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

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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structural genomics is the largest contributor of novel structural leverage

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Journal of structural and Functional Genomics 2009年第2期10卷 181-191页

作者： Nair, Rajesh Liu, Jinfeng Soong, Ta-Tsen Acton, Thomas B. Everett, John K. Kouranov, Andrei Fiser, Andras Godzik, Adam Jaroszewski, Lukasz Orengo, Christine Montelione, Gaetano T. Rost, Burkhard Department of Biochemistry and Molecular Biophysics Columbia University New York NY 10032 630 West 168th St. United States Northeast Structural Genomics Consortium (NESG) Columbia University Center for Computational Biology and Bioinformatics (C2B2) Columbia University New York NY 10032 1130 St. Nicholas Ave. United States Department of Biomedical Informatics Columbia University New York NY 10032 630 West 168th St. United States Center for Advanced Biotechnology Department of Molecular Biology and Biochemistry Rutgers University Piscataway NJ 679 Hoes Lane United States Protein Structure Initiative Knowledge Base and RCSB PDB Department of Chemistry and Chemical Biology Rutgers University Piscataway NJ 08854-8087 610 Taylor Rd. United States New York SGX Research Center for Structural Genomics (NYSGXRC) Department of Systems and Computational Biology Albert Einstein College of Medicine New York NY United States Joint Center for Structural Genomics (JCSG) Burnham Research Institute San Diego CA United States Midwest Center of Structural Genomics (MCSG) Department of Structural Biology University College of London (UCL) London WC1E 6BT United Kingdom Department of Biochemistry Robert Wood Johnson Medical School University of Medicine and Dentistry of New Jersey Piscataway NJ 08854 United States New York Consortium on Membrane Protein Structure (NYCOMPS) Columbia University New York NY 10032 1130 St. Nicholas Ave. United States

The Protein structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today's UniProt (v12.8) has increased linearly from 1992 to 2008;structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849-851, 2007) has resulted from systematic targeting of large families. PSI's per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ∼15 years to cover most sequences in the current UniProt database. © 2009 The Author(s).

关键词： Evolution Protein structure determination Protein universe structural genomics

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structural genomics target selection for the New York consortium on membrane protein structure

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Journal of structural and Functional Genomics 2009年第4期10卷 255-268页

作者： Punta, Marco Love, James Handelman, Samuel Hunt, John F. Shapiro, Lawrence Hendrickson, Wayne A. Rost, Burkhard Department of Biochemistry and Molecular Biophysics Columbia University New York NY 10032 630 West 168th Street United States Columbia University Center for Computational Biology and Bioinformatics (C2B2) New York NY 10032 1130 St. Nicholas Ave. United States New York Consortium on Membrane Protein Structure New York Structural Biology Center New York NY 10027 89 Convent Avenue United States Department of Biological Sciences Columbia University New York NY 10032 United States Howard Hughes Medical Institute Columbia University New York NY 10032 United States Northeast Structural Genomics Consortium (NESG) New York NY 10032 1130 St. Nicholas Ave. United States

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space. © 2009 Springer Science+Business Media B.V.

关键词： Membrane proteins structural genomics Structure determination Target selection

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Late Residual γ-H2AX Foci In Murine Skin are Dose Responsive and Predict Radiosensitivity In Vivo. (vol 172, pg 1, 2010)

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RADIATION RESEARCH 2010年第3期173卷 399-400页

作者： Bhogal, N. Kaspler, P. Jalali, F. Hyrien, O. Chen, R. Hill, R. P. Bristow, R. G. [a]Applied Molecular Oncology and Radiation Medicine Program Ontario Cancer Institute/Princess Margaret Hospital (UHN) Toronto Ontario Canada [b]Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester New York [c]Departments of Medical Biophysics and Radiation Oncology University of Toronto Toronto Ontario Canada Bhogal N. Kaspler P. Jalali F. Hyrien O. Chen R. Hill R. P. and Bristow R. G. Late Residual γ-H2AX Foci In Murine Skin are Dose Responsive and Predict Radiosensitivity In Vivo.

Accurate biodosimetry is needed to estimate radiation doses received in vivo from accidental or unwarranted radiation exposures. We investigated the use of DNA repair foci (e.g. γ-H2AX) at late times after irradiation in vivo as a biodosimeter of initial ionizing radiation dose. Two radiosensitive strains (SCID and BALB/c) and two radioresistant strains (C57BL/6 and C3H/HeJ) were used to quantify γ-H2AX foci in a skin tissue microarray after doses of 1 to 10 Gy at early and late times after irradiation (1 and 7 days). Using a 3D quantitative immunofluorescence microscopy analysis, we observed a dose response for γ-H2AX foci for all strains at 30 min, 24 h and 7 days after irradiation. The numbers of residual foci were significantly different between each of the four strains and reflected the relative radiosensitivity in vivo. In comparing γ-H2AX focus and micronucleus formation after irradiation, we also observed association between the number of micronuclei and number of foci after 1 and 7 days between radiosensitive and radioresistant strains. We conclude that 3D image analysis of γ-H2AX in skin can be used to detect relative radiosensitivity based on late residual γ-H2AX foci. This technique may be a useful biodosimeter to determine dose at times up to 1 week after accidental or catastrophic radiation exposure in vivo.

关键词： Skin Dose response relationship Irradiation Epidermal cells Radiotherapy DNA repair Radiation dose response relationship Mice Biopsies

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Network Analysis as a Grand Unifier in Biomedical Data Science

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Biomedical Data Science 1000年第1期1卷 153-180页

作者： Patrick McGillivray Declan Clarke William Meyerson Jing Zhang Donghoon Lee Mengting Gu Sushant Kumar Holly Zhou Mark Gerstein 1Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA email: mark@*** 2Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA 3Department of Computer Science Yale University New Haven Connecticut 06520 USA

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

关键词： network analysis molecular interaction systems biology cross-disciplinary research network medicine

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computational approaches to understanding protein aggregation in neurodegeneration

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Journal of molecular Cell biology 2014年第2期8卷 104-115页

作者： Rachel L. Redler David Shirvanyants Onur Dagliyan Feng Ding Doo Nam Kim Pradeep Kota Elizabeth A. Proctor Srinivas Ramachandran Arpit Tandon Nikolay V. Dokholyan Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Program in Cellular and Molecular Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Computational and Systems Biology University of NorthCarolina at Chapel Hill chapel Hill NC USA Department of Physics and Astronomy Ctemson University Ctemson SC USA Cellular Growth Mechanisms Section Laboratory of Cell and Developmental Signaling Frederick National Laboratory for Cancer Research Frederick MD USA Division of Basic Sciences Fred Hutchison Cancer Research Center Seatt{e WA USA

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

关键词： protein aggregation molecular dynamics protein folding neurodegeneration

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TET proteins safeguard bivalent promoters from de novo methylation in human embryonic stem cells (vol 50, pg 83, 2017)

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NATURE GENETICS 2018年第5期50卷 764-764页

作者： Verma, Nipun Pan, Heng Dore, Louis C. Shukla, Abhijit Li, Qing V. Pelham-Webb, Bobbie Teijeiro, Virginia Gonzalez, Federico Krivtsov, Andrei Chang, Chan-Jung Papapetrou, Eirini P. He, Chuan Elemento, Olivier Huangfu, Danwei Developmental Biology Program Sloan Kettering Institute New York NY USA Weill Graduate School of Medical Sciences at Cornell University/The Rockefeller University/Sloan Kettering Institute Tri-Institutional MD-PhD Program New York NY USA Department of Physiology and Biophysics Englander Institute for Precision Medicine Institute for Computational Biomedicine Weill Cornell Medical College New York NY USA Weill Cornell Graduate School of Medical Sciences Weill Cornell Medical College New York NY USA Department of Chemistry Department of Biochemistry and Molecular Biology Institute for Biophysical Dynamics Howard Hughes Medical Institute University of Chicago Chicago IL USA Louis V. Gerstner Jr. Graduate School of Biomedical Sciences Memorial Sloan Kettering Cancer Center New York NY USA Cancer Biology and Genetics Program Sloan Kettering Institute New York NY USA Department of Oncological Sciences and Black Family Stem Cell Institute Icahn School of Medicine at Mount Sinai New York NY USA

TET enzymes oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which can lead to DNA demethylation. However, direct connections between TET-mediated DNA demethylation and transcriptional output are difficult to establish owing to challenges in distinguishing global versus locus-specific effects. Here we show that TET1, TET2 and TET3 triple-knockout (TKO) human embryonic stem cells (hESCs) exhibit prominent bivalent promoter hypermethylation without an overall corresponding decrease in gene expression in the undifferentiated state. Focusing on the bivalent PAX6 locus, we find that increased DNMT3B binding is associated with promoter hypermethylation, which precipitates a neural differentiation defect and failure of PAX6 induction during differentiation. dCas9-mediated locus-specific demethylation and global inactivation of DNMT3B in TKO hESCs partially reverses the hypermethylation at the PAX6 promoter and improves differentiation to neuroectoderm. Taking these findings together with further genome-wide methylation and TET1 and DNMT3B ChIP-seq analyses, we conclude that TET proteins safeguard bivalent promoters from de novo methylation to ensure robust lineage-specific transcription upon differentiation.

关键词： Epigenetics Stem cells

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A "non-fratricidal" αβ- T Cell Receptor That Targets Survivin Expressed By Hematological Malignancies

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BLOOD 2013年第21期122卷 141-141页

作者： Arber, Caroline Feng, Xiang Abhyankar, Harshal Heslop, Helen E. Brenner, Malcolm K. Barth, Patrick Dotti, Gianpietro Savoldo, Barbara Center for Cell and Gene Therapy Baylor College of Medicine Houston TX USA Department of Pharmacology Baylor College of Medicine Houston TX USA Structural and Computational Biology and Molecular Biophysics Graduate Program Baylor College of Medicine Houston TX USA Center for Cell and Gene Therapy Baylor College of Medicine The Methodist Hospital Texas Children's Hospital Houston TX USA

141

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Precision and accuracy of single-molecule FRET measurements-a multi-laboratory benchmark study (vol 15, pg 984, 2018)

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NATURE METHODS 2018年第11期15卷 984-984页

作者： Hellenkamp, Bjorn Schmid, Sonja Doroshenko, Olga Opanasyuk, Oleg Kuhnemuth, Ralf Adariani, Soheila Rezaei Ambrose, Benjamin Aznauryan, Mikayel Barth, Anders Birkedal, Victoria Bowen, Mark E. Chen, Hongtao Cordes, Thorben Eilert, Tobias Fijen, Carel Gebhardt, Christian Gotz, Markus Gouridis, Giorgos Gratton, Enrico Ha, Taekjip Hao, Pengyu Hanke, Christian A. Hartmann, Andreas Hendrix, Jelle Hildebrandt, Lasse L. Hirschfeld, Verena Hohlbein, Johannes Hua, Boyang Hubner, Christian G. Kallis, Eleni Kapanidis, Achillefs N. Kim, Jae-Yeol Krainer, Georg Lamb, Don C. Lee, Nam Ki Lemke, Edward A. Levesque, Brie Levitus, Marcia McCann, James J. Naredi-Rainer, Nikolaus Nettels, Daniel Thuy Ngo Qiu, Ruoyi Robb, Nicole C. Rocker, Carlheinz Sanabria, Hugo Schlierf, Michael Schroder, Tim Schuler, Benjamin Seidel, Henning Streit, Lisa Thurn, Johann Tinnefeld, Philip Tyagi, Swati Vandenberk, Niels Vera, Andres Manuel Weninger, Keith R. Wunsch, Bettina Yanez-Orozco, Inna S. Michaelis, Jens Seidel, Claus A. M. Craggs, Timothy D. Hugel, Thorsten Institute of Physical Chemistry University of Freiburg Freiburg im Breisgau Germany Engineering and Applied Sciences Columbia University New York NY USA Department of Bionanoscience Kavli Institute of Nanoscience Delft Delft University of Technology Delft the Netherlands Molecular Physical Chemistry Heinrich-Heine-Universität Düsseldorf Düsseldorf Germany Department of Physics and Astronomy Clemson University Clemson SC USA Department of Chemistry University of Sheffield Sheffield UK Interdisciplinary Nanoscience Center (iNANO) and Department of Chemistry Aarhus University Aarhus Denmark Physical Chemistry Department of Chemistry Nanosystems Initiative Munich (NIM) Center for Integrated Protein Science Munich (CiPSM) and Center for Nanoscience (CeNS) Ludwig-Maximilians-Universität München Munich Germany Department of Physiology & Biophysics Stony Brook University Stony Brook NY USA Department of Biomedical Engineering University of California Irvine Irvine CA USA Molecular Microscopy Research Group Zernike Institute for Advanced Materials University of Groningen Groningen the Netherlands Physical and Synthetic Biology Faculty of Biology Ludwig-Maximilians-Universität München Planegg-Martinsried Germany Institute for Biophysics Ulm University Ulm Germany Laboratory of Biophysics Wageningen University & Research Wageningen the Netherlands Department of Biomedical Engineering John Hopkins University Baltimore MD USA Department of Physics North Carolina State University Raleigh NC USA B CUBE—Center for Molecular Bioengineering TU Dresden Dresden Germany Laboratory for Photochemistry and Spectroscopy Department of Chemistry University of Leuven Leuven Belgium Dynamic Bioimaging Lab Advanced Optical Microscopy Center and Biomedical Research Institute Hasselt University Hasselt Belgium Institute of Physics University of Lübeck Lübeck Germany Microspectroscopy Research Facility Wageningen Wageningen University & Research Wageningen the Netherla

Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

关键词： Biophysical methods DNA Fluorescence resonance energy transfer Single-molecule biophysics Structure determination

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Human gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes

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BMC Research Notes 2012年第1期5卷 1-11页

作者： Michalopoulos, Ioannis Pavlopoulos, Georgios A. Malatras, Apostolos Karelas, Alexandros Kostadima, Myrto-Areti Schneider, Reinhard Kossida, Sophia Cryobiology of Stem Cells Centre of Immunology and Transplantation Academy of Athens Athens 11527 Soranou Efessiou 4 Greece ESAT-SCD/IBBT-K.U. Leuven Future Health Department Katholieke Universiteit Leuven Heverlee-Leuven 3001 Kasteelpark Arenberg 10 Belgium Department of Cell Biology and Biophysics Faculty of Biology University of Athens Panepistimiopolis Athens 15701 Greece Wellcome Trust Genome Campus European Bioinformatics Institute Cambridge CB10 1SD United Kingdom Bioinformatics and Medical Informatics Team Biomedical Research Foundation Academy of Athens Athens 11527 Soranou Efessiou 4 Greece Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg 69117 Meyerhofstrasse 1 Germany Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Campus Belval Esch sur Alzette L-4362 Avenue des Hauts-Fourneaux 7 Luxembourg

Background: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell. Findings: We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster. Conclusion: Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://***/coexpression/. © 2012 Michalopoulos et al.: licensee BioMed Central Ltd.

关键词： Functional annotation Gene annotation Gene coexpression Microarray analysis

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