SUMMARY RNA-binding proteins coordinate the fates of multiple RNAs, but the principles underlying these global interactions remain poorly understood. We elucidated regulatory mechanisms of the RNA-binding protein HuR, by integrating data from diverse high-throughput targeting technologies, specifically PAR-CLIP, RIP-chip, and whole-transcript expression profiling. The number of binding sites per transcript, degree of HuR-association, and degree of HuR-dependent RNA stabilization were positively correlated. Pre-mRNA and mature mRNA containing both intronic and 3′ UTR binding sites were more highly stabilized than transcripts with only 3′ UTR or only intronic binding sites, suggesting that HuR couples pre-mRNA processing with mature mRNA stability. We also observed HuR-dependent splicing changes and substantial binding of HuR in poly-pyrimidine tracts of pre-mRNAs. Comparison of the spatial patterns surrounding HuR and miRNA binding sites provided functional evidence for HuR-dependent antagonism of proximal miRNA-mediated repression. We conclude that HuR coordinates gene expression outcomes at multiple interconnected steps of RNA processing.

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large datasets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at ADH1B. The coding variant rs1229984(∗)T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more efficient crosslinking. We present an approach, centered on the novel PARalyzer tool, for mapping high-confidence sites from PAR-CLIP deep-sequencing data. We show that PARalyzer delineates sites with a high signal-to-noise ratio. Motif finding identifies the sequence preferences of RNA-binding proteins, as well as seed-matches for highly expressed microRNAs when profiling Argonaute proteins. Our study describes tailored analytical methods and provides guidelines for future efforts to utilize high-throughput sequencing in RNA biology. PARalyzer is available at http://www.genome.duke.edu/labs/ohler/research/PARalyzer/.