Tracking the popularity and outcomes of all bioRxiv preprints

“Tracking the popularity and outcomes of all bioRxiv preprints” by Richard J. Abdill and Ran Blekhman https://www.biorxiv.org/content/10.1101/515643v1 In this article, Abdill and Blekhman describe a database of bioRxiv preprints and associated data, present various analyses, and introduce a website, Rxivist.org, for sorting bioRxiv preprints based on Twitter activity and PDF downloads. I selected this article for two reasons. First, the analysis received a lot of attention on social media, particularly (1) correlations between Journal Impact Factor and preprint popularity, (2) delays between preprint and journal publication and (3) the fraction of preprints that are eventually published in journals. »

Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism

“Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism” by Jian Zhou, Christopher Park, Chandra Theesfeld, Yuan Yuan, Kirsty Sawicka, Jennifer Darnell, Claudia Scheckel, John Fak, Yoko Tajima, Robert Darnell, Olga Troyanskaya https://doi.org/10.1101/319681 It is well known that Autism has a strong genetic component. In recent years the discovery of genetic factors linked to Autism has skyrocketed, powered by next generation sequencing. Exome sequencing studies have allowed the discovery of hundreds of coding genetic risk variants. »

Panoramic stitching of heterogeneous single-cell transcriptomic data

“Panoramic stitching of heterogeneous single-cell transcriptomic data” by Brian L Hie, Bryan Bryson, Bonnie Berger. https://doi.org/10.1101/371179 I selected this article for review for three reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit. Methods that can integrate two or more scRNA-seq data sets (across experiments, conditions, treatments, etc) are in high demand and are actively being worked on by many groups. I was intrigued about the idea of drawing “inspiration from computer vision algorithms for panorama stitching that identify images with … overlapping content, which are then merged into a larger panorama”. »

Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0

“Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0” Dylan M Nielson, Francisco Pereira, Charles Y Zheng, Nino Migineishvili, John A Lee, Adam G Thomas, Peter A Bandettini bioRxiv 309260; doi: https://doi.org/10.1101/309260 I have selected the manuscript this article to be featured in biOverlay because it discusses an increasingly important topic of statistical issues involved in combining data from multiple sources. The authors used a large dataset produced by the ABCD consortium that spans multiple sites and scanners. »

Analysis and Correction of Inappropriate Image Duplication: The Molecular and Cellular Biology Experience

“Analysis and Correction of Inappropriate Image Duplication: The Molecular and Cellular Biology Experience” by Arturo Casadevall, Elisabeth M Bik, Ferric C Fang, Amy Kullas, Roger J Davis. https://doi.org/10.1101/354621 [Ed note: we’re using the bioRxiv listing information because that’s what we sent for review, but the author order has changed on the published paper and on that Dr. Bik is first author.] I selected this article for review for a few reasons: »