Rachel S. Schwartz

The paper assesses challenges in reusing scRNA-seq data from GEO, finding that only ~40% provide usable processed data and <10% include cell-type annotations.

This study uses machine learning to predict locus phylogenetic utility, improving topological accuracy and statistical support by filtering out noisy loci from phylogenomic datasets.

The paper profiles phylogenetic and non-phylogenetic signal across mammalian genomes and finds non-coding loci provide more overall signal and a higher proportion of phylogenetic signal than coding loci.