TY  - JOUR
A1  - Smith, Tom Sean
A1  - Heger, Andreas
A1  - Sudbery, Ian
T1  - UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
Y1  - 2017/01/18 
JF  - Genome Research 
JO  - Genome Research 
DO  - 10.1101/gr.209601.116 
SP  - gr.209601.116 
UR  - http://genome.cshlp.org/content/early/2017/01/18/gr.209601.116.abstract 
N2  - 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. 
ER  -