“This agreement was a true collaboration made possible by our shared goals of openness, equity, and transparency.” “PLOS recognizes that APCs create barriers for some researchers to publish open access and contribute to inequity in scholarly communications,” said Chris Bourg, director of MIT Libraries. Instead of authors paying article processing charges (or APCs, payments charged to authors or their institutions to make a work available open access), PLOS charges the Institute transparent and equitable fees as guided by the Plan S Price and Service Transparency Framework. The aim of the PLOS agreements is to remove the burden of cost of publishing articles from authors and allow MIT to support authors who publish open access. The agreement aligns with the core principles of the MIT Framework for Publisher Contracts. 10.2807/1560-7917.ES.2017.The MIT Libraries has negotiated two new open-access publishing agreements with the nonprofit publisher Public Library of Science (PLOS) that allow MIT authors to publish in all PLOS titles with no publishing fees. GISAID: Global initiative on sharing all influenza data-from vision to reality. Sanjuán R, Nebot MR, Chirico N, Mansky LM, Belshaw R. The global spread of 2019-nCoV: a molecular evolutionary analysis. 10.46234/ccdcw2020.017īenvenuto D, Giovanetti M, Salemi M, Prosperi M, Flora C, Alcantara L, et al. A novel coronavirus genome identified in a cluster of pneumonia cases-Wuhan, China 2019- 2020.
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Tan W, Zhao X, Ma X, Wang W, Niu P, Xu W, et al.
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Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at (Jupyter notebook), (command line tool) and via an interactive website at. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain project. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. Viral subtypes may be difficult to detect due to rapid evolution founder effects are more significant than selection pressure and the clustering threshold for subtyping is not standardized. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies.
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Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic.