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
-
A ‘deep dive’ into the SARS-Cov-2 polymerase assembly: identifying novel allosteric sites and analyzing the hydrogen bond networks and correlated dynamics
This article has 4 authors:Reviewed by ScreenIT
-
Analyzing hCov Genome Sequences: Predicting Virulence and Mutation
This article has 8 authors:Reviewed by ScreenIT
-
SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes
This article has 3 authors:Reviewed by ScreenIT
-
Closing coronavirus spike glycoproteins by structure-guided design
This article has 4 authors:Reviewed by ScreenIT
-
Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping
This article has 16 authors:Reviewed by ScreenIT
-
Naturally occurring SARS-CoV-2 gene deletions close to the spike S1/S2 cleavage site in the viral quasispecies of COVID19 patients
This article has 19 authors:Reviewed by ScreenIT
-
Temporal evolution and adaptation of SARS-COV-2 codon usage
This article has 6 authors:Reviewed by ScreenIT
-
Evaluation of Serological Tests for SARS-CoV-2: Implications for Serology Testing in a Low-Prevalence Setting
This article has 15 authors:Reviewed by ScreenIT
-
Critical Analysis of the Possible Determining Factors of COVID-19 Epidemiological Trend in Bangladesh: A Review
This article has 2 authors:Reviewed by ScreenIT
-
Kalman filter based short term prediction model for COVID-19 spread
This article has 4 authors:Reviewed by ScreenIT