MPrime: efficient large scale multiple primer and oligonucleotide design for customized gene microarrays

BACKGROUND: Enhancements in sequencing technology have recently yielded assemblies of large genomes including rat, mouse, human, fruit fly, and zebrafish. The availability of large-scale genomic and genic sequence data coupled with advances in microarray technology have made it possible to study the expression of large numbers of sequence products under several different conditions in days where traditional molecular biology techniques might have taken months, or even years. Therefore, to efficiently study a number of gene products associated with a disease, pathway, or other biological process, it is necessary to be able to design primer pairs or oligonucleotides en masse rather than using a time consuming and laborious gene-by-gene method. RESULTS: We have developed an integrated system, MPrime, in order to efficiently calculate primer pairs or specific oligonucleotides for multiple genic regions based on a keyword, gene name, accession number, or sequence fasta format within the rat, mouse, human, fruit fly, and zebrafish genomes. A set of products created for mouse housekeeping genes from MPrime-designed primer pairs has been validated using both PCR-amplification and DNA sequencing. CONCLUSION: These results indicate MPrime accurately incorporates standard PCR primer design characteristics to produce high scoring primer pairs for genes of interest. In addition, sequence similarity for a set of oligonucleotides constructed for the same set of genes indicates high specificity in oligo design

Personalized modeling for prediction with decision-path models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach

Expression of ionotropic glutamate receptors in the retina of the rdta transgenic mouse

BACKGROUND: The expression of retinal CaMKII is up-regulated in the retina of the rdta mouse in which rod photoreceptors are genetically ablated. As ionotropic glutamate receptors are known substrates of CAMKII, this study set out to determine if the protein levels of ionotropic glutamate receptors in the rdta mouse retina are also affected. RESULTS: The NMDA receptor subunits (NR1, NR2A/B) and the GluR1; AMPA receptor subunit (GluR1) were examined in immunolabeled western blots. The results demonstrate that the amounts of NR1 and NR2A/B receptor subunits are significantly increased in crude synaptic membrane fractions isolated from retinae of the rdta mice when compared to their normal, littermate controls. The GluR1 receptor subunit and its phosphorylation are simultaneously increased in retinae of the rdta mice. CONCLUSIONS: These data indicate that the NMDA receptors and AMPA (GluR1) receptors are altered in the retinae of rdta mice that lack rod photoreceptors. Because the rods are lost at an early stage in development, it is likely that these results are indicative of synaptic reorganization in the retina

Analysis of probe level patterns in Affymetrix microarray data

<p>Abstract</p> <p>Background</p> <p>Microarrays have been used extensively to analyze the expression profiles for thousands of genes in parallel. Most of the widely used methods for analyzing Affymetrix Genechip microarray data, including RMA, GCRMA and Model Based Expression Index (MBEI), summarize probe signal intensity data to generate a single measure of expression for each transcript on the array. In contrast, other methods are applied directly to probe intensities, negating the need for a summarization step.</p> <p>Results</p> <p>In this study, we used the Affymetrix rat genome Genechip to explore variability in probe response patterns within transcripts. We considered a number of possible sources of variability in probe sets including probe location within the transcript, middle base pair of the probe sequence, probe overlap, sequence homology and affinity. Although affinity, middle base pair and probe location effects may be seen at the gross array level, these factors only account for a small proportion of the variation observed at the gene level. A BLAST search and the presence of probe by treatment interactions for selected differentially expressed genes showed high sequence homology for many probes to non-target genes.</p> <p>Conclusion</p> <p>We suggest that examination and modeling of probe level intensities can be used to guide researchers in refining their conclusions regarding differentially expressed genes. We discuss implications for probe sequence selection for confirmatory analysis using real time PCR.</p

Transcriptomic analysis of the zebrafish inner ear points to growth hormone mediated regeneration following acoustic trauma

Background: Unlike mammals, teleost fishes are capable of regenerating sensory inner ear hair cells that have been lost following acoustic or ototoxic trauma. Previous work indicated that immediately following sound exposure, zebrafish saccules exhibit significant hair cell loss that recovers to pre-treatment levels within 14 days. Following acoustic trauma in the zebrafish inner ear, we used microarray analysis to identify genes involved in inner ear repair following acoustic exposure. Additionally, we investigated the effect of growth hormone (GH) on cell proliferation in control zebrafish utricles and saccules, since GH was significantly up-regulated following acoustic trauma. Results: Microarray analysis, validated with the aid of quantitative real-time PCR, revealed several genes that were highly regulated during the process of regeneration in the zebrafish inner ear. Genes that had fold changes of \u3e = 1.4 and P values \u3c = 0.05 were considered significantly regulated and were used for subsequent analysis. Categories of biological function that were significantly regulated included cancer, cellular growth and proliferation, and inflammation. Of particular significance, a greater than 64-fold increase in growth hormone (gh1) transcripts occurred, peaking at 2 days post-sound exposure (dpse) and decreasing to approximately 5.5-fold by 4 dpse. Pathway Analysis software was used to reveal networks of regulated genes and showed how GH affected these networks. Subsequent experiments showed that intraperitoneal injection of salmon growth hormone significantly increased cell proliferation in the zebrafish inner ear. Many other gene transcripts were also differentially regulated, including heavy and light chain myosin transcripts, both of which were down-regulated following sound exposure, and major histocompatability class I and II genes, several of which were significantly regulated on 2 dpse. Conclusions: Transcripts for GH, MHC Class I and II genes, and heavy-and light-chain myosins, as well as many others genes, were differentially regulated in the zebrafish inner ear following overexposure to sound. GH injection increased cell proliferation in the inner ear of non-sound-exposed zebrafish, suggesting that GH could play an important role in sensory hair cell regeneration in the teleost ear

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods