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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Mass spectrometry analysis of newly emerging coronavirus HCoV-19 spike S protein and human ACE2 reveals camouflaging glycans and unique post-translational modifications
This article has 9 authors:Reviewed by ScreenIT
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Evaluation of 19 antiviral drugs against SARS-CoV-2 Infection
This article has 4 authors:Reviewed by ScreenIT
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An engineered stable mini-protein to plug SARS-Cov-2 Spikes
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2 Spike S1 Receptor Binding Domain undergoes Conformational Change upon Interaction with Low Molecular Weight Heparins
This article has 17 authors:Reviewed by ScreenIT
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Activity profiling and structures of inhibitor-bound SARS-CoV-2-PLpro protease provides a framework for anti-COVID-19 drug design
This article has 11 authors:Reviewed by ScreenIT
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Glycosaminoglycans induce conformational change in the SARS-CoV-2 Spike S1 Receptor Binding Domain
This article has 17 authors:Reviewed by ScreenIT
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A collection of designed peptides to target SARS-Cov-2 – ACE2 interaction: Pep I -Covid19 database
This article has 3 authors:Reviewed by ScreenIT
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Controlling the SARS-CoV-2 outbreak, insights from large scale whole genome sequences generated across the world
This article has 6 authors:Reviewed by ScreenIT
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Pervasive generation of non-canonical subgenomic RNAs by SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Analyses of spike protein from first deposited sequences of SARS-CoV2 from West Bengal, India
This article has 7 authors:Reviewed by ScreenIT