End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data
Description
<jats:p>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.</jats:p>
Journal
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- Genome Research
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Genome Research 26 (10), 1397-1410, 2016-07-28
Cold Spring Harbor Laboratory
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Details 詳細情報について
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- CRID
- 1362262945648305920
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- ISSN
- 15495469
- 10889051
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- Data Source
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- Crossref