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Evolution in Biological and Non-biological Systems: The Origins of Life

Biological Theory 2013年第1期7卷 26-37页

作者： Salazar-Ciudad, Isaac Genomics Bioinformatics and Evolution Group Department of Genetics and Microbiology Universitat Autònoma de Barcelona Barcelona Spain Developmental Biology Program Institute of Biotechnology University of Helsinki Helsinki Finland

A replicator is simply something that makes copies of itself. There are hypothetical replicators (e.g., self-catalyzing chemical cycles) that are suspected to be unable to exhibit heritable variation. Variation in any of their constituent molecules would not lead them to produce offspring with those new variant molecules. Copying, such as in DNA replication or in xerox machines, allows any sequence to be remade and then sequence variations to be inherited. This distinction has been used against non-RNA-world hypotheses: without RNA replication systems have no capacity to exhibit heritable variation. However, is copying the only way to have heritable variation? This article suggests that evolution can happen without anything being copied and that, in fact, RNA copying is too complex to arise spontaneously as the first pre-biotic system. There would then be a historical gradation of systems from non-alive to alive in which the capacity to have heritable changes would progressively increase and in which copying, as in template-based RNA-replication, would be a crucial but relatively late event. In addition, this article proposes a way by which RNA replication could have arisen, after systems able to evolve arose, as a crucial innovation and how that innovation spread over existing evolutionary systems and became a major driver of subsequent evolution. © 2012, Konrad Lorenz Institute for Evolution and Cognition Research.

关键词： Compartment-first Development Evolution Generative systems Origins of life

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FANTOM5 CAGE profiles of human and mouse samples

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Scientific Data 2017年第1期4卷 1-10页

作者： Noguchi, Shuhei Arakawa, Takahiro Fukuda, Shiro Furuno, Masaaki Hasegawa, Akira Hori, Fumi Ishikawa-Kato, Sachi Kaida, Kaoru Kaiho, Ai Kanamori-Katayama, Mutsumi Kawashima, Tsugumi Kojima, Miki Kubosaki, Atsutaka Manabe, Ri-Ichiroh Murata, Mitsuyoshi Nagao-Sato, Sayaka Nakazato, Kenichi Ninomiya, Noriko Nishiyori-Sueki, Hiromi Noma, Shohei Saijyo, Eri Saka, Akiko Sakai, Mizuho Simon, Christophe Suzuki, Naoko Tagami, Michihira Watanabe, Shoko Yoshida, Shigehiro Arner, Peter Axton, Richard A. Babina, Magda Kenneth Baillie, J. Barnett, Timothy C. Beckhouse, Anthony G. Blumenthal, Antje Bodega, Beatrice Bonetti, Alessandro Briggs, James Brombacher, Frank Carlisle, Ailsa J. Clevers, Hans C. Davis, Carrie A. Detmar, Michael Dohi, Taeko Edge, B. Albert S. Edinger, Matthias Ehrlund, Anna Ekwall, Karl Endoh, Mitsuhiro Enomoto, Hideki Eslami, Afsaneh Fagiolini, Michela Fairbairn, Lynsey Farach-Carson, Mary C. Faulkner, Geoffrey J. Ferrai, Carmelo Fisher, Malcolm E. Forrester, Lesley M. Fujita, Rie Furusawa, Jun-Ichi Geijtenbeek, Teunis B. Gingeras, Thomas Goldowitz, Daniel Guhl, Sven Guler, Reto Gustincich, Stefano Ha, Thomas J. Hamaguchi, Masahide Hara, Mitsuko Hasegawa, Yuki Herlyn, Meenhard Heutink, Peter Hitchens, Kelly J. Hume, David A. Ikawa, Tomokatsu Ishizu, Yuri Kai, Chieko Kawamoto, Hiroshi Kawamura, Yuki I. Kempfle, Judith S. Kenna, Tony J. Kere, Juha Khachigian, Levon M. Kitamura, Toshio Klein, Sarah Peter Klinken, S. Knox, Alan J. Kojima, Soichi Koseki, Haruhiko Koyasu, Shigeo Lee, Weonju Lennartsson, Andreas MacKay-Sim, Alan Mejhert, Niklas Mizuno, Yosuke Morikawa, Hiromasa Morimoto, Mitsuru Moro, Kazuyo Morris, Kelly J. Motohashi, Hozumi Mummery, Christine L. Nakachi, Yutaka Nakahara, Fumio Nakamura, Toshiyuki Nakamura, Yukio Nozaki, Tadasuke Ogishima, Soichi Ohkura, Naganari Ohno, Hiroshi Ohshima, Mitsuhiro Okada-Hatakeyama, Mariko Okazaki, Yasushi Orlando, Valerio Ovchinnikov, Dmitry A. Passier, Robert Patrikakis, Margaret Pombo, Ana Pradhan-Bhatt, Swati Qin, Xian-Yang Rehli, Michael Rizzu, Patrizia Roy, S Division of Genomic Technologies RIKEN Center for Life Science Technologies Yokohama Kanagawa 230-0045 Japan RIKEN Omics Science Center Yokohama Kanagawa 230-0045 Japan Department of Medicine Karolinska Institutet Stockholm 14186 Sweden Karolinska University Hospital Center for Metabolism and Endocrinology Stockholm 14186 Sweden Scottish Centre for Regenerative Medicine University of Edinburgh 5 Little France Drive Edinburgh EH164UU United Kingdom Department of Dermatology and Allergy Charite University Medicine Berlin Charitéplatz 1 Berlin 10117 Germany Roslin Institute and Royal School of Veterinary Studies University of Edinburgh Edinburgh EH259RG United Kingdom Australian Infectious Diseases Research Centre University of Queensland St Lucia 4072 QLD Australia School of Chemistry and Molecular Biosciences University of Queensland St Lucia 4072 QLD Australia Bio-Rad Laboratories Pty Ltd Hercules 94547 CA United States University of Queensland Diamantina Institute University of Queensland Woolloongabba 4102 QLD Australia IRCCS Fondazione Santa Lucia Via del Fosso di Fiorano 64 Rome 00143 Italy Australian Institute for Bioengineering and Nanotechnology University of Queensland Brisbane St Lucia 4072 QLD Australia Division of Immunology Institute of Infectious Diseases and Molecular Medicine University of Cape Town Anzio Road Observatory 7925 South Africa Immunology of Infectious Diseases Faculty of Health Sciences South African Medical Research Council University of Cape Town Anzio Road 7925 South Africa International Centre for Genetic Engineering and Biotechnology Cape Town Component Anzio Road 7925 South Africa Hubrecht Institute Royal Netherlands Academy of Arts and Sciences Uppsalalaan 8 3584 Netherlands University Medical Centre Utrecht Postbus 85500 Utrecht 3508 Netherlands Genomics Cold Spring Harbor Laboratory Cold Spring Harbor New York 11797 NY United States Institute of Pharmaceutical Sciences ETH Zurich Vladi

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

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Erratum to: A survey of best practices for RNA-seq data analysis

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Genome biology 2016年第1期17卷 181页

作者： Ana Conesa Pedro Madrigal Sonia Tarazona David Gomez-Cabrero Alejandra Cervera Andrew McPherson Michal Wojciech Szcześniak Daniel J Gaffney Laura L Elo Xuegong Zhang Ali Mortazavi Institute for Food and Agricultural Sciences Department of Microbiology and Cell Science University of Florida Gainesville FL 32603 USA. aconesa@ufl.edu. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. aconesa@ufl.edu. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. pm12@sanger.ac.uk. Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute Anne McLaren Laboratory for Regenerative Medicine Department of Surgery University of Cambridge Cambridge CB2 0SZ UK. pm12@sanger.ac.uk. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. Department of Applied Statistics Operations Research and Quality Universidad Politécnica de Valencia 46020 Valencia Spain. Unit of Computational Medicine Department of Medicine Karolinska Institutet Karolinska University Hospital 171 77 Stockholm Sweden. Center for Molecular Medicine Karolinska Institutet 17177 Stockholm Sweden. Unit of Clinical Epidemiology Department of Medicine Karolinska University Hospital L8 17176 Stockholm Sweden. Science for Life Laboratory 17121 Solna Sweden. Systems Biology Laboratory Institute of Biomedicine and Genome-Scale Biology Research Program University of Helsinki 00014 Helsinki Finland. School of Computing Science Simon Fraser University Burnaby V5A 1S6BC Canada. Department of Bioinformatics Institute of Molecular Biology and Biotechnology Adam Mickiewicz University in Poznań 61-614 Poznań Poland. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. Turku Centre for Biotechnology University of Turku and Åbo Akademi University FI-20520 Turku Finland. Key Lab of Bioinformatics/Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China. School of Life Sciences Tsinghua University Beijing 100084 China. Department of Developmental and Cell Biolo

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Cancer Informatics: Profiling Age-Related Epigenetic Markers of Stomach Adenocarcinoma in Young and Old Subjects

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Cancer Informatics 2015年 14卷

作者： Byoung-Chul Kim Hyoung Oh Jeong Daeui Park Chul-Hong Kim Eun Kyeong Lee Dae Hyun Kim Eunok Im Nam Deuk Kim Sunghoon Lee Byung Pal Yu Jong Bhak Hae Young Chung Molecular Inflammation Research Center for Aging Intervention Pusan National University Busan Republic of Korea.Interdisciplinary Research Program of Bioinformatics and Longevity Science Pusan National University Busan Republic of Korea. Genomictree Inc. Yuseong-gu Daejeon Republic of Korea. Molecular Inflammation Research Center for Aging Intervention Pusan National University Busan Republic of Korea. Personal Genomics Institute Genome Research Foundation Suwon Republic of Korea.BioMedical Engineering UNIST Ulsan Republic of Korea. Department of Physiology University of Texas Health Science Center at San Antonio San Antonio TX USA.

The purpose of our study is to identify epigenetic markers that are differently expressed in the stomach adenocarcinoma (STAD) condition. Based on data from The Cancer Genome Atlas (TCGA), we were able to detect an age-related difference in methylation patterns and changes in gene and miRNA expression levels in young (n = 14) and old (n = 70) STAD subjects. Our analysis identified 323 upregulated and 653 downregulated genes in old STAD subjects. We also found 76 miRNAs with age-related expression patterns and 113 differentially methylated genes (DMGs), respectively. Our further analysis revealed that significant upregulated genes (n = 35) were assigned to the cell cycle, while the muscle system process (n = 27) and cell adhesion-related genes (n = 57) were downregulated. In addition, by comparing gene and miRNA expression with methylation change, we identified that three upregulated genes (ELF3, IL1β, and MMP13) known to be involved in inflammatory responses and cell growth were significantly hypomethylated in the promoter region. We further detected target candidates for age-related, downregulated miRNAs (hsa-mir-124-3, hsa-mir-204, and hsa-mir-125b-2) in old STAD subjects. This is the first report of the results from a study exploring age-related epigenetic biomarkers of STAD using high-throughput data and provides evidence for a complex clinicopathological condition expressed by the age-related STAD progression.

关键词： stomach adenocarcinoma age TCGA RNAseq miRNA DNA methylation

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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The FAIR Guiding Principles for scientific data management and stewardship

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Scientific data 2016年第1期3卷 160018页

作者： Mark D Wilkinson Michel Dumontier I Jsbrand Jan Aalbersberg Gabrielle Appleton Myles Axton Arie Baak Niklas Blomberg Jan-Willem Boiten Luiz Bonino da Silva Santos Philip E Bourne Jildau Bouwman Anthony J Brookes Tim Clark Mercè Crosas Ingrid Dillo Olivier Dumon Scott Edmunds Chris T Evelo Richard Finkers Alejandra Gonzalez-Beltran Alasdair J G Gray Paul Groth Carole Goble Jeffrey S Grethe Jaap Heringa Peter A C 't Hoen Rob Hooft Tobias Kuhn Ruben Kok Joost Kok Scott J Lusher Maryann E Martone Albert Mons Abel L Packer Bengt Persson Philippe Rocca-Serra Marco Roos Rene van Schaik Susanna-Assunta Sansone Erik Schultes Thierry Sengstag Ted Slater George Strawn Morris A Swertz Mark Thompson Johan van der Lei Erik van Mulligen Jan Velterop Andra Waagmeester Peter Wittenburg Katherine Wolstencroft Jun Zhao Barend Mons Center for Plant Biotechnology and Genomics Universidad Politécnica de Madrid Madrid 28223 Spain. Stanford University Stanford 94305-5411 USA. Nature Genetics New York 10004-1562 USA. Euretos and Phortos Consultants Rotterdam 2741 CA The Netherlands. ELIXIR Wellcome Genome Campus Hinxton CB10 1SA UK. Lygature Eindhoven 5656 AG The Netherlands. Vrije Universiteit Amsterdam Dutch Techcenter for Life Sciences Amsterdam 1081 HV The Netherlands. Office of the Director National Institutes of Health Rockville 20892 USA. TNO Zeist 3700 AJ The Netherlands. Department of Genetics University of Leicester Leicester LE1 7RH UK. Harvard Medical School Boston Massachusetts MA 02115 USA. Harvard University Cambridge Massachusetts MA 02138 USA. Data Archiving and Networked Services (DANS) The Hague 2593 HW The Netherlands. GigaScience Beijing Genomics Institute Shenzhen 518083 China. Department of Bioinformatics Maastricht University Maastricht 6200 MD The Netherlands. Wageningen UR Plant Breeding Wageningen 6708 PB The Netherlands. Oxford e-Research Center University of Oxford Oxford OX1 3QG UK. Heriot-Watt University Edinburgh EH14 4AS UK. School of Computer Science University of Manchester Manchester M13 9PL UK. Center for Research in Biological Systems School of Medicine University of California San Diego La Jolla California 92093-0446 USA. Dutch Techcenter for the Life Sciences Utrecht 3501 DE The Netherlands. Department of Human Genetics Leiden University Medical Center Dutch Techcenter for the Life Sciences Leiden 2300 RC The Netherlands. Dutch TechCenter for Life Sciences and ELIXIR-NL Utrecht 3501 DE The Netherlands. VU University Amsterdam Amsterdam 1081 HV The Netherlands. Leiden Center of Data Science Leiden University Leiden 2300 RA The Netherlands. Netherlands eScience Center Amsterdam 1098 XG The Netherlands. National Center for Microscopy and Imaging Research UCSD San Diego 92103 USA. Phortos Consultants San Diego 92011 USA. SciELO/FAPESP Pr

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass

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Nature communications 2017年第1期8卷 1414页

作者： M Carola Zillikens Serkalem Demissie Yi-Hsiang Hsu Laura M Yerges-Armstrong Wen-Chi Chou Lisette Stolk Gregory Livshits Linda Broer Toby Johnson Daniel L Koller Zoltán Kutalik Jian'an Luan Ida Malkin Janina S Ried Albert V Smith Gudmar Thorleifsson Liesbeth Vandenput Jing Hua Zhao Weihua Zhang Ali Aghdassi Kristina Åkesson Najaf Amin Leslie J Baier Inês Barroso David A Bennett Lars Bertram Rainer Biffar Murielle Bochud Michael Boehnke Ingrid B Borecki Aron S Buchman Liisa Byberg Harry Campbell Natalia Campos Obanda Jane A Cauley Peggy M Cawthon Henna Cederberg Zhao Chen Nam H Cho Hyung Jin Choi Melina Claussnitzer Francis Collins Steven R Cummings Philip L De Jager Ilja Demuth Rosalie A M Dhonukshe-Rutten Luda Diatchenko Gudny Eiriksdottir Anke W Enneman Mike Erdos Johan G Eriksson Joel Eriksson Karol Estrada Daniel S Evans Mary F Feitosa Mao Fu Melissa Garcia Christian Gieger Thomas Girke Nicole L Glazer Harald Grallert Jagvir Grewal Bok-Ghee Han Robert L Hanson Caroline Hayward Albert Hofman Eric P Hoffman Georg Homuth Wen-Chi Hsueh Monica J Hubal Alan Hubbard Kim M Huffman Lise B Husted Thomas Illig Erik Ingelsson Till Ittermann John-Olov Jansson Joanne M Jordan Antti Jula Magnus Karlsson Kay-Tee Khaw Tuomas O Kilpeläinen Norman Klopp Jacqueline S L Kloth Heikki A Koistinen William E Kraus Stephen Kritchevsky Teemu Kuulasmaa Johanna Kuusisto Markku Laakso Jari Lahti Thomas Lang Bente L Langdahl Lenore J Launer Jong-Young Lee Markus M Lerch Joshua R Lewis Lars Lind Cecilia Lindgren Yongmei Liu Tian Liu Youfang Liu Östen Ljunggren Mattias Lorentzon Robert N Luben William Maixner Fiona E McGuigan Carolina Medina-Gomez Thomas Meitinger Håkan Melhus Dan Mellström Simon Melov Karl Michaëlsson Braxton D Mitchell Andrew P Morris Leif Mosekilde Anne Newman Carrie M Nielson Jeffrey R O'Connell Ben A Oostra Eric S Orwoll Aarno Palotie Stephen C J Parker Munro Peacock Markus Perola Annette Peters Ozren Polasek Richard L Prince Katri Räikkönen Stuart H Ralston Samuli Ripatti John A Robbins Jerome I Rotter Igor Rudan Veikko Department of Internal Medicine Erasmus MC Rotterdam 3000 The Netherlands. Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA) Leiden 2593 The Netherlands. Department of Biostatistics Boston University School of Public Health Boston MA 02118 USA. Hebrew SeniorLife Institute for Aging Research Roslindale MA 02131 USA. Harvard Medical School Boston MA 02115 USA. Molecular and Integrative Physiological Sciences Program Harvard School of Public Health Boston MA 02115 USA. Program in Personalized and Genomic Medicine and Department of Medicine Division of Endocrinology Diabetes and Nutrition University of Maryland School of Medicine Baltimore MD 21201 USA. Broad Institute Cambridge MA 02142 USA. Sackler Faculty of Medicine Department of Anatomy and Anthropology Tel Aviv University Tel Aviv 6997801 Israel. Department of Twin Research and Genetic Epidemiology King's College London St Thomas' Campus London WC2R 2LS UK. Department of Epidemiology Erasmus MC Rotterdam 3000 The Netherlands. Department of Medical Genetics University of Lausanne Lausanne 1011 Switzerland. Swiss Institute of Bioinformatics Lausanne 1015 Switzerland. Centre Hospitalier Universitaire (CHUV) University Institute for Social and Preventive Medicine Lausanne 1010 Switzerland. Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN 46202 USA. MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge Biomedical Campus Cambridge CB2 OQQ UK. Institute of Epidemiology II Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany. Icelandic Heart Association Kopavogur 201 Iceland. Faculty of Medicine University of Iceland Reykjavik 101 Iceland. deCODE Genetics Reykjavik 101 Iceland. Department of Internal Medicine Institute of Medicine Sahlgrenska Academy University of Gothenburg Gothenburg SE-405 30 Sweden. Dep

A correction to this article has been published and is linked from the HTML version of this article.

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Treetrimmer: A method for phylogenetic dataset size reduction

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BMC Research Notes 2013年第1期6卷 1-6页

作者： Maruyama, Shinichiro Eveleigh, Robert Jm Archibald, John M Department of Biochemistry and Molecular Biology Dalhousie University Halifax NS Canada Centre for Comparative Genomics and Evolutionary Bioinformatics Dalhousie University Halifax NS Canada Integrated Microbial Biodiversity Program Canadian Institute for Advanced Research Montreal QC H3A 1A4 Canada McGill University and Génome Québec Montreal QC H3A 1A4 740 Docteur-Penfield Ave Canada

Background: With rapid advances in genome sequencing and bioinformatics, it is now possible to generate phylogenetic trees containing thousands of operational taxonomic units (OTUs) from a wide range of organisms. However, use of rigorous tree-building methods on such large datasets is prohibitive and manual 'pruning' of sequence alignments is time consuming and raises concerns over reproducibility. There is a need for bioinformatic tools with which to objectively carry out such pruning procedures. Findings. Here we present 'TreeTrimmer', a bioinformatics procedure that removes unnecessary redundancy in large phylogenetic datasets, alleviating the size effect on more rigorous downstream analyses. The method identifies and removes user-defined 'redundant' sequences, e.g., orthologous sequences from closely related organisms and 'recently' evolved lineage-specific paralogs. Representative OTUs are retained for more rigorous re-analysis. Conclusions: TreeTrimmer reduces the OTU density of phylogenetic trees without sacrificing taxonomic diversity while retaining the original tree topology, thereby speeding up downstream computer-intensive analyses, e.g., Bayesian and maximum likelihood tree reconstructions, in a reproducible fashion. © 2013 Maruyama et al.;licensee BioMed Central Ltd.

关键词： Dereplication Phylogenetic tree Pruning Taxonomic category TreeTrimmer

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Differentially expressed genes in adipose tissues reveals that ECM-receptor interaction affects on the depot-specific adipogenesis in Hanwoo

Differentially expressed genes in adipose tissues reveals th...

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第11届世界畜产大会(WCAP2013)

作者： Mi Jang Hyun-Jeong Lee Hyeongmin Kim Woori Kwak WonCheoul Park Jae-Yeon Hwang Chang-Kyu Lee Gul Won Jang Mi Na Park Hyeong-Cheol Kim Jin Young Jeong Kang Seok Seo Heebal Kim Seoae Cho Bo-Young Lee C&K Genomics Seoul National University NakSeoungDaeGwanakguSeoulKorea Department of Agricultural Biotechnology and Research Institute for Agriculture and Life ScienceSeoul National UniversitySeoulKorea Department of Animal Science and TechnologyCollege of Life Science and Natural ResourcesSunchon National UniversitySeoulRepublic of Korea C&K GenomicsSeoul National University NakSeoungDaeGwanakguSeoulKorea C&K Genomics Seoul National University NakSeoungDaeGwanakguSeoulKorea Department of Agricultural Biotechnology and Research Institute of Population GenomicsSeoul National UniversitySeoulRepublic of Korea Department of Animal Science and Technology College of Life Science and Natural ResourcesSunchon National UniversitySeoulRepublic of Korea Division of Animal Genomics and BioinformaticsNational Institute of Animal scienceRural Development AdministrationSuwonRepublic of Korea Department of Agricultural Biotechnology and Research Institute of Population GenomicsSeoul National UniversitySeoulRepublic of Korea Interdisciplinary Program in BioinformaticsSeoul National UniversityKorea Division of Animal Genomics and Bioinformatics National Institute of Animal scienceRural Development AdministrationSuwonRepublic of Korea

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