UMMS Affiliation

Program in Bioinformatics and Integrative Biology; RNA Therapeutics Institute; Department of Biochemistry and Molecular Pharmacology

Publication Date


Document Type



Bioinformatics | Computational Biology | Genomics | Integrative Biology | Molecular Biology | Systems Biology


BACKGROUND: RNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules.

RESULTS: We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries.

CONCLUSIONS: Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.


PCR cycle, PCR duplicates, RNA-seq, Ribognome, Sequencing depth, Small RNA-seq, Starting material, Transcriptome, UMI, Unique molecular identifier

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© The Author(s). 2018 Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

DOI of Published Version



BMC Genomics. 2018 Jul 13;19(1):531. doi: 10.1186/s12864-018-4933-1. Link to article on publisher's site

Journal/Book/Conference Title

BMC genomics

Related Resources

Link to Article in PubMed

PubMed ID


Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.