Smith, T.S., Heger, A. and Sudbery, I. orcid.org/0000-0002-5038-0190 (2017) UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research, 27. pp. 491-499. ISSN 1088-9051
Abstract
Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes that are increasingly used in high-throughput sequencing experiments. Through a UMI, identical copies arising from distinct molecules can be distinguished from those arising through PCR-amplification of the same molecule. However, bioinformatic methods to leverage the information from UMIs have yet to be formalised. In particular, sequencing errors in the UMI sequence are often ignored, or else resolved in an ad-hoc manner. We show that errors in the UMI sequence are common and introduce network based methods to account for these errors when identifying PCR duplicates. Using these methods, we demonstrate improved quantification accuracy both under simulated conditions and real iCLIP and single cell RNA-Seq datasets. Reproducibility between iCLIP replicates and single cell RNA Seq clustering are both improved using our proposed network-based method, demonstrating the value of properly accounting for errors in UMIs. These methods are implemented in the open source UMI-tools software package.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Authors. This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International license), as described at http://creativecommons.org/licenses/by/4.0/. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Molecular Biology and Biotechnology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jan 2017 15:05 |
Last Modified: | 29 Jun 2023 15:54 |
Status: | Published |
Publisher: | Cold Spring Harbor Laboratory Press |
Refereed: | Yes |
Identification Number: | 10.1101/gr.209601.116 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111047 |