Keep up with the latest coronavirus research

Chen, Qingyu; Allot, Alexis; Lu, Zhiyong

doi:10.1038/d41586-020-00694-1

Cited by 265 publications

(251 citation statements)

References 0 publications

Supporting

Mentioning

245

Contrasting

Unclassified

Order By: Relevance

“…Another important feature is that it is open source, in addition to being updated daily. This hub is also notable for features such as importing records in two ways, RIS and TSV formats, categorizing articles according to subject areas, and having a link for text and data mining, which researchers find useful (4). Of course, publishers like Nature have also categorized and published their publishers' articles, but the comprehensiveness of this hub is much greater (5).…”

mentioning

confidence: 99%

Features and Limitations of LitCovid Hub for Quick Access to Literature About COVID-19

Arab‐Zozani¹,

Hassanipour²

2020

Balkan Med J

View full text Add to dashboard Cite

mentioning

confidence: 99%

Features and Limitations of LitCovid Hub for Quick Access to Literature About COVID-19

Arab‐Zozani¹,

Hassanipour²

2020

Balkan Med J

View full text Add to dashboard Cite

“…At the time of writing, an extensive amount of coronavirus related articles have been published since the virus' outbreak (indicatively, our collected data contain about 4, 636 articles published in 2020). Moreover, the number of weekly publications is growing, reaching up to 1, 026 publications in the week from April 6 th to April 12 th (based on the statistics presented in [2]). Taking additionally into account previous literature on coronaviruses and related diseases, it is evident that there is a vast literature on the subject.…”

Section: Background and Summarymentioning

confidence: 99%

“…The selected measures were chosen so as to cover different impact aspects of the articles. Furthermore, to select a representative set of publications, we rely on two open datasets of COVID-19-related articles: the CORD-19 [3] and the LitCovid [2] datasets. BIP4COVID19 data are updated on a regular basis and are openly available on Zenodo [1].…”

Section: Background and Summarymentioning

confidence: 99%

See 1 more Smart Citation

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Vergoulis

Kanellos

Chatzopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or, even, government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Finally, we provide a publicly accessible web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

show abstract

“…In this scenario, it is expected that diverse signatures of viral variants spread among different populations in the world. Recently, thousands of GenBank sequences from SARS-CoV-19 available at the NCBI virus database were trackable by region, suggesting that the transmission occurred mainly through clonal events due to clustering of the available sequences (Chen et al 2020; Kupferschmidt 2020) (https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Protein&VirusLineage_ss=SARS-CoV-2,%20taxid:2697049). As a proof of concept, in the early beginning of the outbreak in China, sequencing the virus from nine patients from Wuhan in China revealed 99.9% similarity among samples.…”

Section: Introductionmentioning

confidence: 99%

Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions

Farkas

Fuentes-Villalobos

Garrido³

et al. 2020

Preprint

View full text Add to dashboard Cite

Here we aim to describe early mutational events across samples from publicly available SARS-CoV-2 sequences from the sequence read archive repository. Up until March 27, 2020, we downloaded 53 illumina datasets, mostly from China, USA (Washington DC) and Australia (Victoria). Of 30 high quality datasets, 27 datasets (90%) contain at least a single founder mutation and most of the variants are missense (over 63%). Five-point mutations with clonal (founder) effect were found in USA sequencing samples. Sequencing samples from USA in GenBank present this signature with 50% allele frequencies among samples. Australian mutation signatures were more diverse than USA samples, but still, clonal events were found in those samples. Mutations in the helicase and orf1a coding regions from SARS-CoV-2 were predominant, among others, suggesting that these proteins are prone to evolve by natural selection. Finally, we firmly urge that primer sets for diagnosis be carefully designed, since rapidly occurring variants would affect the performance of the reverse transcribed quantitative PCR (RT-qPCR) based viral testing.

show abstract

Keep up with the latest coronavirus research

Abstract: Correspondence© 2 0 2 0 S p r i n g e r N a t u r e L i m i t e d . A l l r i g h t s r e s e r v e d .

Cited by 265 publications

References 0 publications

Features and Limitations of LitCovid Hub for Quick Access to Literature About COVID-19

Features and Limitations of LitCovid Hub for Quick Access to Literature About COVID-19

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions

Contact Info

Product

Resources

About