End Sequence Analysis ToolKit (ESAT) – a computational pipeline for single cell RNA-seq experiments

RNA-seq protocols that focus on transcript termini are well-suited for applications in which template quantity is limiting. Here researchers from UMass Medical School show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this they created the End Sequence Analysis Toolkit (ESAT). As a test, the researchers 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 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.

rna-seqSchematic representation of the ESAT pipeline

Availability – The analysis toolkit and its source code are freely available from: https://github.com/garber­lab/ESAT

Derr A, Yang C, Zilionis R, Sergushichev A, Blodgett DM, Redick S, Bortell R, Luban J, Harlan DM, Kadener S, Greiner DL, Klein A, Artyomov M, Garber M. (2016) End Sequence Analysis ToolKit (ESAT) expands the extractable from single cell RNA-seq experiments. Genome Res [Epub ahead of print]. [article]

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