Sequence census methods reduce molecular measurements to counting problems via DNA sequencing. For example, in RNA-Seq experiments for measuring gene expression, RNA molecules are converted to cDNA molecules, which are subsequently sequenced. The sequenced fragments are then mapped back to transcripts to reveal their abundances. More advanced protocols are used to infer RNA secondary and tertiary structure by explicitly inducing changes in the composition of the molecule pool, from which structural features may be inferred. In such protocols, both the abundance and location of fragments are important in making biological inference. In this talk, I will describe the biases that are inherent to these protocols and the challenges in converting their raw outputs into meaningful data that can be used to constrain existing RNA structure prediction programs.