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End Sequence Analysis ToolKit (ESAT) expands the extractable information from single cell RNA-Seq data

ABSTRACT: End Sequence Analysis ToolKit (ESAT) expands the extractable information from single cell RNA-Seq data

PROVIDER: PRJNA316604 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	SRR3306070_1.fastq.gz	Fastqsanger.gz
	SRR3306070_2.fastq.gz	Fastqsanger.gz
	SRR3306080_1.fastq.gz	Fastqsanger.gz
	SRR3306080_2.fastq.gz	Fastqsanger.gz
	SRR3306071_1.fastq.gz	Fastqsanger.gz

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End Sequence Analysis ToolKit (ESAT) expands the extractable information from single cell RNA-Seq data

Project description:Test data used for the evaluation of ESAT performance and results files for data from 3' and 5' end-sequencing RNA-Seq protocols and droplet-based single-cell RNA-Seq. Quantification and analysis of Tophat-aligned (v2.0.9) samples from mouse bone-marrow derived dendritic cells (mBMDC) timecourse (0, 2, 4 and 6 hours) post LPS stimulation and non-diabetic BBDR rat pancreatic islet cells. Since end-sequencing (3' or 5') is used for all samples, alignments are only required for the R2 (sequence-containing) read.

2016-07-27 | E-GEOD-79651 | biostudies-arrayexpress

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End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data.

Project description: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.

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