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IEEE Digital Signal Processing Workshop

作者： Patrick L. Harrington Arvind Rao Alfred O., Hero Bioinformatics Graduate Program University of Michigan Ann Arbor MI USA Lane Center of Computational Biology Carnegie Mellon University Pittsburgh PA USA Department of Statistics University of Michigan Ann Arbor MI USA

Much of modern machine learning and statistics research consists of extracting information from high-dimensional patterns. Often times, the large number of features that comprise this high-dimensional pattern are themselves vector valued, corresponding to sampled values in a time-series. Here, we present a classification methodology to accommodate multiple time-series using boosting. This method constructs an additive model by adaptively selecting basis functions consisting of a discriminating feature's full time-series. We present the necessary modifications to fisher linear discriminant analysis and least-squares, as base learners, to accommodate the weighted data in the proposed boosting procedure. We conclude by presenting the performance of our proposed method against a synthetic stochastic differential equation data set and a real world data set involving prediction of cancer patient susceptibility for a particular chemoradiotherapy.

关键词： Boosting Statistics Vectors Linear discriminant analysis Gene expression bioinformatics computational biology Machine learning Data mining Stochastic processes

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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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It takes glue to tango: MeDICi integration framework's data-intensive computing pipeline

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Scientific Computing 2008年第7期25卷 16-19页

作者： Gorton, Ian Oehmen, Christopher S. McDermott, Hason E. PNNL's Data Intensive Computing Program United States Laboratory's Bioinformatics and Computational Biology Group Fundamental and Computational Sciences Directorate United States

Researchers at the Department of Energy's (DOE) Pacific Northwest National Laboratory (PNNL) in Richland, WA, are creating computing environments for biologists that seamlessly integrate collections of data and computational resources. MeDICi is an evolving middleware platform for building complex, high-performance analytical applications. MIF components are constructed using Java programming interfaces that support inter-component communication using asynchronous messaging. Local components execute inside the MIF container. Remote components create distributed solutions and integrate with non-Java code. Mule provides the MIF container environment. MIF extends the Mule interface to make component and pipeline construction easier and to create an encapsulation device for component creation. The MIF interface is agnostic of the underlying Java messaging platform. This allows deployments to configure MIF applications using technologies that meet individual quality-of-service requirements.

关键词： Containers

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Developmental systems biology flourishing on new technologies

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Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

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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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Volumetric, cortical thickness and grey matter density changes on MRI over 12 months: Relationship to conversion status in the ADNI cohort

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Alzheimer's & Dementia 2009年第4期5卷 P200-P200页

作者： Shannon L. Risacher Andrew J. Saykin Li Shen John D. West Sungeun Kim Brenna C. McDonald Center for Neuroimaging Division of Imaging Sciences Department of Radiology Indiana University School of Medicine Indianapolis IN USA Medical Neuroscience Program Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN USA Indiana Alzheimer Disease Center Indianapolis IN USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA

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Large-scale analysis of human heavy chain V(D)J recombination patterns

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Immunome Research 2008年第1期4卷 1-10页

作者： Volpe, Joseph M Kepler, Thomas B Center for Computational Immunology Duke University Durham NC United States Department of Immunology Duke Univeristy Medical Center Durham NC United States Department of Biostatistics and Bioinformatics Duke Univeristy Medical Center Durham NC United States Institute of Statistics and Decision Sciences Duke Univeristy Durham NC United States Computational Biology and Bioinformatics Graduate Program Institute for Genome Sciences and Policy Duke Univeristy Durham NC United States

Background. The processes involved in the somatic assembly of antigen receptor genes are unique to the immune system and are driven largely by random events. Subtle biases, however, may exist and provide clues to the molecular mechanisms involved in their assembly and selection. Large-scale efforts to provide baseline data about the genetic characteristics of immunoglobulin (Ig) genes and the mechanisms involved in their assembly have recently become possible due to the rapid growth of genetic databases. Results. We gathered and analyzed nearly 6,500 productive human Ig heavy chain genes and compared them with 325 non-productive Ig genes that were originally rearranged out of frame and therefore incapable of being biased by selection. We found evidence for differences in n-nucleotide tract length distributions which have interesting interpretations for the mechanisms involved in n-nucleotide polymerization. Additionally, we found striking statistical evidence for pairing preferences among D and J segments. We present a statistical model to support our hypothesis that these pairing biases are due to multiple sequential D-to-J rearrangements. Conclusion. We present here the most precise estimates of gene segment usage frequencies currently available along with analyses regarding n-nucleotide distributions and D-J segment pair preferences. Additionally, we provide the first statistical evidence that sequential D-J recombinations occur at the human heavy chain locus during B-cell ontogeny with an approximate frequency of 20%.

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Knowledge-based versus experimentally acquired distance and angle constraints for NMR structure refinement

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Journal of bioinformatics and computational biology 2008年第2期6卷 283-300页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Laboratory of Cell Biology National Cancer Institute National Institutes of Health Bethesda MD 20892 United States Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Iowa State University Ames IA 50011 United States

Nuclear Overhauser effects (NOE) distance constraints and torsion angle constraints are major conformational constraints for nuclear magnetic resonance (NMR) structure refinement. In particular, the number of NOE constraints has been considered as an important determinant for the quality of NMR structures. Of course, the availability of torsion angle constraints is also critical for the formation of correct local conformations. In our recent work, we have shown how a set of knowledge-based short-range distance constraints can also be utilized for NMR structure refinement, as a complementary set of conformational constraints to the NOE and torsion angle constraints. In this paper, we show the results from a series of structure refinement experiments by using different types of conformational constraints - NOE, torsion angle, or knowledge-based constraints - or their combinations, and make a quantitative assessment on how the experimentally acquired constraints contribute to the quality of structural models and whether or not they can be combined with or substituted by the knowledge-based constraints. We have carried out the experiments on a small set of NMR structures. Our preliminary calculations have revealed that the torsion angle constraints contribute substantially to the quality of the structures, but require to be combined with the NOE constraints to be fully effective. The knowledge-based constraints can be functionally as crucial as the torsion angle constraints, although they are statistical constraints after all and are not meant to be able to replace the latter. © 2008 Imperial College Press.

关键词： Knowledge-based distance constraints NMR structure refinement NOE distance constraints Precision and accuracy of NMR structures Torsion angle constraints

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