检索结果-内蒙古大学图书馆

IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Yanxun Xu Xiaofeng Zheng Yuan Yuan Marcos R Estecio Jean-Pierre Issa Yuan Ji Shoudan Liang Department of Statistics Rice University Houston TX USA Department of Bioinformatics and Computational Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX USA Department of Biochemistry and Molecular Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Fels Institute for Cancer Research and Molecular Biology Temple University Philadelphia PA USA CCRI NorthShore University HealthSystem Chicago IL USA

ISBN: (纸本)9781467352345

A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (≥95 %). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.

关键词： comparison candidates generation Single nucleotide polymorphism sequence number protection a posteriori probability DNA Sequence Bayesian inference Disease Susceptibility Analyses, Sequence

来源：评论

学校读者我要写书评

暂无评论

Correlations between associated SNPs cause upward bias in whole-genome based heritability estimation

Correlations between associated SNPs cause upward bias in wh...

引用

The Global Conference of Chinese Geneticists(2012全球华人遗传学大会)

作者： Cong Li Can Yang John Ferguson Judy Cho Hongyu Zhao IBD Center Division of GastroenterologyDepartment of MedicineYale UniversityNew HavenCT06520 Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT06520 Department of BiostatisticsYale School of Public HealthNew HavenCT06520 Department of GeneticsYale UniversityNew HavenCT06520

来源：评论

学校读者我要写书评

暂无评论

The Interaction between Base Compositional Heterogeneity and Among-Site Rate Variation in Models of Molecular Evolution

引用

International Scholarly Research Notices 2012年第1期2013卷

作者： Nathan C. Sheffield Program in Computational Biology and Bioinformatics Institute for Genome Sciences and Policy Duke University 101 Science Drive Box 3382 Durham NC 27708 USAduke.edu

Many commonly used models of molecular evolution assume homogeneous nucleotide frequencies. A deviation from this assumption has been shown to cause problems for phylogenetic inference. However, some claim that only extreme heterogeneity affects phylogenetic accuracy and suggest that violations of other model assumptions, such as variable rates among sites, are more problematic. In order to explore the interaction between compositional heterogeneity and variable rates among sites, I reanalyzed 3 real heterogeneous datasets using several models. My Bayesian inference recovers accurate topologies under variable rates-among-sites models, but fails under some models that account for compositional heterogeneity. I also ran simulations and found that accounting for rates among sites improves topology accuracy in compositionally heterogeneous data. This indicates that in some cases, models accounting for among-site rate variation can improve outcomes for data that violates the assumption of compositional homogeneity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistical measures on residue-level protein structural properties

引用

Journal of Structural and Functional Genomics 2011年第2期12卷 119-136页

作者： Huang, Yuanyuan Bonett, Stephen Kloczkowski, Andrzej Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50014 United States Summer REU Program on Computational Systems Biology Iowa State University Ames IA 50014 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50014 United States Department of Mathematics Iowa State University Ames IA 50014 United States

The atomic-level structural properties of proteins, such as bond lengths, bond angles, and torsion angles, have been well studied and understood based on either chemistry knowledge or statistical analysis. Similar properties on the residue-level, such as the distances between two residues and the angles formed by short sequences of residues, can be equally important for structural analysis and modeling, but these have not been examined and documented on a similar scale. While these properties are difficult to measure experimentally, they can be statistically estimated in meaningful ways based on their distributions in known proteins structures. Residue-level structural properties including various types of residue distances and angles are estimated statistically. A software package is built to provide direct access to the statistical data for the properties including some important correlations not previously investigated. The distributions of residue distances and angles may vary with varying sequences, but in most cases, are concentrated in some high probability ranges, corresponding to their frequent occurrences in either α-helices or β-sheets. Strong correlations among neighboring residue angles, similar to those between neighboring torsion angles at the atomic-level, are revealed based on their statistical measures. Residue-level statistical potentials can be defined using the statistical distributions and correlations of the residue distances and angles. Ramachandran-like plots for strongly correlated residue angles are plotted and analyzed. Their applications to structural evaluation and refinement are demonstrated. With the increase in both number and quality of known protein structures, many structural properties can be derived from sets of protein structures by statistical analysis and data mining, and these can even be used as a supplement to the experimental data for structure determinations. Indeed, the statistical measures on various types of residue d

关键词： Protein structural analysis Residue-level structural properties Statistical methods Statistical potentials Structural bioinformatics Structural correlation plots Structural databases

来源：评论

学校读者我要写书评

暂无评论

The ABCs of Experimental Evolution

引用

International Scholarly Research Notices 2013年第1期2013卷

作者： Zaid Abdo Mathew Stein Andrzej Wojtowicz Kim M. Pepin Departments of Statistics University of Idaho Moscow ID 83844 USAuidaho.edu Department of Mathematics University of Idaho Moscow ID 83844 USAuidaho.edu Program of Bioinformatics and Computational Biology (BCB) University of Idaho Moscow ID 83844 USAuidaho.edu USDA-ARS South Atlantic Area 950 College Station Road Athens GA 30605-2720 USA Department of Computer Engineering University of Idaho Moscow ID 83844 USAuidaho.edu Micron Technology Inc. Boise ID 83716 *** Department of Animal Sciences Washington State University Pullman WA 99164 USAwsu.edu Fogarty International Center National Institutes of Health 31 Center Drive MSC 2220 Bethesda MD 20892-2220 USAfic.nih.gov Department of Biology Colorado State University Campus Delivery 1878 Fort Collins CO 80523 USAcolostate.edu

Microbial evolution is complex and is influenced by many sources of variation. Experimental evolution is no exception, although it is more controlled, easily replicated, and typically devoid of interactions between species. Mathematical modeling of the evolutionary process can help in understanding the underlying mechanisms that drive outcome of such experiments. These models can be complex and parameter rich, limiting their feasibility for statistical inference. In this paper, we introduce the use of Approximate Bayesian Computation (ABC) as a tool for statistical inference in the study of experimental evolution. ABC is a fast and simple method for fitting complex models to data. We utilize this method, coupled with a mechanistic model of experimental evolution, to study the evolution process of bacteriophage ϕ X174 under benign selection pressure. Our results highlight three mutation-selection scenarios that could explain this process: high mutation/low selection pressure, low mutation/high selection pressure, and low mutation/low selection pressure, with posterior support of 19%, 9.5%, and 71.5% for each of these scenarios, respectively. Sequence data support the first candidate. Though surprising, this scenario was not improbable based on our analysis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluating the quality of conformation sampling methods using experimental residual dipolar coupling data 11

Evaluating the quality of conformation sampling methods usin...

引用

2011 ACM Conference on bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Lin, Tu-Liang Vammi, Santhosh Kumar Song, Guang Computer Science Department Iowa State University Ames IA 50010 United States Program of Bioinformatics and Computational Biology Iowa State University Ames IA 50010 United States

ISBN: (纸本)9781450307963

Many computational approaches have been developed and used for sampling protein conformations near the native state. However, it has been difficult to evaluate the quality of the conformations sampled or to compare them among the various sampling schemes. In this work, we develop a novel method for evaluating the quality of conformation ensembles and apply it to evaluate ubiquitin conformations generated from four widely-used conformation sampling approaches, namely, MD simulation, Elastic Network Model (ENM), CONCOORD, and *** choose ubiquitin because there exists abundant experimental residual dipolar coupling (RDC) data on this protein. RDC data contains rich ensemble-averaged information about a given protein and thus provide tight constraints that can be used for probing what conformations should make up the protein ensemble. Our results demonstrate that the conformations generated by MD simulations are the best among all sampling methods. Specifically, MD simulation performs significantly better than the other methods in capturing the side chain motions. The backbone flexibility modeled and sampled by tCONCOORD comes quite close, with CONCOORD and ENM trailing behind. Copyright © 2011 ACM.

关键词： Conformations

来源：评论

学校读者我要写书评

暂无评论

Determine the Populations of Protein Conformation States Using Experimental Residual Dipolar Coupling Data

Determine the Populations of Protein Conformation States Usi...

引用

第十七届国际生物物理大会;第十二届中国生物物理大会

作者： Santhosh K.Vammi Department of Computer Sciece Program of Bioinformatics and Computational biologyIowa State UniversityAmesIowaUSA

Ensembles have been increasingly used to represent the heterogeneity of protein native states and there is a number of exciting recent work that determines such ensembles using experimental *** what extent these ensembles represent the native states is debatable since the ensemble,which may contain over a hundred conformations,may be underconstrained by the experimental *** this work,we introduce a new feature,the

关键词： structure ensemble conformation states populations ubiquitin dynamics

来源：评论

学校读者我要写书评

暂无评论

Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

引用

Lecture Notes in Computer Science

作者： Patro, Rob Sefer, Emre Malin, Justin Marçais, Guillaume Navlakha, Saket Kingsford, Carl Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 United States Department of Computer Science University of Maryland College Park MD 20742 United States Computational Biology Bioinformatics and Genomics Concentration Biological Sciences Graduate Program University of Maryland College Park MD 20742 United States Program in Applied Mathematics Statistics and Scientific Computation University of Maryland College Park MD 20742 United States School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9783642230370

We consider the problem of reconstructing a maximally parsimonious history of network evolution under models that support gene duplication and loss and independent interaction gain and loss. We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Conservation of ancient full-length open reading frames in vertebrate endogenous retroviruses

引用

Retrovirology 2011年第2期8卷 1-2页

作者： Hugo Martins Palle Villesen Bioinformatics Research Centre Aarhus University Denmark PhD Program in Computational Biology Instituto Gulbenkian de Ciências Oeiras Portugal

来源：评论

学校读者我要写书评

暂无评论

P.R.E.S.S. — An R-package for exploring residual-level protein structural statistics

P.R.E.S.S. — An R-package for exploring residual-level prot...

引用

IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Yuanyuan Huang Steve Bonett Andrzej Kloczkowski Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Iowa State University Ames IA USA Summer REU Program on Computational Systems Biology Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Program on Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA

P.R.E.S.S. is an R package developed to allow researchers to get access to and manipulate on a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures are downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. will be released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment.

关键词： Correlation Proteins Graphical user interfaces Potential energy Protein engineering Software Graphics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：