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The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Dan NETTLETON Divya MISTRY Julie DICKERSON Lin WANG Tom BRUTNELL Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of StatisticsIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Boyce Thompson Institute for Plant ResearchCornell UniversityIthacaNY

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EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO POWDERY MILDEW

EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO ...

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The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Divya MISTRY Julie DICKERSON Dan NETTLETON Department of Statistics Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of StatisticsIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011 Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011

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Regulation of the SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE genes/microRNA 156 Module by the Homeodomain Proteins PENNYWISE and POUND-FOOLISH in Arabidopsis

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Molecular Plant 2011年第6期4卷 1123-1132页

作者： Shruti Lal Leo Bryan Pacis Harley M.S. Smith Genetics Genomics and Bioinformatics Graduate Program University of California Riverside CA 92521 USA Center for Plant Cell Biology Institute of Integrative Genome Biology Department of Botany and Plant Sciences University of California Riverside CA 92521 USA

The morphology of inflorescences is regulated in part by the temporal and spatial events that regulate flower specification. In Arabidopsis, an endogenous flowering time pathway mediated by a subset of SQUAMOSA PROMOTER- BINDING PROTEIN-LIKE （SPL） transcription factors, including SPL3, SPL4, and SPL5, function to specify flowers by activating floral meristem identity genes. During shoot development, SPL3, SPL4, and SPL5 are post-transcriptionally regulated by microRNA156 （miR156）. The photoperiod regulated florigenic signal, FLOWERING LOCUS T （FT）, promotes floral induction, in part by activating SPL3, SPL4, and SPL5. In turn, these SPLs function in parallel with FT to specify flower meristems. Two related BELLl-like homeobox genes PENNYWISE （PNY） and POUND-FOOLISH （PNF） expressed in the shoot apical meristem are absolutely required for the specification of floral meristems. Genetic studies show that the floral specification function of FT depends upon PNYand PNF; however, the interplay between these homeodomain proteins and SPLs is not known. In this manuscript, we show that the photoperiodic floral induction of SPL3, SPL4, and SPL5 is dependent upon PNY and PNE Further, PNY and PNF also control SPL3, SPL4, and SPL5 expression by negatively regulating miR156. Lastly, ectopic expres- sion of SPL4 partially rescues the pny pnf non-flower-producing phenotype, while overexpression of SPL3 or SPL5 in pny pnf plants was unable to restore flower specification. These results suggest that：（1） SPL3, SPL4, and SPL5 function is dependent upon PNY and PNF, or （2） expression of multiple SPLs is required for floral specification in pny pnf plants.

关键词： Development meristem homeodomain flowering microRNA.

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Comprehensive genomic profiling of the lung transcriptome in emphysema and idiopathic pulmonary fibrosis using RNA-Seq

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BMC Proc 2012年第SUPPL 6期6卷 P21-P21页

作者： Rebecca L Kusko John Brothers, II Gang Liu Lingqi Luo Brenda Juan Guardela John Tedrow Yuriy Aleksyev Ivana V Yang Mick Correll Mark Geraci John Quackenbush Frank Sciurba Marc Lenburg David A Schwartz Jennifer Beane Naftali Kaminski Avrum Spira Genetics and Genomics Graduate Program Boston University School of Medicine Boston MA USA Bioinformatics Program Boston University Boston MA USA Division of Computational Biomedicine Department of Medicine Boston University School of Medicine Boston MA USA Simmons Center for Interstitial Lung Disease and Department of Medicine University of Pittsburgh Medical Center Pittsburgh PA USA Department of Pathology and Laboratory Medicine Boston University School of Medicine Boston MA USA Center for Genes Environment and Health and Department of Medicine National Jewish Health Denver CO USA Dana-Farber Cancer Institute and Harvard School of Public Health Boston MA USA Department of Medicine University of Colorado School of Medicine Aurora CO USA

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Moving beyond gene expression: identification of lung-disease-associated novel transcripts and alternative splicing by RNA sequencing

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BMC Proceedings 2012年第6期6卷 1-2页

作者： John Brothers II Paola Sebastiani Marc Lenburg Avrum Spira Rebecca Kusko Yuriy Alekesyev Gang Liu Lingqi Luo Jennifer Beane Brenda Juan Guardela John Tedrow Frank Sciurba Naftali Kaminski Ivana V Yang David A Schwartz Mick Correll John Quackenbush Mark Geraci Bioinformatics Program Boston University Boston MA 02115 USA Genetics and Genomics Graduate Program Boston University School of Medicine Boston MA 02118 USA Department of Medicine Boston University School of Medicine Section of Computational Biomedicine Boston MA 02118 USA Department of Pathology and Laboratory Medicine Boston University School of Medicine Boston MA 02118 USA Simmons Center for Interstitial Lung Disease and Department of Medicine University of Pittsburgh Medical Center Pittsburgh PA 15213 USA Center for Genes Environment and Health and Department of Medicine National Jewish Health Denver CO 80206 USA Dana-Farber Cancer Institute and Harvard School of Public Health Boston MA 02115 USA Department of Medicine University of Colorado School of Medicine Aurora CO 80045 USA

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Characterizing the small RNA transcriptome associated with COPD and ILD using next-generation sequencing

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BMC Proceedings 2012年第6期6卷 1-2页

作者： Joshua D Campbell Joseph Gerrein Marc E Lenburg Avrum Spira Lingqi Luo Gang Liu Ji Xiao Jennifer Beane Brenda J Guardela John Tedrow Frank Sciurba Naftali Kaminski Yuriy O Aleksyev Ivana V Yang David A Schwartz Mick Correll John Quackenbush Mark Geraci Bioinformatics Graduate Program Boston University Boston MA USA Section of Computational Biomedicine Boston University Boston MA USA Department of Pathology and Laboratory Medicine Boston University School of Medicine Boston MA USA Simmons Center for Interstitial Lung Disease and Department of Medicine University of Pittsburgh Medical Center Pittsburgh PA USA Center for Genes Environment and Health and Department of Medicine National Jewish Health Denver CO USA Dana-Farber Cancer Institute and Harvard School of Public Health Boston MA USA Department of Medicine University of Colorado School of Medicine Aurora CO USA

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A Bayesian model for SNP discovery based on next-generation sequencing data

A Bayesian model for SNP discovery based on next-generation ...

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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

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Expression of G protein-coupled receptors and related proteins in HEK293, AtT20, BV2, and N18 cell lines as revealed by microarray analysis

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BMC Genomics 2011年第1期12卷 1-14页

作者： Atwood, Brady K. Lopez, Jacqueline Wager-Miller, James Mackie, Ken Straiker, Alex Department of Psychological and Brain Sciences The Gill Center for Biomolecular Science Indiana University Bloomington IN United States Center for Genomics and Bioinformatics Indiana University Bloomington IN United States Graduate Program in Neurobiology and Behavior University of Washington Seattle WA United States

Background: G protein coupled receptors (GPCRs) are one of the most widely studied gene superfamilies. Thousands of GPCR research studies have utilized heterologous expression systems such as human embryonic kidney cells (HEK293). Though often treated as 'blank slates', these cell lines nevertheless endogenously express GPCRs and related signaling proteins. The outcome of a given GPCR study can be profoundly influenced by this largely unknown complement of receptors and/or signaling proteins. Little easily accessible information exists that describes the expression profiles of the GPCRs in cell lines. What is accessible is often limited in scope - of the hundreds of GPCRs and related proteins, one is unlikely to find information on expression of more than a dozen proteins in a given cell line. Microarray technology has allowed rapid analysis of mRNA levels of thousands of candidate genes, but though often publicly available, the results can be difficult to efficiently access or even to ***: To bridge this gap, we have used microarrays to measure the mRNA levels of a comprehensive profile of non-chemosensory GPCRs and over a hundred GPCR signaling related gene products in four cell lines frequently used for GPCR research: HEK293, AtT20, BV2, and ***: This study provides researchers an easily accessible mRNA profile of the endogenous signaling repertoire that these four cell lines possess. This will assist in choosing the most appropriate cell line for studying GPCRs and related signaling proteins. It also provides a better understanding of the potential interactions between GPCRs and those signaling proteins. © 2011 Atwood et al;licensee BioMed Central Ltd.

关键词： HEK293 Cell AtT20 Cell GPCR Signaling Frizzle Receptor Related Signaling Protein

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Modularity detection in protein-protein interaction networks

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BMC Research Notes 2011年第1期4卷 1-6页

作者： Narayanan, Tejaswini Gersten, Merril Subramaniam, Shankar Grama, Ananth Department of Electrical and Computer Engineering University of California San Diego United States Graduate Program in Bioinformatics and Systems Biology University of California San Diego United States Department of Bioengineering University of California San Diego United States Department of Computer Science Purdue University West Lafayette IN United States

Background: Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results: We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, *** and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions: We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the applic

关键词： Biological Network Jaccard Index Input Network High Betweenness Jaccard Similarity Coefficient

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Parsimonious reconstruction of network evolution

Parsimonious reconstruction of network evolution

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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

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