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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Unravelling the debate on heme effects in COVID-19 infections
This article has 8 authors:Reviewed by ScreenIT
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Ultrastructural analysis of SARS-CoV-2 interactions with the host cell via high resolution scanning electron microscopy
This article has 9 authors:Reviewed by ScreenIT
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A potent synthetic nanobody targets RBD and protects mice from SARS-CoV-2 infection
This article has 19 authors:Reviewed by ScreenIT
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One-step rapid quantification of SARS-CoV-2 virus particles via low-cost nanoplasmonic sensors in generic microplate reader and point-of-care device
This article has 11 authors:Reviewed by ScreenIT
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Comparative analysis of non structural protein 1 of SARS-CoV2 with SARS-CoV1 and MERS-CoV: An in silico study
This article has 1 author:Reviewed by ScreenIT
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Whole Genome Sequencing of SARS-CoV-2: Adapting Illumina Protocols for Quick and Accurate Outbreak Investigation during a Pandemic
This article has 17 authors:Reviewed by ScreenIT
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Designed peptides as potential fusion inhibitors against SARA-CoV-2 coronavirus infection
This article has 6 authors:Reviewed by ScreenIT
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Differential expression of COVID-19-related genes in European Americans and African Americans
This article has 2 authors:Reviewed by ScreenIT
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A Rare Deletion in SARS-CoV-2 ORF6 Dramatically Alters the Predicted Three-Dimensional Structure of the Resultant Protein
This article has 12 authors:Reviewed by ScreenIT
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SARS-CoV-2 sequence typing, evolution and signatures of selection using CoVa, a Python-based command-line utility
This article has 3 authors:Reviewed by ScreenIT