Title UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy Running Title Modelling UMI errors improves quantification accuracy Authors

Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes that are increasingly used in high-throughout 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 in 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 UMItools software package (https://github.com/CGATOxford/UMI-tools). . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/051755 doi: bioRxiv preprint first posted online May. 9, 2016;