Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 5 Articles
Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to\ninterrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required\nnovel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of\nChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods),\neach with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting\nan appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven\ndifferent peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy\nand usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers\nin choosing a suitable tool for handling ChIP-seq data....
Quantitative analysis of animal behaviour is a requirement to understand the task solving strategies of animals and the\r\nunderlying control mechanisms. The identification of repeatedly occurring behavioural components is thereby a key\r\nelement of a structured quantitative description. However, the complexity of most behaviours makes the identification of\r\nsuch behavioural components a challenging problem. We propose an automatic and objective approach for determining\r\nand evaluating prototypical behavioural components. Behavioural prototypes are identified using clustering algorithms and\r\nfinally evaluated with respect to their ability to represent the whole behavioural data set. The prototypes allow for a\r\nmeaningful segmentation of behavioural sequences. We applied our clustering approach to identify prototypical\r\nmovements of the head of blowflies during cruising flight. The results confirm the previously established saccadic gaze\r\nstrategy by the set of prototypes being divided into either predominantly translational or rotational movements,\r\nrespectively. The prototypes reveal additional details about the saccadic and intersaccadic flight sections that could not be\r\nunravelled so far. Successful application of the proposed approach to behavioural data shows its ability to automatically\r\nidentify prototypical behavioural components within a large and noisy database and to evaluate these with respect to their\r\nquality and stability. Hence, this approach might be applied to a broad range of behavioural and neural data obtained from\r\ndifferent animals and in different contexts....
Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most\nimportant under a given situation; the algorithms underlying such control, however, are often not clear. We investigated\npossible algorithms of control for the performance of the majority function, in which participants searched for and identified\none of two alternative categories (left or right pointing arrows) as composing the majority in each stimulus set. We\nmanipulated the amount (set size of 1, 3, and 5) and content (ratio of left and right pointing arrows within a set) of the\ninputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based\non computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to\nalternative algorithms (i.e., exhaustive or self-terminating search). The grouping search algorithm involves sampling and\nresampling of the inputs before a decision is reached. These findings highlight the importance of investigating the\nimplications of voluntary control via algorithms of mental operations....
We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object\r\nfrom the background. However, graph-based algorithms distribute the graph�s nodes uniformly and equidistantly on the\r\nimage. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut\r\nto prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this\r\nproblem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed nonuniformly\r\nand non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable\r\nfrom the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to\r\nsupport the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the\r\nvertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of\r\nexperience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme\r\nyielding an average Dice Similarity Coefficient (DSC) of 90.9762.2%....
Background: Drug design against proteins to cure various diseases has been studied for several years. Numerous design\ntechniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of\nsmall molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem.\nThere has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor\nagainst a protein target have not yet been established.\nMethodology/Principal Findings: A novel de novo peptide design approach is developed to block activities of disease\nrelated protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the\npeptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined\nvia the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by\ngenerating all possible peptide pairs at each point along the path and determining the binding energies between these\npairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices\nof the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface\nis obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that\nresult upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.\nConclusions/Significance: The model is tested on known protein-peptide inhibitor complexes. The present algorithm\npredicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a\nprotein that has excellent binding affinity according to Auto Dock results....
Loading....