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Hidden Treasures in Contemporary RNA Sequencing

  • Serghei MangulEmail author
  • Harry Taegyun Yang
  • Eleazar Eskin
  • Noah Zaitlen
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

High throughput RNA sequencing technologies have provided unprecedented opportunity to explore the individual transcriptome. Unmapped reads, the reads falling to map to the human reference, are a large and often overlooked output of standard RNA-Seq analyses; the hidden treasure in the contemporary RNA-Seq analysis is within the unmapped reads, illuminating previously unexplored biological insights. Here we develop Read Origin Protocol (ROP) to discover the source of all reads originating from complex RNA molecules, recombinant T and B cell receptors, and microbial communities. We applied ROP to 10,641 samples across 2630 individuals from 54 diverse adult human tissues. Our approach can account for 99.9% of 1 trillion reads of various read length. Using in-house RNA-Seq data, we show that immune profiles of asthmatic individuals are significantly different from the profiles of control individuals, with decreased average per sample T and B cell receptor diversity. We also show that microbiomes can be detected in human bloods via RNA-Sequencing and may elucidate important clinical changes in patients with schizophrenia. Furthermore, we demonstrate that receptor-derived reads among other hidden reads can be used to characterize the overall Ig repertoire across diverse human tissues using RNA-Sequencing. Our results demonstrate the potential of ROP to exploit the hidden treasure in contemporary RNA-Sequencing in order to better understand the functional mechanisms underlying connections between the immune system, microbiome, human gene expression, and disease etiology.

Keywords

RNA Sequencing B and T cell receptor immune repertoires Human Microbiome Unmapped reads Immune system RNA aligners 

Notes

Acknowledgements

We thank Loes M Olde Loohuis, Igor Mandric, Nicolas Strauli, Franziska Gruhl, Hagit T. Porath, Dennis Montoya, Jeremy Rotman, Kevin Hsieh, Linus Chen, Will Van Der Wey, Jiem R. Ronas, Timothy Daley, Stephanie Christenson, Benjamin Statz, Douglas Yao, 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 Burchard, Max A. Seibold, Sagiv Shifman, Anil P.S. Ori, Guillaume Jospin, David Koslicki, Timothy Wu, Marco P. Boks, Catherine Lomen-Hoerth, Martina Wiedau-Pazos, Roberto Spreafico, K. Mark Ansel, Rita M. Cantor, Willem M. de Vos, René S. Kahn, Maura Rossetti, and Alex Zelikovsky for their help in this work.

Disclosure

The authors declare no competing interests.

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Serghei Mangul
    • 1
    Email author
  • Harry Taegyun Yang
    • 2
  • Eleazar Eskin
    • 3
  • Noah Zaitlen
    • 4
  1. 1.Department of Computer Science, Institute for Quantitative and Computational BiosciencesUniversity of California Los AngelesLos AngelesUSA
  2. 2.Department of Computer ScienceUniversity of California Los AngelesLos AngelesUSA
  3. 3.Department of Computer Science, Department of Human GeneticsUniversity of California Los AngelesLos AngelesUSA
  4. 4.Division of Pulmonary, Critical Care, Sleep and Allergy, Department of Medicine, Cardiovascular Research InstituteUniversity of CaliforniaSan FranciscoUSA

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