Thermostable group II intron reverse transcriptases (TGIRTs) with high fidelity and processivity have been used for a variety of RNA sequencing (RNA-seq) applications, including comprehensive profiling of whole-cell, exosomal, and human plasma RNAs; quantitative tRNA-seq based on the ability of TGIRT enzymes to give full-length reads of tRNAs and other structured small ncRNAs; high-throughput mapping of post-transcriptional modifications; and RNA structure mapping. Here, we improved TGIRT-seq methods for comprehensive transcriptome profiling by rationally designing RNA-seq adapters that minimize adapter dimer formation. Additionally, we developed biochemical and computational methods for remediating 5'- and 3'-end biases, the latter based on a random forest regression model that provides insight into the contribution of different factors to these biases. These improvements, some of which may be applicable to other RNA-seq methods, increase the efficiency of TGIRT-seq library construction and improve coverage of very small RNAs, such as miRNAs. Our findings provide insight into the biochemical basis of 5'- and 3'-end biases in RNA-seq and suggest general approaches for remediating biases and decreasing adapter dimer formation.