检索结果-内蒙古大学图书馆

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

作者： J. Starmer A. Stomp M. Vouk D. Bitzer Bioinformatics Program North Carolina State University USA Department of Forestry North Carolina State University USA Department of Computer Science North Carolina State University USA

In prokaryotes, Shine-Dalgarno (SD) sequences, nucleotides upstream from start codons on messenger RNAs (mRNAs) that are complementary to ribosomal RNA (rRNA), facilitate the initiation of protein synthesis by allowing the two molecules to hybridize and form a double-helix. Many mRNAs, however, lack SD sequences and presumably use another mechanism to recruit ribosomes. We define a hybridization site (HS) as any mRNA sequence that forms as strong a bond to rRNA as a minimal SD sequence. Thus, SD sequences are a class of HSs that occur in a specific region. We scanned the translation initiation regions (TIRs) in 18 prokaryotic genomes and found that only 56% of the TIRs contained SD sequences, and the most stable HSs are not always upstream from start codons. Indeed, we found 2407 genes where the strongest HS in the TIR includes the start codon, deviating from the expected location of 5 to 10 bases upstream. This result suggests an alternative sequence for recruiting ribosomes to mRNAs that has yet to be characterized.

关键词： Tail RNA bioinformatics Recruitment Genomics Bonding Forestry Computer science Proteins Region 5

来源：评论

学校读者我要写书评

暂无评论

Identifying interaction sites in "Recalcitrant" proteins: Predicted protein and RNA binding sites in REV proteins of HIV-1 and EIAV agree with experimental data

Identifying interaction sites in "Recalcitrant" proteins: Pr...

引用

11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Terribllini, Michael Lee, Jae-Hyung Yan, Changhui Jernigan, Robert L. Carpenter, Susan Honavar, Vasant Dobbs, Drena Bioinformatics and Computational Biology Graduate Program LH. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50010 United States Department of Computer Science Iowa State University Ames IA 50010 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50010 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50010 United States Artificial Intelligence Research Laboratory Center for Computational Intelligence Learning and Discovery Iowa State University Ames IA 50010 United States Department of Veterinary Microbiology and Pathology Washington State University Pullman WA. 99164 United States

ISBN: (纸本)9812564632

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation or gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the proiein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-I and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.

关键词： Binding sites

来源：评论

学校读者我要写书评

暂无评论

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators

引用

Journal of Biomedical Discovery and Collaboration 2006年第1期1卷 1-18页

作者： Smalheiser, Neil R. Torvik, Vetle I. Bischoff-Grethe, Amanda Burhans, Lauren B. Gabriel, Michael Homayouni, Ramin Kashef, Alireza Martone, Maryann E. Perkins, Guy A. Price, Diana L. Talk, Andrew C. West, Ruth Department of Psychiatry Psychiatric Institute University of Illinois Chicago IL 60612 1601 W. Taylor Street United States Department of Psychiatry University of California-San Diego La Jolla CA United States Neuroscience Program Beckman Institute University of Illinois Urbana IL United States Department of Psychology Beckman Institute University of Illinois Urbana IL United States Department of Neurology University of Tennessee Memphis TN United States National Center for Microscopy and Imaging Research Department of Neurosciences University of California-San Diego La Jolla CA United States Veterans Affairs San Diego Healthcare System San Diego CA United States Bioinformatics Program University of Memphis United States

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://*** in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community. © 2006 Smalheiser et al;licensee BioMed Central Ltd.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Assembling genomes on large-scale parallel computers 06

Assembling genomes on large-scale parallel computers

引用

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

来源：评论

学校读者我要写书评

暂无评论

Bacillus Anthracis: Interactions with the Host and Establishment of Inhalational Anthrax

引用

Future Microbiology 2006年第4期1卷 397-415页

作者： Karla D Passalacqua Nicholas H Bergman a University of Michigan Medical School Department of Microbiology & Immunology Ann Arbor MI48109 USA. kcesarek@umich.edu b University of Michigan Medical School Bioinformatics Program & Department of Microbiology & Immunology 6706 Medical Sciences Bldg II 1150 W. Medical Center Drive Ann Arbor MI48109-0620 USA. niber@umich.edu

Due to its potential as a bioweapon,Bacillus anthracishas received a great deal of attention in recent years, and a significant effort has been devoted to understanding how this organism causes anthrax. There has been a particular focus on the inhalational form of the disease, and studies over the past several years have painted an increasingly complex picture of howB. anthracisenters the mammalian host, survives the host‘s defense mechanisms, disseminates throughout the body and causes death. This article reviews recent advances in these areas, with a focus on how the bacterium interacts with its host in establishing infection and causing anthrax.

关键词： anthrax Bacillus anthracis bacterial pathogenesis dendritic cell endospore macrophage phagocyte

来源：评论

学校读者我要写书评

暂无评论

The interaction map of yeast: terra incognita?

引用

Journal of biology 2006年第4期5卷 10页

作者： Joe Mellor Charles DeLisi Program in Bioinformatics 24 Cummington Street Boston University Boston MA 02215 USA. mellor@bu.edu

A systematic curation of the literature on Saccharomyces cerevisiae has yielded a comprehensive collection of experimentally observed interactions. This new resource augments current views of the topological structure of yeast's physical and genetic networks, but also reveals that existing studies cover only a fraction of the cell.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The inflammatory and normal transcriptome of mouse bladder detrusor and mucosa

引用

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a grid-based statistical potential for protein structure prediction

Development of a grid-based statistical potential for protei...

引用

2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Zhao, Guijun Lu, Hui Bioinformatics Program Department of Bioengineering University of Illinois at Chicago Chicago IL 60607 United States

ISBN: (纸本)0780387406

A key component in protein structure prediction is the development of potentials that can discriminate native or near native structures from the wrong ones. Most previously developed statistical potentials are based on the preferred distances between any pair of residues. Here we explore the possible angle dependence between pairs of residues in addition to their distance dependence. For simplicity, we used a grid based partition of the space and analyzed relative geometric propensity between protein residue pairs. One thousand and nine non-redundant protein structures are studied in this paper. We have attached a local coordinate system to each amino acid, and spatial distributions of its nearby residues are investigated. Within the same distance range, there are clear differences in various grids. We have further developed a grid-based statistical potential, which incorporates both the distance dependence and angle dependence using a quasi-chemical approximation. The potential is tested against 32 decoy sets, and in 25 cases the native structure has the best score. This performance is comparable and in two cases better than best performance from previously developed distance-dependent statistical potentials on residue and atom level. © 2005 IEEE.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

A machine learning approach to resolving incongruence in molecular phylogenies and visualization analysis

A machine learning approach to resolving incongruence in mol...

引用

2005 IEEE Symposium on Computational Intelligence in bioinformatics and Computational Biology, CIBCB '05

作者： Xiaoxu, Han Department of Mathematics and Bioinformatics Program Eastern Michigan University Ypsilanti MI 48197 United States

ISBN: (纸本)0780393872

The incongruence between gene trees and species trees is one of the most pervasive challenges in molecular phylogenetics. In this work, a machine learning approach is proposed to overcome this problem. In the machine learning approach, the gene data set is clustered by a self-organizing map (SOM). Then a phylogenetically informative core gene set is created by combining the maximum entropy gene from each cluster to conduct phylogenetic analysis. Using the same data set, this approach performs better than the previous random gene concatenation method. The SOM based information visualization is also employed to compare the species patterns in the phylogenetic tree constructions. © 2005 IEEE.

关键词： Learning systems

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：