Divergent RNA binding specificity of yeast Puf2p

PUF proteins bind mRNAs and regulate their translation, stability, and localization. Each PUF protein binds a selective group of mRNAs, enabling their coordinate control. We focus here on the specificity of Puf2p and Puf1p of Saccharomyces cerevisiae, which copurify with overlapping groups of mRNAs. We applied an RNA-adapted version of the DRIM algorithm to identify putative binding sequences for both proteins. We first identified a novel motif in the 3' UTRs of mRNAs previously shown to associate with Puf2p. This motif consisted of two UAAU tetranucleotides separated by a 3-nt linker sequence, which we refer to as the dual UAAU motif. The dual UAAU motif was necessary for binding to Puf2p, as judged by gel shift, yeast three-hybrid, and coimmunoprecipitation from yeast lysates. The UAAU tetranucleotides are required for optimal binding, while the identity and length of the linker sequences are less critical. Puf1p also binds the dual UAAU sequence, consistent with the prior observation that it associates with similar populations of mRNAs. In contrast, three other canonical yeast PUF proteins fail to bind the Puf2p recognition site. The dual UAAU motif is distinct from previously known PUF protein binding sites, which invariably possess a UGU trinucleotide. This study expands the repertoire of cis elements bound by PUF proteins and suggests new modes by which PUF proteins recognize their mRNA targets.     

fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. Methodology: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated 'ChIP' sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/.