TY  - JOUR
A1  - Derr, Alan
A1  - Yang, Chaoxing
A1  - Zilionis, Rapolas
A1  - Sergushichev, Alexey
A1  - Blodgett, David M.
A1  - Redick, Sambra
A1  - Bortell, Rita
A1  - Luban, Jeremy
A1  - Harlan, David M.
A1  - Kadener, Sebastian
A1  - Greiner, Dale L.
A1  - Klein, Allon
A1  - Artyomov, Maxim N.
A1  - Garber, Manuel
T1  - End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data
Y1  - 2016/10/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1397 
EP  - 1410 
DO  - 10.1101/gr.207902.116 
VL  - 26 
IS  - 10 
UR  - http://genome.cshlp.org/content/26/10/1397.abstract 
N2  - 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. 
ER  -