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Search Results: 1 - 10 of 162283 matches for " William H Majoros "
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Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs
William H. Majoros ,Uwe Ohler
PLOS Computational Biology , 2010, DOI: 10.1371/journal.pcbi.1001037
Abstract: The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.
Spatial preferences of microRNA targets in 3' untranslated regions
William H Majoros, Uwe Ohler
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-152
Abstract: We address how predicted target sites may be affected by alternative polyadenylation events changing the 3'UTR sequence. We find that two thirds of targeted genes have alternative 3'UTRs, with 40% of predicted target sites located in alternative UTR segments. We propose three classes based on whether the target sites fall within constitutive and/or alternative UTR segments, and examine the spatial distribution of predicted targets in alternative UTRs. In particular, there is a strong preference for targets to be located in close vicinity of the stop codon and the polyadenylation sites.The transcript diversity seen in non-coding regions, as well as the relative location of miRNA target sites defined by it, has a potentially large impact on gene regulation by miRNAs and should be taken into account when defining, predicting or validating miRNA targets.Recent years have seen an increased appreciation for the importance of post-transcriptional regulation in eukaryotic organisms [1,2]. The same primary transcript can lead to a number of different isoforms by processing steps such as alternative splicing [3] or polyadenylation [4]. New classes of non-coding RNA genes have been described, including the abundant and conserved class of microRNAs (miRNAs). For instance, one of the earliest identified miRNAs, let-7, is conserved across an impressively wide range of species [5], but its usage in timing of developmental transitions is employed in different species-specific settings [6]. The set of miRNAs comprises hundreds of members in mammalian organisms [7,8], and together, they are regulators of a large fraction of protein-coding genes [9-11], with an important role emerging in developmental transitions and differentiation, as well as establishing cell identity [12-14]. As important regulators of post-transcriptional gene expression, miRNAs are part of essential regulatory networks [15,16]. They have been implied as tumor suppressors [17] and oncogenes [18], and miRNA expres
An empirical analysis of training protocols for probabilistic gene finders
William H Majoros, Steven L Salzberg
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-193
Abstract: Any references to these equations appearing in the text should be modified accordingly.
An empirical analysis of training protocols for probabilistic gene finders
William H Majoros, Steven L Salzberg
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-206
Abstract: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method.We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.The number of generalized hidden Markov model (GHMM) gene finders reported in the literature has increased fairly dramatically of late [1-8], and the community is now contemplating various ways to extend this attractive framework in order to incorporate homology information, with a handful of such systems having already been built (e.g., [9-12]). GHMMs offer a number of clear advantages which would seem to explain this growth in popularity. Chief among these is the fact that the GHMM framework, being (in theory) purely probabilistic, allows for principled approaches to constructing, utilizing, and extending models for accurate prediction of gene structures.While the decoding problem for GHMM gene finders is arguably well understood, being a relatively straightforward extension of the same problem for traditional HMMs and amenable to a Viterbi-like solution (albeit a more complex one), methods for optimally training a GHMM gene finder have received scant attention in the gene-finding literature to date. What information is available (e.g., [2,4]) seems to indicate that the common pr
Motif composition, conservation and condition-specificity of single and alternative transcription start sites in the Drosophila genome
Elizabeth A Rach, Hsiang-Yu Yuan, William H Majoros, Pavel Tomancak, Uwe Ohler
Genome Biology , 2009, DOI: 10.1186/gb-2009-10-7-r73
Abstract: To identify TSSs in Drosophila melanogaster, we applied a hierarchical clustering strategy on available 5' expressed sequence tags (ESTs) and identified a high quality set of 5,665 TSSs for approximately 4,000 genes. We distinguished two initiation patterns: 'peaked' TSSs, and 'broad' TSS cluster groups. Peaked promoters were found to contain location-specific sequence elements; conversely, broad promoters were associated with non-location-specific elements. In alignments across other Drosophila genomes, conservation levels of sequence elements exceeded 90% within the melanogaster subgroup, but dropped considerably for distal species. Elements in broad promoters had lower levels of conservation than those in peaked promoters. When characterizing the distributions of ESTs, 64% of TSSs showed distinct associations to one out of eight different spatiotemporal conditions. Available whole-genome tiling array time series data revealed different temporal patterns of embryonic activity across the majority of genes with distinct alternative promoters. Many genes with maternally inherited transcripts were found to have alternative promoters utilized later in development. Core promoters of maternally inherited transcripts showed differences in motif composition compared to zygotically active promoters.Our study provides a comprehensive map of Drosophila TSSs and the conditions under which they are utilized. Distinct differences in motif associations with initiation pattern and spatiotemporal utilization illustrate the complex regulatory code of transcription initiation.Transcription is a crucial part of gene expression that involves complex interactions of cis-regulatory sequence elements and trans-factors. It is mediated in large part through the binding of transcription factors (TFs) to DNA sequence motifs. The majority of eukaryotic genes (protein-coding genes and many regulatory RNAs) are transcribed by RNA polymerase II (RNA pol II), an enzyme that contains various subuni
JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions
Jonathan E Allen, William H Majoros, Mihaela Pertea, Steven L Salzberg
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-s1-s9
Abstract: Here we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy.While in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based 'combiner' program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research.Predicting complete protein-coding genes in human DNA remains a significant challenge, as the results of the ENCODE Genome Annotation Assessment Project (EGASP) workshop clearly demonstrate. Although much progress has been made of late in the use of increasingly sophisticated models of gene structure, particularly those that utilize homology evidence within a phylogenetic framework (for example, [1,2]), it is clear that there is yet much room for improvement. In the wake of the most recent spate of advances in gene structure modeling, we additionally observe that the sophistication in modeling techniques has to some degree outstripped our ability to ascribe, with high confidence, specific reasons for the difference in performance between competing gene finding systems, particularly those that utilize similar underlying models and/or forms of evidence, but that differ
Efficient decoding algorithms for generalized hidden Markov model gene finders
William H Majoros, Mihaela Pertea, Arthur L Delcher, Steven L Salzberg
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-16
Abstract: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN.In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.Generalized Hidden Markov Models have seen wide use in recent years in the field of computational gene prediction. A number of ab initio gene-finding programs are now available which utilize this mathematical framework internally for the modeling and evaluation of gene structure [1-6], and newer systems are now emerging which expand this framework by simultaneously modeling two genomes at once, in order to harness the mutually informative signals present in homologous gene structures from recently diverged species. As greater numbers of such genomes become available, it is tempting to consider the possibility of integrating all this information into increasingly complex models of gene structure and evolution.Notwithstanding our eagerness to utilize this expected flood of genomic data, methods have yet to be demonstrated which can perform such large-scale parallel analyses without requiring inordinate computational resources. In the case of Generalized Pair HMMs (GPHMMs), for example, the only systems in existence of which we are familiar make
Thermo-magnetic hysteretic properties resembling superconductivity in the normal state of La1.85Sr0.15CuO4
M. Majoros,C. Panagopoulos,T. Nishizaki,H. Iwasaki
Physics , 2005, DOI: 10.1103/PhysRevB.72.024528
Abstract: We have performed detailed magnetic and thermal hysteresis experiments in the normal-state magnetization of La1.85Sr0.15CuO4 single crystal. Using a combination of in-field and in-zero-magnetic-field measurements at different stages of thermal history of the sample, we identified subtle effects associated with the presence of magnetic signatures which resemble those below the superconducting transition temperature (Tc=36 K) but survive up to 250 K.
Magnetic order in the normal state of the archetypal high-Tc superconductor La2-xSrxCuO4
C. Panagopoulos,M. Majoros,T. Nishizaki,H. Iwasaki
Physics , 2004, DOI: 10.1103/PhysRevLett.96.047002
Abstract: We report detailed bulk magnetization measurements of the normal state in the high transition temperature (high-Tc) superconductor La2-xSrxCuO4. A magnetic order in the form of hysteresis in the low field magnetization was observed at temperatures well above Tc but below the pseudogap temperature. The order arises from the interaction of magnetic domains, and the doping (x) dependence of its onset and strength broadly follows that of Tc(x).
A Process Model of Quantum Mechanics  [PDF]
William H. Sulis
Journal of Modern Physics (JMP) , 2014, DOI: 10.4236/jmp.2014.516176
Abstract: A process model of quantum mechanics utilizes a combinatorial game to generate a discrete and finite causal space, which can be defined as a self-consistent quantum mechanics. An emergent space-time  and continuous wave function arise through a non-uniform interpolation process. Standard non-relativistic quantum mechanics emerges under the limit of infinite information (the causal space grows to infinity) and infinitesimal scale (the separation between points goes to zero). This model has the potential to address several paradoxes in quantum mechanics while remaining computationally powerful.
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