@article{Derr01102016,
author = {Derr, Alan and Yang, Chaoxing and Zilionis, Rapolas and Sergushichev, Alexey and Blodgett, David M. and Redick, Sambra and Bortell, Rita and Luban, Jeremy and Harlan, David M. and Kadener, Sebastian and Greiner, Dale L. and Klein, Allon and Artyomov, Maxim N. and Garber, Manuel}, 
title = {End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data},
volume = {26}, 
number = {10}, 
pages = {1397-1410}, 
year = {2016}, 
doi = {10.1101/gr.207902.116}, 
abstract ={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 β-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.}, 
URL = {http://genome.cshlp.org/content/26/10/1397.abstract}, 
eprint = {http://genome.cshlp.org/content/26/10/1397.full.pdf+html}, 
journal = {Genome Research} 
}