UMMS Affiliation

Program in Bioinformatics and Integrative Biology; Program in Molecular Medicine; Diabetes Center of Excellence; Department of Medicine

Publication Date

7-28-2016

Document Type

Article

Disciplines

Bioinformatics | Computational Biology | Genomics

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 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

10.1101/gr.207902.116

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Genome research

PubMed ID

27470110

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.