The problem of bias resulting from biological and technological sources is one that must be consistently reexamined alongside the development of new technologies and methods of analysis. This dissertation addresses the complexities of bias identification, quantification, and correction in RNA-Seq studies that rely on next generation sequencing technologies and associated analysis methodologies. We identify the cause of GC bias resulting from mRNA secondary structure, examine the problem of non-native reference genome usage when comparing closely related bacterial strains, and provide simulation software that makes it possible to separate the sources of bias and to control for specific factors.