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The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components.Availability and implementationThe MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online.Contactmsa@bio.sh.

We modeled 3D structures of all SARS‐CoV‐2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post‐translational modifications, block host translation, and disable host defenses; a further ˜29% self‐assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is—and is not—known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria‐COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark. SYNOPSIS: 2,060 structural models spanning 69% of the SARS‐CoV‐2 proteome are generated and presented in a novel visual layout that summarises current knowledge on viral protein structures, and provides insight into viral replication, mimicry, and hijacking. 2,060 structural models spanning 69% of the SARS‐CoV‐2 proteome are built, grouped in 79 states and visualized in a concise structural coverage map.˜6% of the viral proteome appears to mimic human proteins, with the strongest evidence seen for mimicry of three human helicases (UPF1, IGHMBP2, and AQR) by the viral helicase NSP13.˜7% of the viral proteome is implicated in hijacking human proteins, thereby reversing post‐translational modifications, blocking host translation, and disabling host defenses.˜29% of the viral proteome self‐assembles into heteromers, with the heteromeric states suggesting the order in which proteins assemble to form the replication and translation complex. [ABSTRACT FROM AUTHOR]

The Dundee Resource for Sequence Analysis and Structure Prediction (DRSASP; http://www.compbio.dundee.ac.uk/drsasp.html) is a collection of web services provided by the Barton Group at the University of Dundee. DRSASP's flagship services are the JPred4 webserver for secondary structure and solvent accessibility prediction and the JABAWS 2.2 webserver for multiple sequence alignment, disorder prediction, amino acid conservation calculations, and specificity‐determining site prediction. DRSASP resources are available through conventional web interfaces and APIs but are also integrated into the Jalview sequence analysis workbench, which enables the composition of multitool interactive workflows. Other existing Barton Group tools are being brought under the banner of DRSASP, including NoD (Nucleolar localization sequence detector) and 14‐3‐3‐Pred. New resources are being developed that enable the analysis of population genetic data in evolutionary and 3D structural contexts. Existing resources are actively developed to exploit new technologies and maintain parity with evolving web standards. DRSASP provides substantial computational resources for public use, and since 2016 DRSASP services have completed over 1.5 million jobs. [ABSTRACT FROM AUTHOR]