dataesr/scientific-paragraphs-categorization huggingface dataset:
This shows the utility of the VIRify pipeline, used in isolation from MGnify, for studying coronaviruses in the human respiratory microbiome. VIRify can be used for the identification of coronaviruses in clinical and environmental samples. Due to the intrinsic differences between metatranscriptomes and metagenomes, additional considerations regarding quality control, assembly, post-processing and classification have to be kept in mind (for details, see https://github. com/EBI-Metagenomics/emg-viralpipeline). VIRify is available via GitHub under APLv2: https://github. com/EBI-Metagenomics/emg-viral-pipeline.
Genome analysis tools by VBRC The Viral Bioinformatics Research Centre (VBRC) is a mature resource built specifically for virologists to facilitate the comparative analysis of viral genomes. Within VBRC, a MySQL database created from GenBank files supports numerous analysis tools. The curated database is accessed through Virus Orthologous Clusters  [16] , a powerful, but easy-to-use database GUI. Base-By-Base  [9, 33, 102]    viral genomes. The tool allows the user to export protein or DNA sequences and can display START/STOP codons for 6-frames as well as open reading frames and other user-defined results. If genomes are loaded from the database, it can display shared orthologs. Genome Annotation Transfer Utility  [98]  is a tool for annotating genomes using information from a reference genome.
It provides for interactive annotation, automatically annotating genes that are very similar to the reference virus but leaving others for a human decision. The VBRC was developed for dsDNA viruses but has been adapted for coronaviruses. SARS-CoV-2 and closely related viruses have been added to the database. VBRC tools will help to visualize differences between coronavirus sequences at different levels of resolution (see Figure  4 ). VBRC is available via https://www. 4virology. net; all tools are published under GPLv3.
VIRULIGN is particularly well suited to study the rapidly growing number of SARS-CoV-2 genomes made available  [80] , due to its efficient alignment algorithm that has linear computational complexity with respect to the number of sequences studied. Furthermore, VIRULIGN's flexible output formats (e. g. CSV file with headers corresponding to the genome annotation) facilitate its integration into analysis workflows, lowering the threshold for scientists to deliver advanced bioinformatics pipelines  [13, 57]  and databases  [56] , that are necessary to track the COVID-19 pandemic. The family is a subset of the 3' UTR model (RF03120) that corresponds to the PDB:1XJR 3D structure from SARS-CoV-1. VIRULIGN is available via GitHub under the the GNU General Public License v2. 0 (GPLv2): https://github.
Rfam COVID-19 resources: coronavirus-specific RNA families Rfam  [40]  is a database of RNA families that hosts curated multiple sequence alignments and covariance models. To facilitate the analysis of Coronavirus sequences, Rfam produced a special release 14. 2 with ten new families representing the entire 5' and 3' untranslated regions (UTRs) from Alpha-, Beta-, Gammaand Deltacoronaviruses. A specialized set of Sarbecovirus models is also provided, which includes SARS-CoV-1 and SARS-CoV-2 sequences. The families are based on a set of high-quality whole genome alignments that have been reviewed by expert virologists. In addition, Rfam now contains a revised set of non-UTR Coronavirus structured RNAs, such as the frameshift stimulating element, s2m RNA, and the 3' UTR pseudoknot. The new Rfam families can be used in conjunction with the Infernal software  [71]  to annotate structured RNAs in Coronavirus sequences and predict their secondary structure (see Figure  5 ).
Pfam protein families database The Pfam protein families database is widely used in the field of molecular biology for large-scale functional annotation of proteins  [17]. The latest release of Pfam, version 33. 1, contains an updated set of models that comprehensively cover the proteins encoded by SARS-CoV-2 (see Table  3 ). The only SARS-CoV-2 protein that lacks a match is Orf10, a small putative protein found at the 3'-end of the SARS-CoV-2 genome, which appears to lack similarity to any other sequence in UniProtKB (https:// covid-19. uniprot. org/). The Pfam profile hidden Markov model (HMM) library in combination with the HMMER software  [15]  facilitates rapid search and annotation of coronaviruses and can be used to generate multiple sequence alignments that allow the identification of mutations and clusters of related sequences, particularly useful for outbreak tracking and studying the evolution of coronaviruses.
The Pfam HMM library can be downloaded from https://pfa m. xfam. org and can be used in combination with pfam_scan to perform Pfam analysis locally. Multiple sequence alignments of matches can be generated using hmmalign (http://hmmer. org/). Precalculated matches and alignments are available from the Pfam FTP site (ftp://ftp. ebi. ac. uk/pub/databases/Pfam/releases/-Downloaded from https://academic. oup. com/bib/advance-article/doi/10. 1093/bib/bbaa232/5955939 by guest on 16 November 2020   Pfam_SARS-CoV-2_2. 0/). Pfam is freely available under the Creative Commons Zero (CC0) licence.
For SARS-CoV-2, the default uploaded model is based on Nextstrain  [31]  and GISAID data  [18]. Alternatively, user-uploaded models can be used. Covidex is based on a fast implementation of random forest trained over a k-mer database  [7, 118]. By training the classification algorithms over k-mer frequency vectors, Covidex substantially reduces computational and time requirements and can classify hundreds of SARS-CoV-2 genomes in seconds. Thus, in the context of the current global pandemic where the number of available SARS-CoV-2 genomes is growing exponentially, SARS-CoV-2 research can benefit from this specific tool designed to reduce the time needed in data analysis significantly. Covidex is available via SourceForge under GPLv3: https://sou rceforge. net/projects/covidex or the web application https://ca cciabue.
It can also be used for surveillance purposes, summarizing which lineages are present in an area of interest. The web-application also connects with Microreact (microreact. org) displaying query sequences in the context of the global lineages worldwide. pangolin is used as part of COG-UK's (https://www. cogconsortium. uk/) data processing pipeline to assign lineages to UK sequences. Further, users can define their own finer-scale lineages, for instance within-country lineages, and provide their own guide tree and alignment. Pangolin makes it easy to get useful information out of viral genome sequencing in real-time and can assist in identifying new introductions and in tracking the spread of SARS-CoV-2. Pangolin is available via GitHub under GPLv3: https://github. com/hCoV-2019/pangolin/ and as web application via https://pa ngolin.
BEAST 2: phylodynamics based on Bayesian inference Important evolutionary and epidemiological questions regarding SARS-CoV-2 can be addressed using Bayesian phylodynamic inference  [27] , which allows the adequate combination of evidence from multiple independent sources of data, such as genome sequences, sampling dates and geographic locations. BEAST 2  [6]  is an advanced computational software framework that enables sophisticated Bayesian analyses utilizing a range of phylodynamic packages, e. g. [14, 45, 50, 93, 107, 108, 111]. The phylogenetic history (the tree) can be inferred simultaneously with evolutionary and epidemiological parameters, such that the uncertainty from all aspects of the joined model is accounted for and reflected in the results. Phylodynamic analysis of SARS-CoV-2 is crucial in understanding (i) SARS-CoV-2 evolutionary dynamics, particularly through estimation of the evolutionary rate at which mutations get fixated in the viral genome, (ii) the temporal origin of a selection of COVID-19 cases as an approximation of the time at which a sub-epidemic emerged, (iii) the geographical origin of sub-epidemics, (iv) SARS-CoV-2 transmission dynamics, e.
Phylogeographic reconstruction using air transportation data Phylogeographic methods combine genomic data with the sampling locations of viral isolates and models of spread, e. g. using air travel or local diffusion, to reconstruct the putative spread paths and outbreak origins of rapidly evolving pathogens. [82]  published a method that infers locations for internal nodes of a phylogenetic tree using a parsimonious reconstruction together with effective distances, as defined by  [8]. Effective distances are calculated based on passenger flows between airports. A strong connection between two airports is represented by a small distance. Using these distances as a cost matrix, the parsimonious reconstruction identifies ancestral locations for internal nodes of the tree that minimize the distances along the phylogeny.
This method allows rapid inferences of spread paths on a fine-grained geographical scale  [82]. Reconstruction using effective distances infers phylogeographic spread more accurately than reconstruction using geographic distances or Bayesian reconstructions that do not use any distance information. Phylogeographic reconstruction using air transportation data can be used to study the global spread of the SARS-CoV-2 pandemic, especially in the early phases when air travel still substantially contributed to the spread of the virus. The method is currently adapted to consider both air travel and local movement data within countries during inference to reflect the changing worldwide movements in different phases of the pandemic. The code is included in the GitHub repository for  [82]  under APLv2: https://github.
Firstly, the pipeline builds a multiple sequence alignment, estimates a best-fitting substitution model and performs a recombination analysis followed by the construction of all corresponding phylogenies. Secondly, positively selected sites under varying models are detected. The results are summarized in a user-friendly web page, providing all intermediate results and graphically displaying recombination events and positively selected sites. The rapid detection of positive selection helps to monitor protein changes of SARS-CoV-2 during the pandemic. It provides potential target sites for drug development, helping to counteract the virus during its "arms race" with the human species. Poseidon is available via GitHub under MIT License: https:// github. com/hoelzer/poseidon.
CORDITE: CORona Drug InTERactions database CORDITE collects data on potential drugs, targets and their interactions for SARS-CoV-2 from published articles and preprints  [62]. CORDITE integrates many functionalities to enable users to access, sort and download relevant data to conduct meta-analyses, to design new clinical trials or even to conduct a curated literature search. CORDITE automatically incorporates publications from PubMed (https://www. ncbi. nlm. nih. gov/pubmed/), bioRxiv (https://www. biorxiv. org/), chemRxiv https://www. chemrxiv. org/) and medRxiv https://www. medrxi v. org/) that report information on computational, in vitro, or case studies on potential drugs for COVID-19. Besides original research, reviews and comments are also included in the database. The information from the articles and preprints are manually curated by moderators and can be accessed via the web server or the open API.
Moreover, registered clinical trials from the NIH https://clinicaltrials. gov/) for COVID-19 are also included. Users can directly access the publications, interactions, drugs, targets and clinical trials, and thus the data can be easily integrated into other software or apps. The CORDITE database is updated weekly and, at the date of submission, provides data for more than 700 interactions of 23 targets for more than 530 drugs from almost 300 publications and more than 240 clinical trials (as of  May 19, 2020). It is thus the largest, curated database available for drug interactions for SARS-CoV-2. It allows researchers to carry out meta-analyses on potential drugs systematically and to identify potential drug candidates for clinical trials. CORDITE can be accessed via https://cordite. mathematik.
P-HIPSTer: a virus-host protein-protein interaction resource Viral-host protein-protein interactions (PPIs) play a crucial role during viral infection by co-opting host cellular processes and hold promising therapeutic prospects. Along these lines, the P-HIPSTer database can significantly contribute to SARS-CoV2 research by providing: (1) testable hypotheses on molecular interactions underlying viral infection and pathogenesis and (2) highlighting host factors and pathways that serve as potential drug targets to treat infection caused by different coronaviruses. P-HIPSTer comprises ∼282,000 predicted viral-human PPIs on ∼1,000 viruses with an experimental validation rate of ∼76%  [52]. Its predictive algorithm is an adaptation of PrePPI  [21, 123]  and combines sequence and structural information to infer viralhuman PPIs mediated by domain-domain or peptide-domain contacts (see Figure  10 ).
In addition, P-HIPSTer builds all-atom interaction models for high-confidence PPI predictions involving folded domains and integrates sequence-and structure-based functional annotations for viral proteins at multiple levels, including host biological pathways based on the predicted PPIs  [3, 20, 23, 96]. Hence, P-HIPSTer constitutes a complimentary resource to high-throughput experimental approaches  [26]. As of April 2020, P-HIPSTer contains predictions for 15 coronaviruses with varying pathogenic potential (alpha-and betacoronaviruses) and reports 4,587 viral-host PPIs involving 397 human proteins. This unique collection of predicted viralhuman PPIs enables the discovery of PPIs commonly employed within the Coronaviridae family and PPIs associated with their pathogenicity. The database is available via http://www.
phipster. org/ to be complete, and in light of the rapid ongoing research, further tools will be developed. Efficient response to the pandemic requires high-quality SARS-CoV-2 data and meta-data  [87]  and newly released software to be available freely and as open source. Open source code invites other developers to improve the software. Preferably, code should be shared via a suitable repository such as GitHub, allowing for transparency and managing versioning and feature development. In particular, when software and resources are evolving as fast as the virus, versioning and reproducibility of all steps are of increasing importance. In the context of pipelines, versions of third-party tools should be fixed using package managers like Conda or by encapsulation using container software (Docker  [5] , Singularity).
Workflow management systems such as SnakeMake or Nextflow  [19, 100]  allow easy installation and reproducible execution on various Downloaded from https://academic. oup. com/bib/advance-article/doi/10. 1093/bib/bbaa232/5955939 by guest on 16 November 2020 platforms. In the best case, all tools and pipelines should be automatically and continuously tested to evaluate their quality and usability. Also, manuscripts on software and methods development can be made available as preprints to accelerate their dissemination. Of course, these standards are not specific for coronavirus related research, but rather general points about bioinformatics software.
uni-jena. de/tools/coronavirus-tools/), some of which were presented in this review. Other initiatives are collecting relevant datasets (COVID-19 Data Portal, https://www. covid19datapo rtal. org/) or are supporting researchers by offering assistance with SARS-CoV-2 genome sequencing (NFDI4Microbiota, https:// nfdi4microbiota. de/index. php/covid-19/). ELIXIR (https://elixi r-europe. org/services/covid-19) provides a range of services to study SARS-CoV-2, in particular, the European Galaxy server for data-intensive research that provides access to scientific tools and training materials to guide users through COVID-19 data analysis. In addition, it is an encouraging development seeing researchers joining efforts in national and international initiatives to combat the ongoing pandemic. For example, researchers around the world are jointly reconstructing the molecular processes of the virus-host interactions to develop a COVID-19 Disease Map  [74].
Acknowledgments M. H. appreciates the support of the  Joachim Herz Foundation  by the add-on fellowship for interdisciplinary life science. P. L. acknowledges funding from the  EpiPose  project (European Union's  SC1-PHE-CORONAVIRUS-2020 programme ,  H2020/101003688 ). Á. N. O. T. thanks  Anthony Underwood  and  David Aanensen  ( Centre for Genomic Pathogen Surevillance, Hinxton, Cambridgeshire ), as well as  JT McCrone  and  Verity Hill  ( Rambaut Group, Edinburgh University ) for contributions to the pangolin code. G. R. -P. thanks the  CABANA  Project for their support while conducting a research secondment at  EMBL-EBI. We acknowledge the  UniProt Consortium  for the production of UniProt. GISAID acknowledgements can be found at this link: https://raw. githubusercontent. com/hCoV-2019/lineages/master/gisaid_acknowledgements.
4virology. net https://github. com/rega-cev/virulign https://rfam. org/covid-19 https://covid-19. uniprot. org/ https://pfam. xfam. org https://sourceforge. net/projects/covidex https://cacciabue. shinyapps. io/ shiny2/ https://pangolin. cog-uk. io/ https://github. com/hCoV-2019/pangolin/ https://www. beast2. org/ https://github. com/hzi-bifo/Phylogeography_Paper http://copasi. org/ https://github. com/copasi http://www. kaderali. org:3838/covidsim http://cov-glue. cvr. gla. ac. uk/ https://github. com/hoelzer/poseidon http://virhostnet. prabi. fr/ https://cordite. mathematik. uni-marburg. de https://exbio. wzw. tum. de/covex/ http://www. phipster. org/ License GPLv3 GPLv3 GPLv3 public domain APLv2 APLv2 APLv2 GPLv3 GPLv2 CC0 CC BY 4. 0 CC0 GPLv3 GPLv3 LGPL APLv2 Artistic License 2. 0 - (AGPLv3) a MIT License -b CC BY-ND -c -d Tool Advancing SARS-CoV-2 research by Detection and annotation PriSeT computing SARS-CoV-2 specific primers for RT-PCR tests CoVPipe reproducible, reliable and fast analysis of NGS data poreCov reducing time-consuming bioinformatic bottlenecks in processing sequencing runs VADR validation and annotation of SARS-CoV-2 sequences V-Pipe reproducible NGS-based, end-to-end analysis of genomic diversity in intra-host virus populations Haplof low detection and full-length reconstruction of multi-strain infections VIRify identifying viruses in clinical samples VBRC genome analysis visualizing differences between coronavirus sequences at different tools levels of resolution VIRULIGN fast, codon-correct multiple sequence alignment and annotation of virus genomes Rfam COVID-19 annotating structured RNAs in coronavirus sequences and predicting secondary structures UniProt COVID-19 providing latest knowledge on proteins relevant to the disease for virus and host Pfam protein detection and annotation for outbreak tracking and studying evolution Tracking, epidemiology and evolution Covidex fast and accurate subtypification of SARS-CoV-2 genomes Pangolin assigning a global lineage to query genomes BEAST 2 understanding geographical origin and evolutionary and transmission dynamics Phylogeographic studying the global spread of the pandemic with particular focus on air reconstruction transportation data COPASI modelling the dynamics of the epidemic and effect of interventions COVIDSIM analysing effects of contact reduction measures and guide political decision-making CoV-GLUE tracking changes accumulating in the SARS-CoV-2 genome PoSeiDon detection of positive selection in protein-coding genes Drug design VirHostNet understanding molecular mechanisms underlying virus replication and pathogenesis CORDITE carrying out meta-analyses on potential drugs and identifying potential drug candidates for clinical trials CoVex identifying already approved drugs that could be repurposed to treat COVID-19 P-HIPSTer enabling the discovery of PPIs commonly employed within the coronavirus family and PPIs associated with their pathogenicity a License of the underlying software system GLUE; b All data open access; c Source code available upon request; d Predictions available upon request.
Optimization methods are playing an increasingly important role in all facets of photonics engineering, from integrated photonics to free space diffractive optics. However, efforts in the photonics community to develop optimization algorithms remain uncoordinated, which has hindered proper benchmarking of design approaches and access to device designs based on optimization. We introduce MetaNet, an online database of photonic devices and design codes intended to promote coordination and collaboration within the photonics community. Using metagratings as a model system, we have uploaded over one hundred thousand device layouts to the database, as well as source code for implementations of local and global topology optimization methods. Further analyses of these large datasets allow the distribution of optimized devices to be visualized for a given optimization method.
Additionally, the lack of coordination within the community prevents the collective production of very large datasets of related devices for applications such as data-driven analysis. In this paper, we introduce MetaNet (http://metanet. stanford. edu), an online repository of design algorithms and device layouts for photonic technologies. The inspiration for MetaNet comes from the data sciences community, where swift progress in machine learning development has been largely mediated through a culture of sharing, benchmarking, and use of common datasets, such as those featured in ImageNet  [35]  and CIFAR  [36]. With MetaNet, we hope to satisfy four major needs within the photonics community. The first is providing an accessible and indexed repository for sharing high-performance devices, which can be directly used in an application.
The second is providing labeled large-scale datasets for big data analyses, such as those based on machine learning. The third is the standardization of conventions and parameters for easy design benchmarking. The fourth is a centralized resource for openly available nanophotonics design software from groups across the world. To initiate this effort, we focus here on filling out MetaNet with metagrating devices and codebases, though our goal is to extend MetaNet to many classes of photonic technologies. Such a culture of sharing and collaboration is currently not mainstream in the photonics community, but we anticipate that this cultural shift will greatly expedite collaboration and the advancement of photonics design tools.
The MetaNet database Our focus in this paper is on metasurfaces, specifically periodic metasurfaces (i. e. , metagratings) that diffract incident light to the +1 diffraction channel. Metagratings are an excellent model system for metasurface analysis and algorithm benchmarking for a few reasons. First, the design objectives are well-defined and unambiguous: we want to maximize the intensity of light in a specific diffraction channel. Other types of devices may have more ambiguous design objectives. For example, the performance criteria for metalenses can range from maximizing the field magnitude at the focal point to maximizing the light intensity within the central and first side lobes. Second, metagratings are periodic structures and can therefore be designed using rigorous coupled wave analysis (RCWA), which is a high-speed electromagnetic solver.