  Despite great interest in solving RNA secondary structures due to their
impact on function, it remains an open problem to determine structure from
sequence. Among experimental approaches, a promising candidate is the "chemical
modification strategy", which involves application of chemicals to RNA that are
sensitive to structure and that result in modifications that can be assayed via
sequencing technologies. One approach that can reveal paired nucleotides via
chemical modification followed by sequencing is SHAPE, and it has been used in
conjunction with capillary electrophoresis (SHAPE-CE) and high-throughput
sequencing (SHAPE-Seq). The solution of mathematical inverse problems is needed
to relate the sequence data to the modified sites, and a number of approaches
have been previously suggested for SHAPE-CE, and separately for SHAPE-Seq
analysis. Here we introduce a new model for inference of chemical modification
experiments, whose formulation results in closed-form maximum likelihood
estimates that can be easily applied to data. The model can be specialized to
both SHAPE-CE and SHAPE-Seq, and therefore allows for a direct comparison of
the two technologies. We then show that the extra information obtained with
SHAPE-Seq but not with SHAPE-CE is valuable with respect to ML estimation.
