Hybridization thermodynamics of NimbleGen Microarrays

Background While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets. Results We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex. Conclusions This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals

Predictors of outcome in infant and toddlers functional or behavioral disorders after a brief parent–infant psychotherapy

The efficacy of parent–child psychotherapies is widely recognized today. There are, however, less data on predictive factors for outcome in infants and toddlers and their parents. The aim of this study was to highlight predictive factors for outcome after a brief psychotherapy in a population of 49 infants and toddlers aged 3–30 months presenting functional or behavioral disorders. Two assessments were performed, the first before treatment and the second a month after the end of the therapy. These assessments included an evaluation of the child’s symptoms, and of depressive or anxiety symptoms in the parents. The assessments after therapy show complete or partial improvement in the child’s symptoms for nearly three quarters, and a decrease in the number of anxious and depressive mothers, and also in the number of depressive fathers. Three independent factors appear as predictive of unfavorable outcome for the child: frequency and intensity of behavioral problems and fears, and the absence of the father at more than two-thirds of consultations. The outcome for the mother is associated solely with her anxiety score at the start of the therapy. This study underlines the particular difficulties involved in the treatment of infants and toddlers presenting behavioral disturbances and emotional difficulties, and the value of involving the father in treatment

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

Background: RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico. Results: We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59. Conclusions: Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.This work has been funded by research grants from the University of Trento, Ital