ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues.

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State: Public
Version: Final published version
Serval ID
serval:BIB_66C286C8031F
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
ROP: dumpster diving in RNA-sequencing to find the source of 1 trillion reads across diverse adult human tissues.
Journal
Genome Biology
Author(s)
Mangul S., Yang H.T., Strauli N., Gruhl F., Porath H.T., Hsieh K., Chen L., Daley T., Christenson S., Wesolowska-Andersen A., Spreafico R., Rios C., Eng C., Smith A.D., Hernandez R.D., Ophoff R.A., Santana J.R., Levanon E.Y., Woodruff P.G., Burchard E., Seibold M.A., Shifman S., Eskin E., Zaitlen N.
ISSN
1474-760X (Electronic)
ISSN-L
1474-7596
Publication state
Published
Issued date
2018
Peer-reviewed
Oui
Volume
19
Number
1
Pages
36
Language
english
Abstract
High-throughput RNA-sequencing (RNA-seq) technologies provide an unprecedented opportunity to explore the individual transcriptome. Unmapped reads are a large and often overlooked output of standard RNA-seq analyses. Here, we present Read Origin Protocol (ROP), a tool for discovering the source of all reads originating from complex RNA molecules. We apply ROP to samples across 2630 individuals from 54 diverse human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Additionally, we use ROP to investigate the functional mechanisms underlying connections between the immune system, microbiome, and disease. ROP is freely available at https://github.com/smangul1/rop/wiki .

Pubmed
Web of science
Open Access
Yes
Create date
22/03/2018 20:17
Last modification date
20/08/2019 15:22
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