UCHIME2: improved chimera prediction for amplicon sequencing

Abstract

Amplicon sequencing generates chimeric reads which can cause spurious inferences of biological variation. I describe UCHIME2, an update of the popular UCHIME chimera detection algorithm with new modes optimized for high-resolution biological sequence reconstruction (“denoising”) and other applications. I show that chimera frequency correlates inversely with divergence, that error-free chimera prediction from sequence is impossible in principle, and that UCHIME2 achieves higher detection accuracy than previous methods.