One sentence summary: UMI-Reducer is a computational method allowing to differentiate PCR duplicates from biological duplicates based on the UMIs (Unique Molecular Identifiers)
This is a collaboration with Kelsey Martin Lab, David Geffen School of Medicine, Department of Biological Chemistry, UCLA.
Every sequencing library contains duplicate reads. While many duplicates arise during polymerase chain reaction (PCR), some of these duplicates derive from multiple identical fragments of mRNA present in the original lysate (termed “biological duplicates”). Unique Molecular Identifiers (UMIs) are random oligonucleotide sequences that allow differentiation between technical and biological duplicates.
UMI-Reducer, a new computational tool for processing and differentiating PCR duplicates from biological duplicates. UMI-Reducer uses UMIs and the mapping position of the read to identify and collapse reads that are technical duplicates. Remaining true biological reads are further used for bias-free estimate of mRNA abundance in the original lysate. This strategy is of particular use for libraries made from low amounts of starting material, which typically require additional cycles of PCR.
UMI-Reducer tutorial is available here
Mangul, Serghei, et al. “UMI-Reducer: Collapsing duplicate sequencing reads via Unique Molecular Identifiers.” bioRxiv (2017)
UMI-Reducer 0.2 release 05/16/2017
- –m option for collapsePCRduplicates.py allowing to save multi-mapped reads. Previously multi-mapped reads were discarded
- Now we are distributing gprofile.py and gprofilePlus.py from ROP (v1.0.6) allowing to categorize reads into genomic categories. Multi-mapped reads are randomly assigned considering mRNA abundances
UMI-Reducer 0.1 release 05/13/2016
UMI-Reducer 0.1 is available for download here. The first public release of UMI-Reducer. Because this is the first release, the manual is very limited. Only the basic options have been described, but we plan to update it frequently. If you have any questions about how UMI-Reducer works, please contact Serghei Mangul (email@example.com).