Bo Li

TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.

TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.

TL;DR: The study of immune cell transcriptomes from tumors demonstrates a strategy for identifying predictors, mechanisms, and targets for enhancing checkpoint immunotherapy by targeting novel combinations of factors in exhausted cells.

TL;DR: Simulations with the method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed, and the method is capable of modeling non-uniform read distributions.

TL;DR: This protocol describes the use of the Trinity platform for de novo transcriptome assembly from RNA-Seq data in non-model organisms and presents Trinity’s supported companion utilities for downstream applications, including RSEM for transcript abundance estimation and R/Bioconductor packages for identifying differentially expressed transcripts across samples.