Models for transcript quantification from RNA-Seq

RNA-Seq is rapidly becoming the standard technology for transcriptome analysis. Fundamental to many of the applications of RNA-Seq is the quantification problem, which is the accurate measurement of relative transcript abundances from the sequenced reads. We focus on this problem, and review many recently published models that are used to estimate the relative abundances. In addition to describing the models and the different approaches to inference, we also explain how methods are related to each other. A key result is that we show how inference with many of the models results in identical estimates of relative abundances, even though model formulations can be very different. In fact, we are able to show how a single general model captures many of the elements of previously published methods. We also review the applications of RNA-Seq models to differential analysis, and explain why accurate relative transcript abundance estimates are crucial for downstream analyses

In for a Penny, or: If You Disapprove of Investment Migration, Why Do You Approve of High-Skilled Migration?

While many argue investment-based criteria for immigration are wrong or at least problematic, skill-based criteria remain relatively uncontroversial. This is normatively inconsistent. This article assesses three prominent normative objections to investment-based selection criteria for immigrants: that they wrongfully discriminate between prospective immigrants that they are unfair, and that they undermine political equality among citizens. It argues that either skill-based criteria are equally susceptible to these objections, or that investment-based criteria are equally shielded from them. Indeed, in some ways investment-based criteria are less normatively problematic than skill-based criteria. Given this analysis, the resistance to investment-based migration criteria, but not to skill-based criteria, is inconsistent