2021 Qi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.",
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                        "abstract": "© 2018 The Author(s).Background: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. Results: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. Conclusions: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.",
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                        "title": "ALeS: Adaptive-length spaced-seed design",
                        "abstract": "© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.Motivation: Sequence similarity is the most frequently used procedure in biological research, as proved by the widely used BLAST program. The consecutive seed used by BLAST can be dramatically improved by considering multiple spaced seeds. Finding the best seeds is a hard problem and much effort went into developing heuristic algorithms and software for designing highly sensitive spaced seeds. Results: We introduce a new algorithm and software, ALeS, that produces more sensitive seeds than the current state-of-the-art programs, as shown by extensive testing. We also accurately estimate the sensitivity of a seed, enabling its computation for arbitrary seeds. Availabilityand implementation: The source code is freely available at github.com/lucian-ilie/ALeS.",
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