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Front. Genet. | doi: 10.3389/fgene.2019.00150

Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques

Eric R. Reed1, 2,  Elizabeth Moses2, Xiaohui Xiao2, Gang Liu2,  Joshua Campbell1, 2, Catalina Perdomo2 and  Stefano Monti1, 2*
  • 1Bioinformatics program, Boston University, United States
  • 2Section of Computational Biomedicine, School of Medicine, Boston University, United States

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine multiple samples into a single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq, microarrays, as well as another low-cost highly multiplexed technique known as 3’ digital gene expression (3’DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3’DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

Keywords: RNA sequencing (RNA-Seq), Gene Expression, Microarray, multiplexing, Platform comparison

Received: 06 Sep 2018; Accepted: 12 Feb 2019.

Edited by:

Filippo Geraci, Italian National Research Council (CNR), Italy

Reviewed by:

Kashmir Singh, Panjab University, Chandigarh, India
Matteo Benelli, University of Trento, Italy
Haibo Liu, Iowa State University, United States  

Copyright: © 2019 Reed, Moses, Xiao, Liu, Campbell, Perdomo and Monti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Stefano Monti, Bioinformatics program, Boston University, Boston, 02215, Massachusetts, United States,