Program in Bioinformatics and Integrative Biology; Program in Molecular Medicine; Diabetes Center of Excellence; Department of Medicine
Bioinformatics | Computational Biology | Genomics
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 1,000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified 9 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.
Rights and Permissions
The version of the article available for download is the authors' final, peer-reviewed accepted manuscript as prepared for publication in Citation: Genome Res. 2016 Jul 28. pii: gr.207902.116. Link to article on publisher's site
This manuscript is Open Access. This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
DOI of Published Version
Derr, Alan G.; Yang, Chaoxing; Zilionis, Rapolas; Sergushichev, Alexey; Blodgett, David; Redick, Sambra D.; Bortell, Rita; Luban, Jeremy; Harlan, David; Kadener, Sebastian; Greiner, Dale L.; Klein, Allon; Artyomov, Maxim; and Garber, Manuel, "End Sequence Analysis ToolKit (ESAT) expands the extractable from single cell RNA-seq experiments" (2016). Open Access Articles. 2781.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License