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{ "count": 3101, "next": "?page=2", "previous": null, "list": [ { "name": "SBOLCanvas", "description": "SBOLCavas is a web application for creation and editing of genetic constructs using the SBOL data and visual standard. 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SBOLCanvas allows a user to create a genetic design visually and structurally from start to finish. It also allows users to incorporate existing SBOL data from a SynBioHub repository. By the nature of being a web-based application, SBOLCanvas is readily accessible and easy to use. 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However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize diverse cell populations. Single cell discriminant analysis (scDA) solves this problem by simultaneously identifying cell groups and discriminant metagenes based on the construction of cell-by-cell representation graph, and then using them to annotate unlabeled cells in data. We demonstrate scDA is effective to determine cell types, revealing the overall variabilities between cells from eleven data sets. scDA also outperforms several state-of-the-art methods when inferring the labels of new samples. In particular, we found scDA less sensitive to drop-out events and capable to label a mass of cells within or across datasets after learning even from a small set of data. The scDA approach offers a new way to efficiently analyze scRNA-seq profiles of large size or from different batches. scDA was implemented and freely available at https://github.com/ZCCQQWork/scDA.", "date": "2021-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Shi Q." }, { "name": "Li X." }, { "name": "Peng Q." }, { "name": "Zhang C." }, { "name": "Chen L." } ], "journal": "Computational and Structural Biotechnology Journal" } } ], "credit": [ { "name": "Chuanchao Zhang", "email": "chaozhangchuan@163.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Luonan Chen", "email": "lnchen@sibs.ac.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Jennifer", "additionDate": "2021-10-17T15:01:50.684526Z", "lastUpdate": "2021-10-17T15:03:16.159890Z", 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Includes a graphical modeling environment.", "homepage": "https://messerlab.org/slim/", "biotoolsID": "SLiM_software", "biotoolsCURIE": "biotools:SLiM_software", "version": [ "3.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0550", "term": "DNA substitution modelling" }, { "uri": "http://edamontology.org/operation_3946", "term": "Ecological modelling" } ], "input": [], "output": [], "note": "Run individual-based evolutionary simulations with explicit genetics", "cmd": null } ], "toolType": [ "Command-line tool", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0610", "term": "Ecology" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_3299", "term": "Evolutionary biology" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "C++" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://messerlab.org/slim/", "type": [ "Software catalogue" ], "note": "SLiM home page in the Messer Lab website" }, { "url": "https://github.com/MesserLab/SLiM", "type": [ "Repository" ], "note": "GitHub repository for SLiM" }, { "url": "https://groups.google.com/g/slim-discuss", "type": [ "Discussion forum" ], "note": "Discussion forum for SLiM questions" }, { "url": "https://groups.google.com/g/slim-announce", "type": [ "Mailing list" ], "note": "Announcements mailing list" } ], "download": [ { "url": "http://benhaller.com/slim/SLiM.zip", "type": "Source code", "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.", "version": null }, { "url": "https://github.com/MesserLab/SLiM/releases/latest", "type": "Downloads page", "note": "The GitHub page for the current release version, to obtain full source code.", "version": null } ], "documentation": [ { "url": "http://benhaller.com/slim/SLiM_Manual.pdf", "type": [ "User manual" ], "note": "The manual for SLiM itself" }, { "url": "http://benhaller.com/slim/Eidos_Manual.pdf", "type": [ "User manual" ], "note": "The manual for Eidos, the scripting language used by SLiM" }, { "url": "http://benhaller.com/slim/SLiMEidosRefSheets.zip", "type": [ "Quick start guide" ], "note": "Quick reference sheets for SLiM and Eidos" } ], "publication": [ { "doi": "10.1093/molbev/msy228", "pmid": null, "pmcid": null, "type": [ "Primary" ], "version": null, "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": "© 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." } ], "journal": "Molecular Biology and Evolution" } }, { "doi": "10.1093/molbev/msy237", "pmid": null, "pmcid": null, "type": [ "Usage" ], "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": "© 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", "citationCount": 1, "authors": [ { "name": "Haller B.C." }, { "name": "Messer P.W." } ], "journal": "Molecular Biology and Evolution" } }, { "doi": "10.1111/1755-0998.12968", "pmid": null, "pmcid": null, "type": [ "Method" ], "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.", "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." } ], "journal": "Molecular Ecology Resources" } } ], "credit": [ { "name": "Philipp Messer", "email": "messer@cornell.edu", "url": "https://messerlab.org", "orcidid": "https://orcid.org/0000-0001-8453-9377", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Benjamin C. Haller", "email": "bhaller@benhaller.com", "url": "http://benhaller.com", "orcidid": "https://orcid.org/0000-0003-1874-8327", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "bchaller", "additionDate": "2019-05-27T16:59:15Z", "lastUpdate": "2021-10-17T00:12:21.515331Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SDImpute", "description": "SDImpute is an implement block imputation for dropout events in scRNA-seq data. SDImpute combines both cell-level and gene-level information to identify the drop-out events and borrows the information unaffected by dropouts from similar cells to impute the dropouts.", "homepage": "https://github.com/Jinsl-lab/SDImpute", "biotoolsID": "sdimpute", "biotoolsCURIE": "biotools:sdimpute", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3557", "term": "Imputation" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3960", "term": "Principal component analysis" }, { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/Jinsl-lab/SDImpute/blob/master/R/SDImpute.r", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/Jinsl-lab/SDImpute", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1371/JOURNAL.PCBI.1009118", "pmid": "34138847", "pmcid": "PMC8266063", "type": [], "version": null, "note": null, "metadata": { "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.", "date": "2021-06-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Qi J." }, { "name": "Zhou Y." }, { "name": "Zhao Z." }, { "name": "Jin S." } ], "journal": "PLoS Computational Biology" } } ], "credit": [ { "name": "Shuilin Jin", "email": "jinsl@hit.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-2318-432X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jing Qi", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Yang Zhou", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Zicen Zhao", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "Jennifer", "additionDate": "2021-10-15T15:08:49.095068Z", "lastUpdate": "2021-10-15T15:10:52.394535Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SFMEB", "description": "A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.", "homepage": "https://bioconductor.org/packages/MEB", "biotoolsID": "sfmeb", "biotoolsCURIE": "biotools:sfmeb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3676", "term": "Exome sequencing" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-2.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://bioconductor.org/packages/release/bioc/html/MEB.html", "type": "Command-line specification", "note": null, "version": null } ], "documentation": [ { "url": "https://bioconductor.org/packages/release/bioc/manuals/MEB/man/MEB.pdf", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1186/S12864-021-07790-0", "pmid": "34174824", "pmcid": "PMC8234728", "type": [], "version": null, "note": null, "metadata": { "title": "A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data", "abstract": "BACKGROUND: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. RESULTS: In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization. CONCLUSIONS: Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .", "date": "2021-06-26T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhou Y." }, { "name": "Yang B." }, { "name": "Wang J." }, { "name": "Zhu J." }, { "name": "Tian G." } ], "journal": "BMC genomics" } } ], "credit": [ { "name": "Jiadi Zhu", "email": "2160090406@email.szu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Guoliang Tian", "email": "tiangl@sustech.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Jennifer", "additionDate": "2021-10-15T14:56:23.749712Z", "lastUpdate": "2021-10-15T14:59:23.023943Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }