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Computing H/D-Exchange rates of single residues from data of proteolytic fragments

BMC bioinformatics 2010年第1期11卷 1-12页

作者： Althaus, Ernst Canzar, Stefan Ehrler, Carsten Emmett, Mark R. Karrenbauer, Andreas Marshall, Alan G. Meyer-Bäse, Anke Tipton, Jeremiah D. Zhang, Hui-Min Institut für Informatik Fachbereich 08 Staudingerweg 9 55099 Mainz Germany Algorithmic Computational Biology Centrum Wiskunde and Informatica Amsterdam Netherlands Center for Bioinformatics Computer Science Department Saarland University 66041 Saarbrücken Germany Ion Cyclotron Resonance Program National High Magnetic Field Laboratory Florida State University 1800 East Paul Dirac Drive Tallahassee FL 32310-4005 United States Department of Chemistry and Biochemistry Florida State University 95 Chieftain Way Tallahassee FL 32306 United States Institute of Mathematics EPFL Station 8 1015 Lausanne Switzerland Department of Scientific Computing Florida State University Tallahassee FL 32306-4120 United States

Background: Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein ***: In this work, sHDX data was collected on a 14.5 T FT-ICR MS. An algorithm was developed based on combinatorial optimization that predicts deuterium exchange with high spatial resolution based on the sHDX data of overlapping proteolytic fragments. Often the algorithm assigns deuterium exchange with single residue ***: With our new method it is possible to automatically determine deuterium exchange with higher spatial resolution than the level of digested fragments. © 2010 Althaus et al;licensee BioMed Central Ltd.

关键词： Proteins

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The keyword-based and semantic-driven data matching approach for assisting structuralizing the textual clinical documents

The keyword-based and semantic-driven data matching approach...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Chi-Huang Chen Xiao-Ou Ping Zi-Jun Wang Sheau-Ling Hsieh Li-Chin Chen Yi-Ju Tseng Ching-Wei Hsu Feipei Lai Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronic and Bioinformatics National Taiwan University Taipei Taiwan EECS International Graduate Program Institute IT Center National Chiao Tung University Hsinchu Taiwan

The clinical data stored in the health information system can be categorized as two types including structuralized data and non-structuralized ones. In the paper, a data extraction system is developed to assist data retrieval from the non-structuralized textual clinical documents such as radiology reports, pathology reports, etc. The system provides keyword-based and semantic-driven data matching methodology to extract the specific information from the textual clinical documents. The matching methodology provides the capabilities to recognize the selected keywords and the related semantics in the documents. Through the extraction verification interface, clinicians can extract and verify the matched information semi-automatically. The extracted data can be filled into predefined case-oriented templates. The structuralized data can be stored back into the clinical data warehouse for further analyzing. Moreover, the case-oriented templates can support collecting corresponding extracted data for various researches.

关键词： Data mining Semantics Pattern matching Tumors Radiology Pathology Cancer

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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

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BMC Research Notes 2009年第1期2卷 1-7页

作者： Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 United States Department of Plant Biology Carnegie Institution for Science Stanford CA 94305 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

Background. There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. Findings. This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. Conclusion. Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. © 2009 Sasidharan et al;licensee BioMed Central Ltd.

关键词： Tiling Array Transcript Mapping Tile Path Tiling Microarray Intergenic Transcript

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Erratum to: An approach to compare genome tiling microarray and MPSS sequencing data for transcript mapping

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BMC Research Notes 2009年第1期2卷 1-2页

作者： Rajkumar Sasidharan Ashish Agarwal Joel Rozowsky Mark Gerstein Molecular Biophysics and Biochemistry Department Yale University New Haven CT 06520 USA Department of Plant Biology Carnegie Institution for Science Stanford California 94305 USA Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA Department of Computer Science Yale University New Haven CT 06520 USA

We are correcting the abstract of our published article ([1]). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript.

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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

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Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European bioinformatics Institute Biomedical Ontology Stable Link Breakout Group

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Comprehensive analysis of the pseudogenes of glycolytic enzymes in vertebrates: The anomalously high number of GAPDH pseudogenes highlights a recent burst of retrotrans-positional activity

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BMC Genomics 2009年第1期10卷 480-480页

作者： Liu, Yuen-Jong Zheng, Deyou Balasubramanian, Suganthi Carriero, Nicholas Khurana, Ekta Robilotto, Rebecca Gerstein, Mark B. Department of Surgery Beth Israel Deaconess Medical Center Harvard Medical School Boston MA 110 Francis Street United States Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 P.O. Box 208114 United States Albert Einstein College of Medicine of Yeshiva University Department of Neurology Rose F. Kennedy Center Bronx NY 10461 1410 Pelham Parkway South United States Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 Bass 432 266 Whitney Avenue United States

Background: Pseudogenes provide a record of the molecular evolution of genes. As glycolysis is such a highly conserved and fundamental metabolic pathway, the pseudogenes of glycolytic enzymes comprise a standardized genomic measuring stick and an ideal platform for studying molecular evolution. One of the glycolytic enzymes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), has already been noted to have one of the largest numbers of associated pseudogenes, among all proteins. Results: We assembled the first comprehensive catalog of the processed and duplicated pseudogenes of glycolytic enzymes in many vertebrate model-organism genomes, including human, chimpanzee, mouse, rat, chicken, zebrafish, pufferfish, fruitfly, and worm (available at http://***/glycolysis/). We found that glycolytic pseudogenes are predominantly processed, i.e. retrotransposed from the mRNA of their parent genes. Although each glycolytic enzyme plays a unique role, GAPDH has by far the most pseudogenes, perhaps reflecting its large number of non-glycolytic functions or its possession of a particularly retrotranspositionally active sub-sequence. Furthermore, the number of GAPDH pseudogenes varies significantly among the genomes we studied: none in zebrafish, pufferfish, fruitfly, and worm, 1 in chicken, 50 in chimpanzee, 62 in human, 331 in mouse, and 364 in rat. Next, we developed a simple method of identifying conserved syntenic blocks (consistently applicable to the wide range of organisms in the study) by using orthologous genes as anchors delimiting a conserved block between a pair of genomes. This approach showed that few glycolytic pseudogenes are shared between primate and rodent lineages. Finally, by estimating pseudogene ages using Kimura's two-parameter model of nucleotide substitution, we found evidence for bursts of retrotranspositional activity approximately 42, 36, and 26 million years ago in the human, mouse, and rat lineages, respectively. Conclusion: Overall, we pe

关键词： Glycolytic Enzyme Segmental Duplication Chimpanzee Genome Assembly Version ENSEMBL Release

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On codeword design in metric DNA spaces

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Natural Computing 2008年第3期8卷 571-588页

作者： Vinhthuy Phan Max H. Garzon Department of Computer Science and the Bioinformatics Program University of Memphis Memphis USA

Finding a large set of single DNA strands that do not crosshybridize to themselves and/or to their complements is an important problem in DNA computing, self-assembly, and DNA memories. We describe a theoretical framework to analyze this problem, gauge its computational difficulty, and provide nearly optimal solutions. In this framework, codeword design is reduced to finding large sets of strands maximaly separated in a DNA space and the size of such sets depends on the geometry of these metric spaces. We show that codeword design is NP-complete using any single reasonable measure that approximates the Gibbs energy, thus practically excluding the possibility of finding any procedure to find maximal sets efficiently. Second, we extend a technique known as shuffling to provide a construction that yields provably nearly-maximal codes. Third, we propose a filtering process that removes strands creating pairs with low Gibbs energies, as approximated by the nearest-neighbor model. These two steps produce large codes of thermodynamic high quality. The proposed framework can be used to gain an understanding of the Gibbs energy landscapes for DNA strands on which much of DNA computing and self-assembly are based.

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Grappling with the Gulf

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science 2009年第5911期323卷 210页

作者： Dov Greenbaum Mark Gerstein Mcdermott Will and Emory LLP 3150 Porter Drive Palo Alto CA 94304 USA. Department of Molecular Biophysics and Biochemistry Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT 06520 USA.

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Synthetic Gene Design with a Large Number of Hidden Stop Codons

Synthetic Gene Design with a Large Number of Hidden Stop Cod...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Vinhthuy Phan Sudip Saha Ashutosh Pandey Wong Tit-Yee Department of Computer Science The Bioinformatics Program University of Memphis USA Department of Computer Science University of Memphis USA The Bioinformatics Program University of Memphis USA Department of Biology University of Memphis USA

Hidden stop codons are nucleotide triples TAA, TAG, and TGA that appear in the second and third reading frames of a protein coding gene. Recent studies reported biological evidence suggesting that hidden stop codons are important in preventing misread of mRNA, which is often detrimental to the cell. We study the problem of designing protein-encoding genes with large number of hidden stop codons under biological constraints including GC content and codon usage of individual organism. In simpler models, we obtained provably optimal results. In more complex models, the designed genes have many more hidden stop codons than wild-type genes do, as observed in an experiment with 8 genomes with a wide range of GC content and codon usage.

关键词： Proteins bioinformatics Organisms Amino acids Genetics Technical Activities Guide -TAG Genomics computer science Biological system modeling Synthetic biology

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Using a literature-based NMF model for discovering gene functional relationships

Using a literature-based NMF model for discovering gene func...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Elina Tjioe Michael Berry Ramin Homayouni Genome Science and Technology Graduate School University of Tennessee Knoxville TN USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Bioinformatics Program Department of Biology University of Memphis Memphis TN USA

The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software environment called FAUN or feature annotation using nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation.

关键词： bioinformatics Genomics Data mining Matrix decomposition Large scale integration Feature extraction Iterative methods Biological system modeling computer science Computational biology

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