To the Editor -Genomic surveillance of the evolving SARS-CoV-2 strains is an important tool for helping control the pandemic 1 . For efficient surveillance, the first major requirement for analysis of how the virus is evolving and spreading is the availability of all sequenced genomes on an open-access platform that is accessible to researchers worldwide. Therefore, soon after researchers became aware of COVID-19, toward the end of 2019, the Global Initiative on Sharing All Influenza Data (GISAID), an existing platform for sharing influenza virus sequences, began receiving deposits of SARS-CoV-2 genome sequences. Here we report an analysis of the median collection to submission time (CST) lag for SARS-CoV-2 sequences to GISAID on a country-by-country basis. Our results suggest that researchers in the United Kingdom are the fastest, logging sequences in a median time of 16 days, which is not only more than 5 times as fast as the upload times of sequences originating from industrial countries such as Japan or Canada, but also 18 times as fast as that of Qatar, among the countries that have sequenced over 1,000 genomes.As of now, GISAID is the largest open-access portal, hosting the genome sequences and related epidemiological and clinical data of more than 1.7 million SARS-CoV-2 strains. Thanks to ongoing genomic surveillance using GISAID data, several new SARS-CoV-2 variants, such as B.1.1.7 (Alpha; first identified in the United Kingdom), B.1.351 (Beta; first identified in South Africa), B.1.1.28 (Gamma; P.1, first identified in Brazil), B.1.617.2 (Delta; first identified in India), B.1.617.1 (Kappa; first identified in India), P.3 (Theta; first identified in the Philippines), and B.1.427 and B.1.429 (Epsilon; first identified in the United States), have been identified 2-5 . This information has been used to update public health policies for the control of COVID-19 infections 6,7 .Considering the benefits of genomic surveillance 6,8 , scientists have pressured countries to increase their sequencing capacity, and this has led to several initiatives, such as COG-UK

Coronovirus disease 2019 (COVID-19) infection, which originated from Wuhan, China, has seized the whole world in its grasp and created a huge pandemic situation before humanity. Since December 2019, genomes of numerous isolates have been sequenced and analyzed for testing confirmation, epidemiology, and evolutionary studies. In the first half of this article, we provide a detailed review of the history and origin of COVID-19, followed by the taxonomy, nomenclature and genome organization of its causative agent Severe Acute Respiratory Syndrome-related Coronavirus-2 (SARS-CoV-2). In the latter half, we analyze subgenus Sarbecovirus (167 SARS-CoV-2, 312 SARS-CoV, and 5 Pangolin CoV) genomes to understand their diversity, origin, and evolution, along with pan-genome analysis of genus Betacoronavirus members. Whole-genome sequence-based phylogeny of subgenus Sarbecovirus genomes reasserted the fact that SARS-CoV-2 strains evolved from their common ancestors putatively residing in bat or pangolin hosts. We predicted a few country-specific patterns of relatedness and identified mutational hotspots with high, medium and low probability based on genome alignment of 167 SARS-CoV-2 strains. A total of 100-nucleotide segment-based homology studies revealed that the majority of the SARS-CoV-2 genome segments are close to Bat CoV, followed by some to Pangolin CoV, and some are unique ones. Open pan-genome of genus Betacoronavirus members indicates the diversity contributed by the novel viruses emerging in this group. Overall, the exploration of the diversity of these isolates, mutational hotspots and pan-genome will shed light on the evolution and pathogenicity of SARS-CoV-2 and help in developing putative methods of diagnosis and treatment.

Genomic surveillance has enabled the identification of several SARS-CoV-2 variants, allowing the formulation of appropriate public health policies. However, surveillance could be made more effective. We have determined that the time taken from strain collection to genome submission for over 1.7 million SARS-CoV-2 strains available at GISAID. We find that strain-wise, time lag in this process ranges from one day to over a year. Country-wise, the UK has taken a median of 16 days (for 417,287 genomes), India took 57 days (for 15,614 genomes), whereas Qatar spent 289 days (for 2298 genomes). We strongly emphasize that along with increasing the number of genomes of COVID-19 positive cases sequenced, their accelerated submission to GISAID should also be strongly encouraged and facilitated. This will enable researchers across the globe to track the spreading of variants in a timely manner; analyse their biology, epidemiology, and re-emerging infections; and define effective public health policies.

Nipah Virus (NiV) came into limelight due to an outbreak in Kerala, India. NiV causes severe disease and death in people with over 75% case fatality rate. It is a public health concern and has the potential to become a global pandemic. Lack of treatment has forced the containment methods to be restricted to isolation and surveillance. WHO’s‘R&D Blueprint list of priority diseases’ (2018) indicates that there is an urgent need for accelerated research & development for addressing NiV.In the quest for druglike NiV inhibitors (NVIs) a thorough literature search followed by systematic data curation was conducted. Rigorous data analysis was done with curated NVIs for prioritizing druglike compounds. For the same, more than 1800 descriptors of NVIs were computed and comparative analysis was performed with the FDA approved small molecules and antivirals. These compounds were further evaluated through PAINS filter to study their toxicity profile. Simultaneously, compounds were also prioritized based on the robustness of the assays through which they were identified. Our efforts lead to the creation of a well-curated structured knowledgebase of 182 NVIs with 98 small molecule inhibitors. The reported IC50/EC50 values for some of these inhibitors are in the nanomolar range – as low as 0.47 nM. In order to prioritize these inhibitors, we performed several tests and applied filters to identify drug-like non-toxic compounds. Of 98, a few compounds passed DruLito & PAINS filters exhibiting drug-like properties and were also prioritized in an independent screen based only the assay robustness. The NVIs have diverse structural features and offer a wide spectrum of ways in which they can be developed further as druglike molecules.We report a knowledgebase for furthering the development of NVIs. The platform has a diverse set of 98 NVIs of which a few have been prioritized based on a combined evidence strategy. The platform has the provision to submit new inhibitors as and when reported by the community for further enhancementof NiV inhibitor landscape.

BackgroundNipah Virus (NiV) came into limelight recently due to an outbreak in Kerala, India. NiV causes severe disease and death in people with over 75% case fatality rate. It is a public health concern and has the potential to become a global pandemic. Lack of treatment has forced the containment methods to be restricted to isolation and surveillance. WHO's 'R&D Blueprint list of priority diseases' (2018) indicates that there is an urgent need for accelerated research & development for addressing NiV. Materials & MethodsIn the quest for druglike NiV inhibitors (NVIs) a thorough literature search followed by systematic data curation was conducted. Rigorous data analysis was done with curated NVIs for prioritizing druglike compounds. For the same, more than 1800 descriptors of NVIs were computed and comparative analysis was performed with the FDA approved small molecules and antivirals. These compounds were further evaluated through PAINS filter to study their toxicity profile. Simultaneously, compounds were also prioritized based on the robustness of the assays through which they were identified. ResultsOur efforts lead to the creation of a well-curated structured knowledgebase of 182 NVIs with 98 small molecule inhibitors. The reported IC50/EC50 values for some of these inhibitors are in the nanomolar range -as low as 0.47 nM. In order to prioritize these inhibitors, we performed several tests and applied filters to identify drug-like non-toxic compounds. Of 98, a few compounds passed DruLito & PAINS filters exhibiting drug-like properties and were also prioritized in an independent screen based only the assay robustness. The NVIs have diverse structural features and offer a wide spectrum of ways in which they can be developed further as druglike molecules. ConclusionWe report a knowledgebase for furthering the development of NVIs. The platform has a diverse set of 98 NVIs of which a few have been prioritized based on a combined evidence strategy. The platform has the provision to submit new inhibitors as and when reported by the community for further enhancement of NiV inhibitor landscape.