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A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles
ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles Andreas Hoppe and Hermann-Georg Holzhütter Charité University Medicine Berlin, Institute for Biochemistry, Computational Systems Biochemistry Group [email protected] Abstract Genome-wide transcript profiles are often the only available quantitative data for a particular perturbation of a cellular system and their interpretation with respect to the metabolism is a major challenge in systems biology, especially beyond on/off distinction of genes. We present a method that predicts activity changes of metabolic functions by scoring reference flux distributions based on relative transcript profiles, providing a ranked list of most regulated functions. Then, for each metabolic function, the involved genes are ranked upon how much they represent a specific regulation pattern. Compared with the naïve pathway-based approach, the reference modes can be chosen freely, and they represent full metabolic functions, thus, directly provide testable hypotheses for the metabolic study. In conclusion, the novel method provides promising functions for subsequent experimental elucidation together with outstanding associated genes, solely based on transcript profiles. 1998 ACM Subject Classification J.3 Life and Medical Sciences Keywords and phrases Metabolic network, expression profile, metabolic function Digital Object Identifier 10.4230/OASIcs.GCB.2012.1 1 Background The comprehensive study of the cell’s metabolism would include measuring metabolite concentrations, reaction fluxes, and enzyme activities on a large scale. Measuring fluxes is the most difficult part in this, for a recent assessment of techniques, see [31]. Although mass spectrometry allows to assess metabolite concentrations in a more comprehensive way, the larger the set of potential metabolites, the more difficult [8]. -
The Kyoto Encyclopedia of Genes and Genomes (KEGG)
Kyoto Encyclopedia of Genes and Genome Minoru Kanehisa Institute for Chemical Research, Kyoto University HFSPO Workshop, Strasbourg, November 18, 2016 The KEGG Databases Category Database Content PATHWAY KEGG pathway maps Systems information BRITE BRITE functional hierarchies MODULE KEGG modules KO (KEGG ORTHOLOGY) KO groups for functional orthologs Genomic information GENOME KEGG organisms, viruses and addendum GENES / SSDB Genes and proteins / sequence similarity COMPOUND Chemical compounds GLYCAN Glycans Chemical information REACTION / RCLASS Reactions / reaction classes ENZYME Enzyme nomenclature DISEASE Human diseases DRUG / DGROUP Drugs / drug groups Health information ENVIRON Health-related substances (KEGG MEDICUS) JAPIC Japanese drug labels DailyMed FDA drug labels 12 manually curated original DBs 3 DBs taken from outside sources and given original annotations (GENOME, GENES, ENZYME) 1 computationally generated DB (SSDB) 2 outside DBs (JAPIC, DailyMed) KEGG is widely used for functional interpretation and practical application of genome sequences and other high-throughput data KO PATHWAY GENOME BRITE DISEASE GENES MODULE DRUG Genome Molecular High-level Practical Metagenome functions functions applications Transcriptome etc. Metabolome Glycome etc. COMPOUND GLYCAN REACTION Funding Annual budget Period Funding source (USD) 1995-2010 Supported by 10+ grants from Ministry of Education, >2 M Japan Society for Promotion of Science (JSPS) and Japan Science and Technology Agency (JST) 2011-2013 Supported by National Bioscience Database Center 0.8 M (NBDC) of JST 2014-2016 Supported by NBDC 0.5 M 2017- ? 1995 KEGG website made freely available 1997 KEGG FTP site made freely available 2011 Plea to support KEGG KEGG FTP academic subscription introduced 1998 First commercial licensing Contingency Plan 1999 Pathway Solutions Inc. -
Distinctive Regulatory Architectures of Germline-Active and Somatic Genes in C
Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Research Distinctive regulatory architectures of germline-active and somatic genes in C. elegans Jacques Serizay, Yan Dong, Jürgen Jänes, Michael Chesney, Chiara Cerrato, and Julie Ahringer The Gurdon Institute and Department of Genetics, University of Cambridge, CB2 1QN Cambridge, United Kingdom RNA profiling has provided increasingly detailed knowledge of gene expression patterns, yet the different regulatory ar- chitectures that drive them are not well understood. To address this, we profiled and compared transcriptional and regu- latory element activities across five tissues of Caenorhabditis elegans, covering ∼90% of cells. We find that the majority of promoters and enhancers have tissue-specific accessibility, and we discover regulatory grammars associated with ubiquitous, germline, and somatic tissue–specific gene expression patterns. In addition, we find that germline-active and soma-specific promoters have distinct features. Germline-active promoters have well-positioned +1 and −1 nucleosomes associated with a periodic 10-bp WW signal (W = A/T). Somatic tissue–specific promoters lack positioned nucleosomes and this signal, have wide nucleosome-depleted regions, and are more enriched for core promoter elements, which largely differ between tissues. We observe the 10-bp periodic WW signal at ubiquitous promoters in other animals, suggesting it is an ancient conserved signal. Our results show fundamental differences in regulatory architectures of germline and somatic tissue–specific genes, uncover regulatory rules for generating diverse gene expression patterns, and provide a tissue-specific resource for future studies. [Supplemental material is available for this article.] Cell type–specific transcription regulation underlies production tissues are achieved and whether expression is governed by dis- of the myriad of different cells generated during development. -
Jawaharlal Nehru University, New Delhi
Jawaharlal Nehru University, New Delhi Evaluative Report of the Department (science) Name of the School/Spl. Center Pages 1 School of Life Sciences 1-45 2. School of Biotechnology 46-100 3 School of Computer and systems Sciences 101-120 4 School of Computational and Integrative Sciences 121-141 5. School of Physical Sciences 142-160 6 School of Environmental Sciences 161-197 7 Spl. Center for Molecular Medicine 198-256 8 Spl Center for Nano Sciences 257-268 Evaluation Report of School of Life Sciences In the past century, biology, with inputs from other disciplines, has made tremendous progress in terms of advancement of knowledge, development of technology and its applications. As a consequence, in the past fifty years, there has been a paradigm shift in our interpreting the life process. In the process, modern biology had acquired a truly interdisciplinary character in which all streams of sciences have made monumental contributions. Because of such rapid emergence as a premier subject of teaching and research; a necessity to restructure classical teachings in biology was recognised by the academics worldwide. In tune with such trends, the academic leadership of Jawaharlal Nehru University conceptualised the School of Life Sciences as an interdisciplinary research/teaching programme unifying various facets of biology while reflecting essential commonality regarding structure, function and evolution of biomolecules. The School was established in 1973 and since offering integrated teaching and research at M. Sc/ Ph.D level in various sub-disciplines in life sciences. Since inception, it remained dedicated to its core objectives and evolved to be one of the top such institutions in India and perhaps in South East Asia. -
High-Throughput, Pooled Sequencing Identifies Mutations in NUBPL And
ARTICLES High-throughput, pooled sequencing identifies mutations in NUBPL and FOXRED1 in human complex I deficiency Sarah E Calvo1–3,10, Elena J Tucker4,5,10, Alison G Compton4,10, Denise M Kirby4, Gabriel Crawford3, Noel P Burtt3, Manuel Rivas1,3, Candace Guiducci3, Damien L Bruno4, Olga A Goldberger1,2, Michelle C Redman3, Esko Wiltshire6,7, Callum J Wilson8, David Altshuler1,3,9, Stacey B Gabriel3, Mark J Daly1,3, David R Thorburn4,5 & Vamsi K Mootha1–3 Discovering the molecular basis of mitochondrial respiratory chain disease is challenging given the large number of both mitochondrial and nuclear genes that are involved. We report a strategy of focused candidate gene prediction, high-throughput sequencing and experimental validation to uncover the molecular basis of mitochondrial complex I disorders. We created seven pools of DNA from a cohort of 103 cases and 42 healthy controls and then performed deep sequencing of 103 candidate genes to identify 151 rare variants that were predicted to affect protein function. We established genetic diagnoses in 13 of 60 previously unsolved cases using confirmatory experiments, including cDNA complementation to show that mutations in NUBPL and FOXRED1 can cause complex I deficiency. Our study illustrates how large-scale sequencing, coupled with functional prediction and experimental validation, can be used to identify causal mutations in individual cases. Complex I of the mitochondrial respiratory chain is a large ~1-MDa assembly factors are probably required, as suggested by the 20 factors macromolecular machine composed of 45 protein subunits encoded necessary for assembly of the smaller complex IV9 and by cohort by both the nuclear and mitochondrial (mtDNA) genomes. -
Semantic Web
SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES Edited by Christopher J. O. Baker1 and Kei-Hoi Cheung2 1Knowledge Discovery Department, Institute for Infocomm Research, Singapore, Singapore; 2Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA Kluwer Academic Publishers Boston/Dordrecht/London Contents PART I: Database and Literature Integration Semantic web approach to database integration in the life sciences KEI-HOI CHEUNG, ANDREW K. SMITH, KEVIN Y. L. YIP, CHRISTOPHER J. O. BAKER AND MARK B. GERSTEIN Querying Semantic Web Contents: A case study LOIC ROYER, BENEDIKT LINSE, THOMAS WÄCHTER, TIM FURCH, FRANCOIS BRY, AND MICHAEL SCHROEDER Knowledge Acquisition from the Biomedical Literature LYNETTE HIRSCHMAN, WILLIAM HAYES AND ALFONSO VALENCIA PART II: Ontologies in the Life Sciences Biological Ontologies PATRICK LAMBRIX, HE TAN, VAIDA JAKONIENE, AND LENA STRÖMBÄCK Clinical Ontologies YVES LUSSIER AND OLIVIER BODENREIDER Ontology Engineering For Biological Applications vi Revolutionizing knowledge discovery in the life sciences LARISA N. SOLDATOVA AND ROSS D. KING The Evaluation of Ontologies: Toward Improved Semantic Interoperability LEO OBRST, WERNER CEUSTERS, INDERJEET MANI, STEVE RAY AND BARRY SMITH OWL for the Novice JEFF PAN PART III: Ontology Visualization Techniques for Ontology Visualization XIAOSHU WANG AND JONAS ALMEIDA On Vizualization of OWL Ontologies SERGUEI KRIVOV, FERDINANDO VILLA, RICHARD WILLIAMS, AND XINDONG WU PART IV: Ontologies in Action Applying OWL Reasoning to Genomics: A Case Study KATY WOLSTENCROFT, ROBERT STEVENS AND VOLKER HAARSLEV Can Semantic Web Technologies enable Translational Medicine? VIPUL KASHYAP, TONYA HONGSERMEIER AND SAMUEL J. ARONSON Ontology Design for Biomedical Text Mining RENÉ WITTE, THOMAS KAPPLER, AND CHRISTOPHER J. -
Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus
LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Genome Informatics 4–8 September 2002, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
Comparative and Functional Genomics Comp Funct Genom 2003; 4: 509–514. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.300 Feature Meeting Highlights: Genome Informatics 4–8 September 2002, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK Jo Wixon1* and Jennifer Ashurst2 1MRC UK HGMP-RC, Hinxton, Cambridge CB10 1SB, UK 2The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK *Correspondence to: Abstract Jo Wixon, MRC UK HGMP-RC, Hinxton, Cambridge CB10 We bring you the highlights of the second Joint Cold Spring Harbor Laboratory 1SB, UK. and Wellcome Trust ‘Genome Informatics’ Conference, organized by Ewan Birney, E-mail: [email protected] Suzanna Lewis and Lincoln Stein. There were sessions on in silico data discovery, comparative genomics, annotation pipelines, functional genomics and integrative biology. The conference included a keynote address by Sydney Brenner, who was awarded the 2002 Nobel Prize in Physiology or Medicine (jointly with John Sulston and H. Robert Horvitz) a month later. Copyright 2003 John Wiley & Sons, Ltd. In silico data discovery background set was genes which had a log ratio of ∼0 in liver. Their approach found 17 of 17 known In the first of two sessions on this topic, Naoya promoters with a specificity of 17/28. None of the Hata (Cold Spring Harbor Laboratory, USA) sites they identified was located downstream of a spoke about motif searching for tissue specific TSS and all showed an excess in the foreground promoters. The first step in the process is to sample compared to the background sample. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Gurdon Institute 20122011 PROSPECTUS / ANNUAL REPORT 20112010
The Wellcome Trust/Cancer Research UK Gurdon Institute 20122011 PROSPECTUS / ANNUAL REPORT 20112010 Gurdon I N S T I T U T E PROSPECTUS 2012 ANNUAL REPORT 2011 http://www.gurdon.cam.ac.uk CONTENTS THE INSTITUTE IN 2011 INTRODUCTION........................................................................................................................................3 HISTORICAL BACKGROUND..........................................................................................................4 CENTRAL SUPPORT SERVICES....................................................................................................5 FUNDING.........................................................................................................................................................5 RETREAT............................................................................................................................................................5 RESEARCH GROUPS.........................................................................................................6 MEMBERS OF THE INSTITUTE................................................................................44 CATEGORIES OF APPOINTMENT..............................................................................44 POSTGRADUATE OPPORTUNITIES..........................................................................44 SENIOR GROUP LEADERS.............................................................................................44 GROUP LEADERS.......................................................................................................................48 -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective .