Leveraging multiple transcriptome assembly methods for improved gene structure annotation.

Background: The performance of RNA sequencing (RNA-seq) aligners and assemblers varies greatly across different organisms and experiments, and often the optimal approach is not known beforehand. Results: Here, we show that the accuracy of transcript reconstruction can be boosted by combining multiple methods, and we present a novel algorithm to integrate multiple RNA-seq assemblies into a coherent transcript annotation. Our algorithm can remove redundancies and select the best transcript models according to user-specified metrics, while solving common artifacts such as erroneous transcript chimerisms. Conclusions: We have implemented this method in an open-source Python3 and Cython program, Mikado, available on GitHub.