End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data
Authors
Derr, Alan G.Yang, Chaoxing
Zilionis, Rapolas
Sergushichev, Alexey
Blodgett, David
Redick, Sambra D.
Bortell, Rita
Luban, Jeremy
Harlan, David M.
Kadener, Sebastian
Greiner, Dale L.
Klein, Allon
Artyomov, Maxim N.
Garber, Manuel
UMass Chan Affiliations
Department of MedicineProgram in Molecular Medicine, Diabetes Center of Excellence
Program in Bioinformatics and Integrative Biology
Document Type
Journal ArticlePublication Date
2016-10-01Keywords
UMCCTS fundingBiochemistry
Bioinformatics
Computational Biology
Genomics
Integrative Biology
Molecular Biology
Molecular Genetics
Metadata
Show full item recordAbstract
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.Source
Genome Res. 2016 Oct;26(10):1397-1410. Epub 2016 Jul 28. Link to article on publisher's site
DOI
10.1101/gr.207902.116Permanent Link to this Item
http://hdl.handle.net/20.500.14038/44481PubMed ID
27470110Related Resources
Rights
© 2016 Derr et al. This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.Distribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.1101/gr.207902.116
Scopus Count
Except where otherwise noted, this item's license is described as © 2016 Derr et al. This article, published in Genome Research, is available under
a Creative Commons License (Attribution 4.0 International), as described at
http://creativecommons.org/licenses/by/4.0/.