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Characterization of the retinal proteome during rod photoreceptor genesis

BMC Research Notes 2010年第1期3卷 1-8页

作者： Barnhill, Alison E Hecker, Laura A Kohutyuk, Oksana Buss, Janice E Honavar, Vasant G Greenlee, Heather West Interdepartmental Neuroscience Program Iowa State University Ames IA United States Department of Biomedical Sciences Iowa State University Ames IA United States Bioinformatics and Computational Biology Program Iowa State University Ames IA United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA United States Department of Computer Science Iowa State University Ames IA United States National Animal Disease Center Ames IA 50010 United States Mayo Clinic Department of Ophthalmology Rochester MN 55905 United States

Background. The process of rod photoreceptor genesis, cell fate determination and differentiation is complex and multi-factorial. Previous studies have defined a model of photoreceptor differentiation that relies on intrinsic changes within the presumptive photoreceptor cells as well as changes in surrounding tissue that are extrinsic to the cell. We have used a proteomics approach to identify proteins that are dynamically expressed in the mouse retina during rod genesis and differentiation. Findings. A series of six developmental ages from E13 to P5 were used to define changes in retinal protein expression during rod photoreceptor genesis and early differentiation. Retinal proteins were separated by isoelectric focus point and molecular weight. Gels were analyzed for changes in protein spot intensity across developmental time. Protein spots that peaked in expression at E17, P0 and P5 were picked from gels for identification. There were 239 spots that were picked for identification based on their dynamic expression during the developmental period of maximal rod photoreceptor genesis and differentiation. Of the 239 spots, 60 of them were reliably identified and represented a single protein. Ten proteins were represented by multiple spots, suggesting they were post-translationally modified. Of the 42 unique dynamically expressed proteins identified, 16 had been previously reported to be associated with the developing retina. Conclusions. Our results represent the first proteomics study of the developing mouse retina that includes prenatal development. We identified 26 dynamically expressed proteins in the developing mouse retina whose expression had not been previously associated with retinal development. © 2010 Greenlee et al.;licensee BioMed Central Ltd.

关键词： Protein Spot Mouse Retina Retinal Development Retinal Protein Probability Base Mowse Score

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Workshop: Flexible read decomposition for improved short read error correction

Workshop: Flexible read decomposition for improved short rea...

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IEEE International Conference on computational Advances in Bio and Medical Sciences (ICCABS)

作者： Xiao Yang Karin S. Dorman Srinivas Aluru Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Department of Genetics Development and Cell Biology Iowa State University Ames IA USA Iowa State University Ames IA US

Error correction is often an important first step prior to analyzing reads from next-generation DNA sequencers. This talk will be focused on a flexible read decomposition method developed to improve the accuracy of error correction and make it more computationally efficient. The method relies on decomposing a read by overlapping tiles, each containing two or more kmers that serve as the basis for error correction. While the value of k is chosen so that the kmer occurs with sufficient frequency, the surrounding kmers within the tile provide the context for improving specificity and resolve ambiguity. The method adopts a flexible tile decomposition strategy to make swift progress in regions with sparse occurrence of errors and thorough exploration in regions of clustered errors. Space usage is reduced by avoiding explicit construction and storage of relationships among kmers, instead relying on the creation of space-efficient data structures that can compute such information on the fly. Experimental verification on benchmark data sets from Illumina Genome Analyzer confirms that the method achieves high error correction accuracy, while the improvements in run-time and memory usage enable scaling to large data sets.

关键词： Error correction bioinformatics Genomics Tiles Accuracy Conferences

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Ranking docked models of protein-protein complexes using predicted partner-specific protein-protein interfaces: A preliminary study 11

Ranking docked models of protein-protein complexes using pre...

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

作者： Xue, Li C. Jordan, Rafael A. El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Computer Science Pontificia Universidad Javeriana Cali Colombia Department of Systems and Computer Engineering AI-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting proteinprotein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://***/DockRank/. Copyright © 2011 ACM.

关键词： Proteins

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A pathway-based kernel boosting method for sample classification using genomic data

arXiv

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arXiv 2018年

作者： Zeng, Li Yu, Zhaolong Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States

The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classifica- tion loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites, and metastasis status. Our results show that PKB outperforms other methods, and identifies pathways relevant to the outcome variables. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Use Symmetry to Elucidate the Roles of Global Shape and Local Interactions in Protein Dynamics and Cooperativity

arXiv

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arXiv 2019年

作者： Song, Guang Department of Computer Science Iowa State University AmesIA50011 United States Program of Bioinformatics and Computational Biology Iowa State University AmesIA50011 United States

Shape had been intuitively recognized to play a dominant role in determining the global motion patterns of bio-molecular assemblies. However, it is not clear exactly how shape determines the motion patterns. What about the local interactions that hold a structure together to a certain shape? The contributions of global shape and local interactions usually mix together and are difficult to tease part. In this work, we use symmetry to elucidate the distinct roles of global shape and local interactions in protein dynamics. Symmetric complexes provide an ideal platform for this task since in them the effects of local interactions and global shape are separable, allowing their distinct roles to be identified. Our key findings based on symmetric assemblies are: (i) the motion patterns of each subunit are determined primarily by intra-subunit interactions (IRSi), and secondarily by inter-subunit interactions (IESi);(ii) the motion patterns of the whole assembly are fully dictated by the global symmetry/shape and have nothing to do with local iESi or IRSi. This is followed by a discussion on how the findings may be generalized to complexes in any shape, with or without symmetry. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Text mining systems biology: Turning the microscope back on the observer

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Current Opinion in Systems biology 2018年 11卷 117-122页

作者： Kong, Xiangmeng Gerstein, Mark B. Program in Computational Biology & Bioinformatics Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Molecular Biophysics & Biochemistry Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Computer Science Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States

In this review, we describe the relationship between systems biology and text mining. On the one hand, text mining functions as a practical tool for systems biology research, which integrates diverse sub-fields and takes an overall, systems-level view of biological phenomena. In this vein, various analyses have been done to recognize biological entities, construct networks of them (e.g. protein–protein interaction maps) and even find disease-associated genes directly from texts. On the other hand, text mining can also be applied to study systems biology itself, giving a ”distant-reading perspective” on the evolution of the field. For example, by examining changes in the frequencies of terms in systems biology publications, we can analyze trends in research focus and in the popularity of systems approaches in various subdomains (e.g., autism). Given these two uses of text mining for systems biology, we close with suggestions for adapting current publication formats for facilitating text mining and enabling its broader use. © 2018

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Flor Revisited(Again):eQTL and Mutational Analysis of NB-LRR Mediated Immunity to Powdery Mildew in Barley

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Journal of Integrative Agriculture 2014年第2期13卷 237-243页

作者： Roger Wise Priyanka Surana Greg Fuerst Ruo Xu Divya Mistry Julie Dickerson Dan Nettleton Corn Insects and Crop Genetics Research Unit U.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology & Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA

Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl （Required for Mla12-specified resistance1）, and Roml （Restoration of Mla-specified resistancel）. The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 （Mla6） uncovered a novel locus, designated Rar3 （R_equired for Mla6-specified resitance3）. Rar3 segregates independent of Mla6 and Rarl, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei （Bgh） isolate 5874 （A VRar）, whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barleyl GeneChip and GAIIx paired-end RNA-Seq. Whereas Rarl affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus, eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla （q value/FDR=0.00001）, a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity.

关键词： eQTL transcript profiling immunity resistance signaling barley Blumeria graminis

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Lymphatic endothelial mTORC1 instructs metabolic and developmental signaling during lymphangiogenesis

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Developmental Cell 2025年

作者： Han, Fei Simeroth, Summer Zhu, Jie Gryniuk, Irma Pranay, Atul Chen, Weiqing Wang, Yuan Cai, Yuanyuan Shen, Zhiyuan Wang, Guangyu Griffin, Courtney T. Xia, Lijun Yu, Pengchun Cardiovascular Biology Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Department of Cell Biology University of Oklahoma Health Sciences Center Oklahoma City 73104 OK United States Aging and Metabolism Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute Houston TX United States Department of Physiology Biophysics & Systems Biology Weill Cornell Graduate School of Medical Science Weill Cornell Medicine Cornell University New York NY United States Department of Radiation Oncology Rutgers Cancer Institute and Robert Wood Johnson Medical School Rutgers University New Brunswick NJ United States Center for Cardiovascular Regeneration Houston Methodist Research Institute Houston TX United States Center for RNA Therapeutics Houston Methodist Research Institute Houston TX United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University New York NY United States Department of Biochemistry and Physiology University of Oklahoma Health Sciences Center Oklahoma City OK United States

The lymphatic vasculature comprises lymphatic capillaries and collecting vessels. To support lymphatic development, lymphatic endothelial cells (LECs) utilize nutrients to fuel lymphangiogenic processes. Meanwhile, LECs maintain constant prospero homeobox 1 (PROX1) expression critical for lymphatic specification. However, molecular mechanisms orchestrating nutrient metabolism while sustaining PROX1 levels in LECs remain unclear. Here, we show that loss of RAPTOR, an indispensable mechanistic target of rapamycin complex 1 (mTORC1) component, downregulates PROX1 and impairs lymphatic capillary growth and differentiation of collecting lymphatics in mice. Mechanistically, mTORC1 inhibition in mouse and human LECs causes Myc reduction, which decreases hexokinase 2 (HK2) and glutaminase (GLS), inhibiting glycolysis and glutaminolysis. Myc or HK2/GLS ablation impedes lymphatic capillary and collecting vessel formation. Interestingly, mTORC1 regulation of PROX1 is independent of Myc-HK2/GLS signaling. Moreover, genetic interaction analysis indicates that Myc and PROX1 play crucial roles in mTORC1-regulated lymphatic development. Collectively, our findings identify mTORC1 as a key regulator of metabolic programs and PROX1 expression during lymphangiogenesis. © 2025 Elsevier Inc.

关键词： glutaminase glutaminolysis glycolysis hexokinase 2 lymphangiogenesis lymphatic endothelial cell lymphatic vessel mTORC1 Myc PROX1

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SRAMM: Short read alignment mapping metrics

arXiv

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arXiv 2021年

作者： Chon, Alvin Huang, Xiaoqiu Bioinformatics and Computational Biology Graduate Program Iowa State University AmesIA50010 United States Department of Computer Science Iowa State University AmesIA50010 United States

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://***/achon/sramm. © 2021, CC BY.

关键词： Mapping

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Article Commentary: Snapshots of Tree Space

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Evolutionary bioinformatics 2009年 5卷

作者： Wang, Zheng López-Giréidez, Francesc Townsend, Jeffrey P. Department of Ecology and Evolutionary Biology Yale University New Haven 06520 CT United States Program in Computational Biology and Bioinformatics Yale University New Haven 06520 CT United States

Achievement of the best balance between the accuracy and efficiency is always an important issue when searching a tree space of large data sets. In the 5th issue in 2009, Rodrigo et al used bootstrapped topologies as fixed genealogies to distribute an MCMC analysis across a cluster of computers, resulting in an efficiency yielding results 37 times faster than in the standard MCMC methods. Tree searches can seldom be guided with certainty, so that such snapshots sampling partial but “more-likely” tree space facilitated by parallel programs on computer clusters may provide great promise among a few choices that are computationally affordable when tree space is large and complicated. © 2009 SAGE Publications.

关键词： computer cluster large phylogeny tree sampling

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