biOverlay

We started the biOverlay experiment with the goal of testing detailed, portable comments for preprints. One hope was that authors would have access to portable reviews that could be taken to journals. We would then be able to link journal editors with reviewers to help speed papers through the review process with the fewest wasted reviews (i.e., those that are written for journals where a manuscript is ultimately rejected). Our promise to associate editors was that they’d have the opportunity to ask their peers for detailed thoughts on what they found to be the most interesting papers of the moment. »

“Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data” by Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter. https://doi.org/10.1101/534834 I selected this article for review for four reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit. I was curious to learn more about methods related to infer gene regulatory networks using single-cell data. I was curious to learn how the authors were benchmarking their results from the network reconstruction algorithm. »

Update: This paper has now been published at eLife. “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. »

Update: This paper has now been published at Nature Genetics “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. »

Update: This paper has now been published at Nature Biotech “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. »