DOCSLIB.ORG

SOAP/WSDL Interface to Consensuspathdb

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
SOAP/WSDL Interface to Consensuspathdb SOAP/WSDL interface to ConsensusPathDB Thomas Meinel, Konstantin Pentchev, Atanas Kamburov Manual version 1.01: May 6, 2009 Web service access The Web service access to ConsensusPathDB is available through the WSDL file. The file is available and accessible at: http://cpdb.molgen.mpg.de/ws/CPDB.wsdl. All provided web service methods are originally defined and documented therein. They can be retrieved from there and are explained in the section ’web service methods’ of this manual. Server description The server of a web service is the endpoint for a request message access. It is the interface of the web service request and the database access. The server retrieves the database query result and returns the response message. The ConsensusPathDB database access and web interface is constructed in Python. Therefore, the web service module in the web server environment is written in Python: The Zolera SOAP Infrastructure ZSI, http://pywebsvcs.sourceforge.net, is used for ConsensusPathDB because it can handle complex data types in contrast to SOAPpy. The given WSDL file describes the provided methods (functions, operations; synonyms) of ConsensusPathDB in correspondence to existing database queries. The program wsdl2py has been used to generate two Python files automatically from the WSDL file, a class file and a data types file. A server script (endpoint) has been generated, which technically integrates the data structure of the messages (this is supported by the two files) with the database connection. Hints for client generation A WSDL file comprises all necessary information that must be published if a client shall be used, from accessing the server endpoint to the data structure of single methods. The Consen- susPathDB WSDL file is constructed using method parameters of type ’complexTypes’. This allows the serialization of two or more data arrays in a SOAP message, which is needed for sev- eral ConsensusPathDB methods. In those cases, the arrays have to be combined by individual programming in a respective client script. A server should allow the access from different types of platforms a client can reside. There are good experiences to use the Python ZSI environment, the Perl XML:Compile:WSDL11 environment, as well as the Taverna workflow management system. Other WSDL-supporting 1 modules like Python SOAPpy or Perl SOAP:Lite cannot regard complex data types and are therefore unable to support the ConsensusPathDB web service access. We provide examples of clients for single methods and platforms in a separate section of this documentation and in the Embrace Registry, http://www.embraceregistry.net/. 2 Web service methods General methods 1 getDatabaseVersion Returns the current version and build date of ConsensusPathDB. parameter name data type input parameter (none) (none) output explanation current CPDB version parameter name data type output parameter CPDB version string example getDatabaseVersion() output 9 (08.01.2009) status: Perl-client: OK Python-client: OK 2 getSourceDatabaseVersions Returns a list of strings containing the versions of integrated source databases. parameter name data type input parameter (none) (none) output explanation two string arrays of integrated source databases and database versions parameter name data type output parameter 1 databases array of strings . new: database array of strings output parameter 2 versions array of strings . new: version array of strings example getSourceDatabaseVersions() output 1 Reactome; KEGG; HumanCyc; PID; . output 2 27; 49.0; 12.5; 2008 12 09; ... status: Perl-client: OK Python-client: OK 3 getNames Retrieves the names of any object in ConsensusPathDB given by its identifier. Different names correspond to the annotations in the source databases. input explanation any ConsensusPathDB entity identifier parameter name data type input parameter CPDB entityID integer output explanation annotation names parameter name data type output parameter entityNames array of strings . new: entityName array of strings example getNames(186) input 186 output ATP; Adenosine 5‘-triphosphate; adenylpyrophosphate; . status: Perl-client: OK Python-client: OK 3 4 getDBrefs Identifiers of external databases are integrated in ConsensusPathDB. This access method offers a retracing to the original resource annotation for a given ConsensusPathDB entity. input explanation any ConsensusPathDB entity identifier parameter name data type input parameter CPDB entityID integer output explanation annotation names parameter name data type output parameter 1 accType array of strings output parameter 2 acc array of strings . new: databaseKey array of strings . new: databaseID array of strings example getDBrefs(186) input 186 output 1 pubchem, kegg, reactome, . output 2 8143180, C00002, 29358; . status: Perl-client: OK Python-client: nonsense ID induces no error, but response message is not conform 4 Methods connected with an over-representation analysis 5 ORApathways ConsensusPathDB provides an over-representation analysis for entities in a pathway searching all pathways. Entities are handled to the method as a list of identifiers of a foreign database. Additionally, the the minimum overlap between pathway members and the input list (default: 2) is required as well as the respective database key (’hgnc’, ’uniprot’, ’ensembl’, . ). The result is a list of pathways that are found according the in- put parameters. It comprises the pathway name, the source database key of the pathway, all members of a pathway (by ConsensusPathDB ID), pathway members overlapping with the input (by ConsensusPathDB ID), the overall number of elements in the pathway, the number of overlapping members, and the p-value that is calculated with the hypergeo- metric distribution. input explanation parameter name data type input parameter 1 CPDB idType string input parameter 2 CPDB idList array of strings . new: databaseKey string . new: databaseID array of strings input parameter 3 bol integer output explanation list of a list of seven parameters parameter name data type output parameter 1 pValue float output parameter 2 pathway string output parameter 3 database string . new databaseKey string output parameter 4 pathway members space-delimited string of identifiers . new pathwayMember space-delimited string of identifiers output parameter 5 membersInputOverlap space-delimited string of identifiers . new: overlapMember space-delimited string of identifiers output parameter 6 pathwaySize integer output parameter 7 overlapSize integer example ORApathways(hgnc;’EIF3A,TUBA3D’;1) (the first element list is here displayed, only) input hgnc input (EIF3A, TUBA3D) input 1 output 1, 1. element list 0.000001 output 2, 1. element list eukaryotic protein translation output 3, 1. element list BioCarta output 4, 1. element list 16819, 16835, 19134, 19234, . output 5, 1. element list 19234 output 6, 1. element list 18 output 7, 1. element list 1 status: list of all database keys ??? Perl-client: OK Python-client: nonsense ID induces no error, but response message is not conform 5 Workflows: combination of methods The combination of methods elucidates the ConsensusPathDB database content, which is orga- nized in instances and explanations. Instances are pathways, interactions, or entities with the special cases of complexes, groups and interaction partners. They are represented by Consen- susPathDB identifiers. Explanations are given by names, references, entity types, or interaction types and consist of verbose terms or abbreviations. queriable instances with retrieval methods instance queriable by method method names pathway 12 searchPathway interaction 8, 14 getInteractionsOfEntity, getInteractionsOfPathway entity 5, 13 searchEntity, getEntitiesOfPathway complex components 10 getComponentsOfComplex group members 11 getMembersOfGroupEntity interaction partners 7 queriable explanations with retrieval methods explanation queriable by method method names names 3 getNames references 4 getDBrefs type 6, 9 getEntityType, getInteractionType Presented methods perform the retrieval of instances and explanations, the combination of methods make the database query result human readable. Meaningful combinations, initially based on querying entities or pathways, are presented in the figure below. 6 Client scripts Python clients using the ZSI module Prepare class file CPDB 1 04 services.py and types file CPDB 1 04 services types.py: unix/linux/console> wsdl2py -u http://cpdb.molgen.mpg.de/ws/CPDB.wsdl The handling of complex data types, array of arrays, in Python is performed by the zip function as given in method 15, ORApathways. 1. getDatabaseVersion from CPDB 1 04 services import ∗ # including message tracing import sys kw = { ’tracefile’ : sys.stdout } methodLocator = CPDBLocator() .getCPDB portType (∗∗kw) # or without message tracing methodLocator = CPDBLocator() .getCPDB portType ( ) # Build the request request = getDatabaseVersionRequest() # Call the service response = methodLocator.getDatabaseVersion(request) # Extract the result result = response. CPDB version print r e s u l t 3. getNames from CPDB 1 04 services import ∗ methodLocator = CPDBLocator() .getCPDB portType ( ) # Build the request request = getNamesRequest() r e q u e s t . CPDB entityID = 2010 # Call the service response = methodLocator.getNames(request) # Extract the result result = response. entityNames print r e s u l t 15. ORApathways from CPDB 1 04 services import ∗ methodLocator = CPDBLocator() .getCPDB portType ( ) # Build the request request = ORApathwaysRequest() r e q u e s t . CPDB idType = ’hgnc’ r e q
Load more

Recommended publications
  • STRING V10: Protein–Protein Interaction Networks, Integrated
    Published online 28 October 2014 Nucleic Acids Research, 2015, Vol. 43, Database issue D447–D452 doi: 10.1093/nar/gku1003 STRING v10: protein–protein interaction networks, integrated over the tree of life Damian Szklarczyk1, Andrea Franceschini1, Stefan Wyder1, Kristoffer Forslund2, Davide Heller1, Jaime Huerta-Cepas2, Milan Simonovic1, Alexander Roth1, Alberto Santos3, Kalliopi P. Tsafou3, Michael Kuhn4,5, Peer Bork2,*, Lars J. Jensen3,* and Christian von Mering1,* 1Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, 2European Molecular Biology Laboratory, 69117 Heidelberg, Germany, 3Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark, 4Biotechnology Center, Technische Universitat¨ Dresden, 01062 Dresden, Germany and 5Max Planck Institute of Molecular Cell Biology and Downloaded from Genetics, 01062 Dresden, Germany Received September 15, 2014; Accepted October 07, 2014 ABSTRACT INTRODUCTION http://nar.oxfordjournals.org/ The many functional partnerships and interactions For a full description of a protein’s function, knowledge that occur between proteins are at the core of cel- about its specific interaction partners is an important pre- lular processing and their systematic characteriza- requisite. The concept of protein ‘function’ is somewhat hi- tion helps to provide context in molecular systems erarchical (1–4), and at all levels in this hierarchy, interac- biology. However, known and predicted interactions tions between proteins help to describe and narrow down a protein’s function: its three-dimensional structure may are scattered over multiple resources, and the avail- become meaningful only in the context of a larger pro- able data exhibit notable differences in terms of qual- tein assembly, its molecular actions may be regulated by at University of Aarhus on January 22, 2015 ity and completeness.
    [Show full text]
  • Reactome Pathway Analysis
    Fabregat et al. BMC Bioinformatics (2017) 18:142 DOI 10.1186/s12859-017-1559-2 SOFTWARE Open Access Reactome pathway analysis: a high- performance in-memory approach Antonio Fabregat1,2, Konstantinos Sidiropoulos1, Guilherme Viteri1, Oscar Forner1, Pablo Marin-Garcia3,4, Vicente Arnau5,6, Peter D’Eustachio7, Lincoln Stein8,9 and Henning Hermjakob1,10* Abstract Background: Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results: Here, we present a new high-performance in-memory implementation of the well-established over- representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion: Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data.
    [Show full text]
  • Network-Based Association of Hypoxia-Responsive Genes with Cardiovascular Diseases
    Network-based association of hypoxia-responsive genes with cardiovascular diseases Rui-Sheng Wang, William M Oldham and Joseph Loscalzo Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA E-mail: [email protected] Received 19 May 2014, revised 21 July 2014 Accepted for publication 11 August 2014 Published 24 October 2014 New Journal of Physics 16 (2014) 105014 doi:10.1088/1367-2630/16/10/105014 Abstract Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiol- ogy of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia- CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology.
    [Show full text]
  • Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
    biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is.
    [Show full text]
  • The 2021 Update of the EPA's Adverse Outcome Pathway Database
    www.nature.com/scientificdata OPEN The 2021 update of the EPA’s Data DescRiptor adverse outcome pathway database Holly M. Mortensen1 ✉ , Jonathan Senn2, Trevor Levey2,3, Phillip Langley2,4 & Antony J. Williams5 The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the frst publicly available AOP-DB web user interface. Potential users of the data may investigate specifc molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern. Background & Summary Tere is a need for approaches to understand the biological mechanism of adverse outcomes and human varia- bility in response to environmental chemical exposure. A recent legislation, the Frank R. Lautenberg Chemical Safety for the twenty-frst Century Act of 20161, requires the US Environmental Protection Agency to evaluate new and existing toxic chemicals with explicit consideration of susceptible populations of all types (life stage, exposure, genetic, etc.).
    [Show full text]
  • Comparative Expression Analysis in Small Cell Lung Carcinoma Reveals Neuroendocrine Pattern Change in Primary Tumor Versus Lymph Node Metastases
    950 Original Article Comparative expression analysis in small cell lung carcinoma reveals neuroendocrine pattern change in primary tumor versus lymph node metastases Zoltan Lohinai1#, Zsolt Megyesfalvi1,2,3#, Kenichi Suda4, Tunde Harko5, Shengxiang Ren6, Judit Moldvay1, Viktoria Laszlo1,3, Christopher Rivard7, Balazs Dome1,2,3, Fred R. Hirsch7,8 1Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, Hungary; 2Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; 3Division of Thoracic Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria; 4Division of Thoracic Surgery, Department of Surgery, Faculty of Medicine, Kindai University, Osaka-Sayama, Japan; 5Department of Pathology, National Koranyi Institute of Pulmonology, Budapest, Hungary; 6Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China; 7Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA; 8Tisch Cancer Institute, Center for Thoracic Oncology, Mount Sinai Health System, New York, NY, USA Contributions: (I) Conception and design: Z Lohinai, Z Megyesfalvi, C Rivard, B Dome, FR Hirsch; (II) Administrative support: K Suda, T Harko, J Moldvay, S Ren; (III) Provision of study materials or patients: Z Lohinai, Z Megyesfalvi, J Moldvay; (IV) Collection and assembly of data: Z Lohinai, Z Megyesfalvi, V Laszlo; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Zoltan Lohinai, MD, PhD. Department of Tumor Biology, National Koranyi Institute of Pulmonology, H-1121, Piheno ut 1., Budapest, Hungary. Email: [email protected]; Balazs Dome, MD, PhD. Division of Thoracic Surgery, Department of Surgery, Medical University of Vienna, Austria, Waehringer Guertel 18-20, A-1090 Vienna, Austria.
    [Show full text]
  • Profiling Cellular Processes in Adipose Tissue During Weight Loss Using Time Series Gene Expression Samar H.K
    Profiling cellular processes in adipose tissue during weight loss using time series gene expression Samar H.K. Tareen1,*, Michiel E. Adriaens1, Ilja C.W. Arts1,2, Theo M. de Kok1,3, Roel G. Vink4, Nadia J.T. Roumans4, Marleen A. van Baak4, Edwin C.M. Mariman4, Chris T. Evelo1,5, and Martina Kutmon1,5 1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands 2Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands 3Department of Toxicogenomics, GROW School of Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands 4Department of Human Biology, NUTRIM Research School, Maastricht University, Maastricht, the Netherlands 5Department of Bioinformatics - BiGCaT, NUTRIM Research School, Maastricht University, Maastricht, the Netherlands *[email protected] Supplementary Information Figures 2 Figure S1: Principal component analysis of the raw gene expression data of the 46 individuals, showing the outlying samples highlighted in red circles. 2 Figure S2: Correlation network generated for the low calorie diet (LCD). 3 Figure S3: Correlation network generated for the very low calorie diet (VLCD). 3 Figure S4: Frobenius norm plot showing the norm distance of the correlation network of the 44 individuals with the respective diet-wide correlation network. 4 Figure S5: Gene expression patterns of the respective genes in each topological cluster of the overlap network, based on the fold change in the respective diets. Also shows the five un-clustered paired correlated genes. 5 Source Code 5 Tables 7 Table S1: Results from the robustness analysis using the minimum %age individuals having the same correlation direction (for |corr.| ≥ 0.6).
    [Show full text]
  • And Intercellular Signaling Knowledge for Multicellular Omics Analysis
    Article Integrated intra- and intercellular signaling knowledge for multicellular omics analysis Denes Turei€ 1 , Alberto Valdeolivas1 , Lejla Gul2, Nicolas Palacio-Escat1,3,4 , Michal Klein5 , Olga Ivanova1 ,Marton Ölbei2,6 , Attila Gabor 1 , Fabian Theis5,7 , DezsoM} odos 2,6 , Tamas Korcsmaros 2,6 & Julio Saez-Rodriguez1,3,* Abstract pathways that are often represented as a network. This network determines the response of the cell under different physiological and Molecular knowledge of biological processes is a cornerstone in omics disease conditions. In multicellular organisms, the behavior of each data analysis. Applied to single-cell data, such analyses provide mech- cell is regulated by higher levels of organization: the tissue and, ulti- anistic insights into individual cells and their interactions. However, mately, the organism. In tissues, multiple cells communicate to knowledge of intercellular communication is scarce, scattered across coordinate their behavior to maintain homeostasis. For example, resources, and not linked to intracellular processes. To address this cells may produce and sense extracellular matrix (ECM), and release gap, we combined over 100 resources covering interactions and roles enzymes acting on the ECM as well as ligands. These ligands are of proteins in inter- and intracellular signaling, as well as transcrip- detected by receptors in the same or different cells, that in turn trig- tional and post-transcriptional regulation. We added protein complex ger intracellular pathways that control other processes, including information and annotations on function, localization, and role in the production of ligands and the physical binding to other cells. diseases for each protein. The resource is available for human, and The totality of these processes mediates the intercellular communi- via homology translation for mouse and rat.
    [Show full text]
  • Causes and Consequences of Mitochondrial Proteome Size-Variation in Animals
    bioRxiv preprint doi: https://doi.org/10.1101/829127; this version posted November 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Causes and consequences of mitochondrial proteome size-variation in animals Viraj Muthye1;2, Dennis Lavrov1;2 1 Bioinformatics and Computational Biology Program, Iowa State University, 2437 Pammel Drive, Ames, Iowa 50011, USA 2 Department of Ecology, Evolution and Organismal Biology, Iowa State University, 241 Bessey Hall, Ames, Iowa 50011, USA 1 bioRxiv preprint doi: https://doi.org/10.1101/829127; this version posted November 5, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Despite a conserved set of core mitochondrial functions, animal mitochondrial proteomes show a large variation in size. In this study, we analyzed the putative mechanisms behind and functional significance of this variation using experimentally-verified mt-proteomes of four bilaterian animals and two non-animal outgroups. We found that, of several factors affecting mitochondrial proteome size, evolution of novel mitochondrial proteins in mammals and loss of ancestral proteins in pro- tostomes were the main contributors. Interestingly, gain and loss of conventional mitochondrial targeting signals was not a significant factor in the proteome size evolution. Keywords: mitochondria, proteome, mitochondrial-targeting-signals, animal 2 bioRxiv preprint doi: https://doi.org/10.1101/829127; this version posted November 5, 2019.
    [Show full text]
  • Research Report 2018
    MPIMG RESEARCH REPORT 2018 Max Planck Institute for Molecular Genetics Impairment of cerebral organoid development induced by microRNA overexpression Shown are day 30 organoids following microRNA overexpression (left), and their matched day 30 untreated organoids (right), stained for the neural stem/progenitor cell marker SOX2 (red) and the neuronal differentiation marker DCX (green). The development of cerebral organoid cytoarchitecture can be clearly seen in control organoids, while it appears stalled following microRNA overexpression. The Elkabetz group is studying the mechanisms by which microRNAs govern cerebral development. Picture: Elkabetz Lab / Human Brain and Neural Stem Cell Studies MPIMG RESEARCH REPORT 2018 Max Planck Institute for Molecular Genetics Michael Müller, Governing Mayor of Berlin, at a visit at the MPIMG during the Long Night of Sciences 2017 (from left to right: Hans Lehrach, Martin Vingron, Michael Müller, Bernhard Herrmann, Alexander Meissner) 4 The Max Planck Institute for Molecular Genetics – Research Report 2018 Editorial We are very pleased to present the 2018 Research Report of the Max Planck Institute for Molecular Genetics (MPIMG), covering the time period from 2015 to mid-2018. The Insti- tute looks back on three challenging and productive years. The first important step in the transition and scientific reorientation following the retire- ment of H.-Hilger Ropers and Hans Lehrach in 2014 has been completed successfully by the appointment of Alexander Meissner to director of the new department “Genome Regulation”. This recruitment marks the beginning of a new important track of research at the MPIMG with a strong emphasis on gene regulatory mechanisms in stem cell differen- tiation, embryogenesis and human disease.
    [Show full text]
  • Research Report 2015 Max Planck Institute for Molecular Genetics, Berlin
    MPIMG Research Report 2015 Max Planck Institute for Molecular Genetics, Berlin Max Planck Institute Underground / U-Bahn Bus routes / Buslinien for Molecular Genetics .U3/Oskar-Helene-Heim .M11, 110 - Ihnestr. 63–73 .U3/Thielplatz Saarge münder Str./ 14195 Berlin Urban rail system / S-Bahn Bitscher Str. Germany .S1 / Sundgauer Str. .X10, 115, 285 - Clayallee/Leichhardtstr. or Schützallee .M48, 101 - Berliner Straße/Holländische Genetics (MPIMG) Molecular Max Planck Institute for Report 2015 Research Mühle 4c_U1-U4_maxplanck_Titel_2015.indd 1 13.10.2015 14:29:25 Imprint | Research Report 2015 Published by the Max Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany, Oktober 2015 Editorial Board: Bernhard G. Herrmann & Martin Vingron Coordination: Patricia Marquardt Photos & Scientifi c Illustrations: MPIMG Production: Thomas Didier, Meta Druck Copies: 500 Contact: Max Planck Institute for Molecular Genetics Ihnestr. 63 – 73 14195 Berlin Germany Phone: +49 (0)30 8413-0 Fax: +49 (0)30 8413-1207 Email: [email protected] For further information about the MPIMG, see http://www.molgen.mpg.de Cover image 3D-rendered light sheet micrograph of the caudal end of an E9.5 mouse embryo illustrating the generation of neuroectoderm (green cells) and mesoderm (red cells) from common progenitor cells (yellow cells, mostly located deep in the tissue) during trunk development. The neuroectoderm gives rise to the spinal cord, the mesoderm to the vertebral column, skeletal muscles and other tissues. Picture taken by Frederic Koch, Manuela Scholze, and Matthias Marks, MPIMG. 44c_U1-U4_maxplanck_Titel_2015.inddc_U1-U4_maxplanck_Titel_2015.indd 2 113.10.20153.10.2015 114:29:264:29:26 MPI for Molecular Genetics Research Report 2015 1 Research Report 2015 Max Planck Institute for Molecular Genetics Berlin, September 2015 The Max Plank Institute for Molecular Genetics 2 Ada Yonath, Nobel laureate in chemistry 2009 and former member of the MPIMG, at the celebratory symposium on the occasion of the institute’s 50th anniversary in December 2014.
    [Show full text]
  • Analyzing and Interpreting Genome Data at the Network Level With
    PROTOCOL Analyzing and interpreting genome data at the network level with ConsensusPathDB Ralf Herwig1, Christopher Hardt1, Matthias Lienhard1 & Atanas Kamburov2–4 1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. 2Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA. 3Harvard Medical School, Boston, Massachusetts, USA. 4Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. Correspondence should be addressed to R.H. ([email protected]) or A.K. ([email protected]). Published online 8 September 2016; doi:10.1038/nprot.2016.117 ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user’s input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high- throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.
    [Show full text]