Cells regulate biological responses in part through changes in transcription start sites (TSS) or cleavage and polyadenylation sites (PAS). To fully understand gene regulatory networks, it is therefore critical to accurately annotate cell type-specific TSS and PAS. Here we present a simple and straightforward approach for genome-wide annotation of 5- and 3-RNA ends. Our approach reliably discerns bona fide PAS from false PAS that arise due to internal poly(A) tracts, a common problem with current PAS annotation methods. We applied our methodology to study the impact of temperature on the Drosophila melanogaster head transcriptome. We found hundreds of previously unidentified TSS and PAS which revealed two interesting phenomena: first, genes with multiple PASs tend to harbor a motif near the most proximal PAS, which likely represents a new cleavage and polyadenylation signal. Second, motif analysis of promoters of genes affected by temperature suggested that boundary element association factor of 32 kDa (BEAF-32) and DREF mediates a transcriptional program at warm temperatures, a result we validated in a fly line where beaf-32 is downregulated. These results demonstrate the utility of a high-throughput platform for complete experimental and computational analysis of mRNA-ends to improve gene annotation.

RNA-seq protocols that focus on transcript termini are well suited for applications in which template quantity is limiting. Here we show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this, we created the End Sequence Analysis Toolkit (ESAT). As a test, we first compared end-sequencing and bulk RNA-seq using RNA from dendritic cells stimulated with lipopolysaccharide (LPS). As predicted by the telescripting model for transcriptional bursts, ESAT detected an LPS-stimulated shift to shorter 3'-isoforms that was not evident by conventional computational methods. Then, droplet-based microfluidics was used to generate 1000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified nine distinct cell types, three distinct beta-cell types, and a complex interplay between hormone secretion and vascularization. ESAT, then, offers a much-needed and generally applicable computational pipeline for either bulk or single-cell RNA end-sequencing.