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The inflammatory and normal transcriptome of mouse bladder detrusor and mucosa

BMC Physiology 2006年第1期6卷 1-20页

作者： Saban, Marcia R. Hellmich, Helen L. Turner, Mary Nguyen, Ngoc-Bich Vadigepalli, Rajanikanth Dyer, David W. Hurst, Robert E. Centola, Michael Saban, Ricardo Department of Physiology University Oklahoma Health Sciences Center Oklahoma City OK United States Department of Anesthesiology University of Texas Medical Branch Galveston TX United States Oklahoma Medical Research Foundation (OMRF) Arthritis and Immunology Research Program Microarray Core Facility Oklahoma City OK United States Daniel Baugh Institute for Functional Genomics and Computational Biology Department of Pathology Anatomy and Cell Biology Thomas Jefferson University Philadelphia United States Department of Microbiology and Immunology Laboratory for Genomics and Bioinformatics Oklahoma University Health Sciences Center Oklahoma City OK United States Department of Urology University Oklahoma Health Sciences Center Oklahoma City OK United States Cellular and Structural Biology University of Texas Health Science Center at San Antonio San Antonio TX United States

Background: An organ such as the bladder consists of complex, interacting set of tissues and cells. Inflammation has been implicated in every major disease of the bladder, including cancer, interstitial cystitis, and infection. However, scanty is the information about individual detrusor and urothelium transcriptomes in response to inflammation. Here, we used suppression subtractive hybridizations (SSH) to determine bladder tissue- and disease-specific genes and transcriptional regulatory elements (TRE)s. Unique TREs and genes were assembled into putative networks. Results: It was found that the control bladder mucosa presented regulatory elements driving genes such as myosin light chain phosphatase and calponin I that influence the smooth muscle phenotype. In the control detrusor network the Pax-3 TRE was significantly over-represented. During development, the Pax-3 transcription factor (TF) maintains progenitor cells in an undifferentiated state whereas, during inflammation, Pax-3 was suppressed and genes involved in neuronal development (synapsin I) were up-regulated. Therefore, during inflammation, an increased maturation of neural progenitor cells in the muscle may underlie detrusor instability. NF-κB was specifically over-represented in the inflamed mucosa regulatory network. When the inflamed detrusor was compared to control, two major pathways were found, one encoding synapsin I, a neuron-specific phosphoprotein, and the other an important apoptotic protein, siva. In response to LPS-induced inflammation, the liver X receptor was over-represented in both mucosa and detrusor regulatory networks confirming a role for this nuclear receptor in LPS-induced gene expression. Conclusion: A new approach for understanding bladder muscle-urothelium interaction was developed by assembling SSH, real time PCR, and TRE analysis results into regulatory networks. Interestingly, some of the TREs and their downstream transcripts originally involved in organogenesis and oncogene

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Assembling genomes on large-scale parallel computers 06

Assembling genomes on large-scale parallel computers

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： A. Kalyanaraman S.J. Emrich P.S. Schnable S. Aluru Department of Electrical and Computer Engineering Iowa State University Ames IA USA Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Iowa State University Ames IA US Dept. of Electr. & Comput. Eng. Iowa State Univ. Ames IA USA

ISBN: (纸本)9781424400546

Assembly of large genomes from tens of millions of short genomic fragments is computationally demanding requiring hundreds of gigabytes of memory and tens of thousands of CPU hours. New gene-enrichment sequencing strategies are expected to further exacerbate this situation. In this paper, we present a massively parallel genome assembly framework. The unique features of our approach include space-efficient and on-demand algorithms that consume only linear space, and heuristic strategies that reduce the number of expensive pairwise sequence alignments while maintaining assembly quality. As part of the ongoing efforts in maize genome sequencing, we applied our assembly framework to the largest available collection of maize genomic data. We report the partitioning of more than 1.6 million fragments of over 1.25 billion nucleotides total size into genomic islands in 2 hours on 1,024 processors of an IBM BlueGene/L supercomputer.

关键词： Assembly Genomics bioinformatics Large-scale systems Concurrent computing Sequences DNA Organisms biology computing Biological cells

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of bioinformatics and computational biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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A comparison of the robustness of evolutionary computation and random walks

A comparison of the robustness of evolutionary computation a...

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Congress on Evolutionary Computation

作者： J. Schonfeld D.A. Ashlock Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Mathematics Department Iowa State University Ames IA USA

Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model systems that are easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection evolutionary algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.

关键词： Robustness Evolutionary computation Genetic mutations Evolution (biology) Biological systems Proteins Stochastic processes Systems engineering and theory Stochastic systems bioinformatics

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Finite Size Effects on Thermal Denaturation of Globular Proteins

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Physical Review Letters 2004年第26期93卷 268107-268107页

作者： Mai Suan Li Dmitri K. Klimov D. Thirumalai Institute of Physics Polish Academy of Sciences Al. Lotnikow 32/46 02-668 Warsaw Poland Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA Bioinformatics and Computational Biology Program School of Computational Sciences George Mason University Manassas Virginia 20110 USA

Finite size effects on the cooperative thermal denaturation of proteins are considered. A dimensionless measure of cooperativity, Ωc, scales as Nζ, where N is the number of amino acids. Surprisingly, we find that ζ is universal with ζ=1+γ, where the exponent γ characterizes the divergence of the susceptibility for a self-avoiding walk. Our lattice model simulations and experimental data are consistent with the theory. Our finding rationalizes the marginal stability of proteins and substantiates the earlier predictions that the efficient folding of two-state proteins requires TF≈Tθ, where Tθ and TF are the collapse and folding transition temperatures, respectively.

关键词： .stability Transitions PRL ending susceptibility experimental data Thermal denaturation folding transition exponent unfolded native state Globular Proteins cooperativity two-state NBA

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Perspectives on Human Population Structure at the Cusp of the Sequencing Era

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Genomics and Human Genetics 1000年第1期12卷 245-274页

作者： John Novembre Sohini Ramachandran *Both authors contributed equally. 1Department of Ecology and Evolutionary Biology and Interdepartmental Program on Bioinformatics University of California Los Angeles California 90403 email: jnovembre@ucla.edu 2Department of Ecology and Evolutionary Biology and Center for Computational Molecular Biology Brown University Providence Rhode Island 02912 email: sramachandran@brown.edu

Human groups show structured levels of genetic similarity as a consequence of factors such as geographical subdivision and genetic drift. Surveying this structure gives us a scientific perspective on human origins, sheds light on evolutionary processes that shape both human adaptation and disease, and is integral to effectively carrying out the mission of global medical genetics and personalized medicine. Surveys of population structure have been ongoing for decades, but in the past three years, single-nucleotide-polymorphism (SNP) array technology has provided unprecedented detail on human population structure at global and regional scales. These studies have confirmed well-known relationships between distantly related populations and uncovered previously unresolvable relationships among closely related human groups. SNPs represent the first dense genome-wide markers, and as such, their analysis has raised many challenges and insights relevant to the study of population genetics with whole-genome sequences. Here we draw on the lessons from these studies to anticipate the directions that will be most fruitful to pursue during the emerging whole-genome sequencing era.

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Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

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Biomedical Data Science 1000年第1期5卷 141-161页

作者： Bian Li Bowen Jin John A. Capra William S. Bush 1Department of Biological Sciences and Center for Structural Biology Vanderbilt University Nashville Tennessee USA 2Graduate Program in Systems Biology and Bioinformatics Department of Nutrition School of Medicine Case Western Reserve University Cleveland Ohio USA 3Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics University of California San Francisco California USA email: tony@*** 4Cleveland Institute for Computational Biology Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland Ohio USA email: wsb36@case.edu

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

关键词： protein 3D structure population genetics variant interpretation disease gene discovery data integration

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Network Analysis as a Grand Unifier in Biomedical Data Science

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Biomedical Data Science 1000年第1期1卷 153-180页

作者： Patrick McGillivray Declan Clarke William Meyerson Jing Zhang Donghoon Lee Mengting Gu Sushant Kumar Holly Zhou Mark Gerstein 1Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: mark@*** 2Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA 3Department of Computer Science Yale University New Haven Connecticut 06520 USA

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

关键词： network analysis molecular interaction systems biology cross-disciplinary research network medicine

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Research in computational Molecular biology 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Minghua Deng Rui Jiang Fengzhu Sun Xuegong Zhang

ISBN: (数字)9783642371950

ISBN: (纸本)9783642371943

This book constitutes the refereed proceedings of the 17th Annual International Conference on Research in computational Molecular biology, RECOMB 2013, held in Beijing, China, in April 2013. The 32 revised full papers were carefully reviewed and selected from 167 submissions. The papers cover a wide range of topics including molecular sequence analysis; genes and regulatory elements; molecular evolution; gene expression; biological networks; sequencing and genotyping technologies; genomics; epigenomics; metagenomics; population, statistical genetics; systems biology; computational proteomics; computational structural biology; imaging; large-scale data management.

关键词： computational biology/bioinformatics Mathematical and computational biology Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity

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Causes and Consequences of Apparent Timescaling Across All Estimated Evolutionary Rates

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Ecology, Evolution, and Systematics 1000年第1期52卷 587-609页

作者： Luke J. Harmon Matthew W. Pennell L. Francisco Henao-Diaz Jonathan Rolland Breanna N. Sipley Josef C. Uyeda 1Institute for Bioinformatics and Evolutionary Studies (IBEST) and Department of Biological Sciences University of Idaho Moscow Idaho 83844 USA email: lukeh@uidaho.edu 2Department of Zoology and Biodiversity Research Centre University of British Columbia Vancouver British Columbia V6T 1Z4 Canada 3Laboratoire Evolution et Diversité Biologique CNRS UMR5174 Université Toulouse III–Paul Sabatier 31062 Toulouse France 4Program for Bioinformatics and Computational Biology University of Idaho Moscow Idaho 83844 USA 5Department of Biological Sciences Virginia Tech University Blacksburg Virginia 24061 USA

Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.

关键词： speciation extinction genetics phenotypes microevolution macroevolution

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