Over the last two decades, the field of molecular biology has witnessed a revolution due to the development of next generation sequencing (NGS) technologies. NGS enables researchers to routinely generate huge amounts of data that can be used to pursue a large variety of questions in diverse biological systems. The development of these techniques has propelled the emergence of a sub-discipline within computational biology that is concerned with developing methods and statistical models to derive quantitative information from the complex and often indirect data that are generated by NGS. Often, NGS analysis results in particular patterns per biological entity that can be exploited to estimate quantitative parameters of biological interest. Here, I define read feature models (RFMs) as a general framework for such data. RFMs entail global, genome-wide parameters as well as parameters per biological entity, suggesting a two-step procedure for parameter estimation. I describe the analysis of metabolic RNA labeling data as an example of an RFM and analyze and discuss the merits and shortcomings of the two-step estimation.