ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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A scientometric overview of CORD-19
This article has 6 authors:Reviewed by ScreenIT
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Leveraging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences
This article has 1 author:Reviewed by ScreenIT
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“Identification and enrichment of SECReTE cis -acting RNA elements in the Coronaviridae and other (+) single-strand RNA viruses”
This article has 4 authors:Reviewed by ScreenIT
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The crystal structure of nsp10-nsp16 heterodimer from SARS-CoV-2 in complex with S-adenosylmethionine
This article has 14 authors:Reviewed by ScreenIT
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Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions
This article has 79 authors:Reviewed by ScreenIT
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SARS-coronavirus-2 replication in Vero E6 cells: replication kinetics, rapid adaptation and cytopathology
This article has 12 authors:Reviewed by ScreenIT
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Blocking antibodies against SARS-CoV-2 RBD isolated from a phage display antibody library using a competitive biopanning strategy
This article has 12 authors:Reviewed by ScreenIT
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Design an efficient multi-epitope peptide vaccine candidate against SARS-CoV-2: An in silico analysis
This article has 4 authors:Reviewed by ScreenIT
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Protocol and reagents for pseudotyping lentiviral particles with SARS-CoV-2 Spike protein for neutralization assays
This article has 14 authors:Reviewed by ScreenIT
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Phylodynamics of SARS-CoV-2 transmission in Spain
This article has 14 authors:Reviewed by ScreenIT