Algorithms for efficiently collapsing reads with Unique Molecular Identifiers

Abstract

Background. Unique Molecular Identifiers (UMI) are used in many experiments to find and remove PCR duplicates. There are many tools for solving the problem of deduplicating reads based on their finding reads with the same alignment coordinates and UMIs. However, many tools either cannot handle substitution errors, or require expensive pairwise UMI comparisons that do not efficiently scale to larger datasets. Results. We reformulate the problem of deduplicating UMIs in a manner that enables optimizations to be made, and more efficient data structures to be used. We implement our data structures and optimizations in a tool called UMICollapse, which is able to deduplicate over one million unique UMIs of length 9 at a single alignment position in around 26 s, using only a single thread and much less than 10 GB of memory. Conclusions. We present a new formulation of the UMI deduplication problem, and show that it can be solved faster, with more sophisticated data structures.