RNA-sequencing is a technique to study RNA expression in biological material. It is quickly gaining popularity in the field of transcriptomics. Trinity is a software tool that was developed for efficient de novo reconstruction of transcriptomes from RNA-Seq data. In this paper we first conduct a performance study of Trinity and compare it to previously published data from 2011. The version from 2011 is much slower than many other de novo assemblers and biologists have thus been forced to choose between quality and speed. We examine the runtime behavior of Trinity as a whole as well as its individual components and then optimize the most performance critical parts. We find that standard best practices for HPC applications can also be applied to Trinity, especially on systems with large amounts of memory. When combining best practices for HPC applications along with our specific performance optimization, we can decrease the runtime of Trinity by a factor of 3.9. This brings the runtime of Trinity in line with other de novo assemblers while maintaining superior quality. The purpose of this paper is to describe a series of improvements to Trinity, quantify the execution improvements achieved, and document the new version of the software.