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
-
Simulation of the omicron variant of SARS-CoV-2 shows broad antibody escape, weakened ACE2 binding, and modest increase in furin binding
This article has 5 authors:Reviewed by ScreenIT
-
Long-Read RNA Sequencing Identifies Polyadenylation Elongation and Differential Transcript Usage of Host Transcripts During SARS-CoV-2 In Vitro Infection
This article has 12 authors:Reviewed by ScreenIT
-
Genomic determinants of Furin cleavage in diverse European SARS-related bat coronaviruses
This article has 9 authors:Reviewed by ScreenIT
-
Evidence for a mouse origin of the SARS-CoV-2 Omicron variant
This article has 6 authors:Reviewed by ScreenIT
-
Language models for the prediction of SARS-CoV-2 inhibitors
This article has 10 authors:Reviewed by ScreenIT
-
Mucosal and systemic responses to SARS-CoV-2 vaccination in infection naïve and experienced individuals
This article has 15 authors:Reviewed by ScreenIT
-
Isolation and comparative analysis of antibodies that broadly neutralize sarbecoviruses
This article has 30 authors:Reviewed by ScreenIT
-
Scanning the RBD-ACE2 molecular interactions in Omicron variant
This article has 3 authors:Reviewed by ScreenIT
-
In silico evidence of superantigenic features in ORF8 protein from COVID-19
This article has 3 authors:Reviewed by ScreenIT
-
Potential cross-protection against SARS-CoV-2 from previous exposure to bovine coronavirus
This article has 3 authors:Reviewed by ScreenIT