Tree-based differential testing using inferential uncertainty for RNA-Seq.

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. Given a set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves.

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.

Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty.

Aided recall of e-cigarette prevention advertisements among a nationally representative sample of adolescents.

E-cigarette use among youth remains a significant public health concern. In 2018, campaign began disseminating messages about the harms of vaping, primarily using digital media. We sought to determine the prevalence of aided recall of e-cigarette prevention ads and identify potential differences by participant characteristics.