Genome-wide prediction of transcription factor binding sites using an integrated model

A new approach for genome-wide transcription factor binding site prediction is presented that integrates sequence and chromatin modification data

Prediction of regulatory elements in mammalian genomes using chromatin signatures

<p>Abstract</p> <p>Background</p> <p>Recent genomic scale survey of epigenetic states in the mammalian genomes has shown that promoters and enhancers are correlated with distinct chromatin signatures, providing a pragmatic way for systematic mapping of these regulatory elements in the genome. With rapid accumulation of chromatin modification profiles in the genome of various organisms and cell types, this chromatin based approach promises to uncover many new regulatory elements, but computational methods to effectively extract information from these datasets are still limited.</p> <p>Results</p> <p>We present here a supervised learning method to predict promoters and enhancers based on their unique chromatin modification signatures. We trained Hidden Markov models (HMMs) on the histone modification data for known promoters and enhancers, and then used the trained HMMs to identify promoter or enhancer like sequences in the human genome. Using a simulated annealing (SA) procedure, we searched for the most informative combination and the optimal window size of histone marks.</p> <p>Conclusion</p> <p>Compared with the previous methods, the HMM method can capture the complex patterns of histone modifications particularly from the weak signals. Cross validation and scanning the ENCODE regions showed that our method outperforms the previous profile-based method in mapping promoters and enhancers. We also showed that including more histone marks can further boost the performance of our method. This observation suggests that the HMM is robust and is capable of integrating information from multiple histone marks. To further demonstrate the usefulness of our method, we applied it to analyzing genome wide ChIP-Seq data in three mouse cell lines and correctly predicted active and inactive promoters with positive predictive values of more than 80%. The software is available at <url>http://http:/nash.ucsd.edu/chromatin.tar.gz</url>.</p

Modified Damus-Kaye-Stansel/Dor Procedure for a Newborn With Severe Left Ventricular Aneurysm

Congenital left ventricular aneurysm (CVA) is a rare cardiac malformation. The prognosis is variable, depending on such factors as the size in comparison to the ventricular cavity, signs of heart failure, arrhythmia and so on. Most infants and young children with large aneurysm showed poor clinical outcomes. Here, we report the case of patient who was prenatally diagnosed with a large CVA, who had severe left ventricular dysfunction at 21 weeks' gestation for which she successfully underwent a modified Damus-Kaye-Stansel/Dor procedure

Regularization in neural network optimization via trimmed stochastic gradient descent with noisy label

Regularization is essential for avoiding over-fitting to training data in neural network optimization, leading to better generalization of the trained networks. The label noise provides a strong implicit regularization by replacing the target ground truth labels of training examples by uniform random labels. However, it may also cause undesirable misleading gradients due to the large loss associated with incorrect labels. We propose a first-order optimization method (Label-Noised Trim-SGD) which combines the label noise with the example trimming in order to remove the outliers. The proposed algorithm enables us to impose a large label noise and obtain a better regularization effect than the original methods. The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm. We also present empirical results that demonstrate the effectiveness of our algorithm using the major benchmarks and the fundamental networks, where our method has successfully outperformed the state-of-the-art optimization methods

Point Mutation of Hoxd12 in Mice

Purpose: Genes of the HoxD cluster play a major role in vertebrate limb development, and changes that modify the Hoxd12 locus affect other genes also, suggesting that HoxD function is coordinated by a control mechanism involving multiple genes during limb morphogenesis. In this study, mutant phenotypes were produced by treatment of mice with chemical mutagen, N-ethyl-N-nitrosourea (ENU). We analyzed mutant mice exhibiting the specific microdactyly phenotype and examined the genes affected. Materials and Methods: We focused on phenotype characteristics including size, bone formation, and digit morphology of ENU-induced microdactyly mice. The expressions of several molecules were analyzed by genome-wide screening and quantitative real-time PCR to define the affected genes. Results: We report on limb phenotypes of an ENU-induced A-to-C mutation in the Hoxd12 gene, resulting in alanine-to-serine conversion. Microdactyly mice exhibited growth defects in the zeugopod and autopod, shortening of digits, a missing tip of digit I, limb growth affected, and dramatic increases in the expressions of Fgf4 and Lmx1b. However, the expression level of Shh was not changed Hoxd12 point mutated mice. Conclusion: These results suggest that point mutation rather than the entire deletion of Hoxd12, such as in knockout and transgenic mice, causes the abnormal limb phenotype in microdactyly mice. The precise nature of the spectrum of differences requires further investigation.link_to_subscribed_fulltex