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

Serghei Mangul*, Harry Taegyun Yang, Nicolas Strauli, Franziska Gruhl, Hagit T. Porath, Kevin Hsieh, Linus Chen, Timothy Daley, Stephanie Christenson, Agata Wesolowska-Andersen, Roberto Spreafico, Cydney Rios, Celeste Eng, Andrew D. Smith, Ryan D. Hernandez, Roel A. Ophoff, Jose Rodriguez Santana, Erez Y. Levanon, Prescott G. Woodruff, Esteban BurchardMax A. Seibold, Sagiv Shifman, Eleazar Eskin, Noah Zaitlen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

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.

Original languageEnglish
Article number36
JournalGenome Biology
Volume19
Issue number1
DOIs
StatePublished - 15 Feb 2018

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

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