DOCSLIB.ORG

Web-Link Name Reference DRSC Flockhart I, Booker M, Kiger A, Et Al.: Flyrnai: the Drosophila Rnai Screening Center Database

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Web-Link Name Reference DRSC Flockhart I, Booker M, Kiger A, Et Al.: Flyrnai: the Drosophila Rnai Screening Center Database web-link Name Reference http://flyrnai.org/cgi-bin/RNAi_screens.pl DRSC Flockhart I, Booker M, Kiger A, et al.: FlyRNAi: the Drosophila RNAi screening center database. Nucleic Acids Res. 34(Database issue):D489-94 (2006) http://flybase.bio.indiana.edu/ FlyBase Grumbling G, Strelets V.: FlyBase: anatomical data, images and queries. Nucleic Acids Res. 34(Database issue):D484-8 (2006) http://rnai.org/ RNAiDB Gunsalus KC, Yueh WC, MacMenamin P, et al.: RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res. 32(Database issue):D406-10 (2004) http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIMOMIM Hamosh A, Scott AF, Amberger JS, et al.: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33(Database issue):D514-7 (2005) http://www.phenomicdb.de/ PhenomicDB Kahraman A, Avramov A, Nashev LG, et al.: PhenomicDB: a multi-species genotype/phenotype database for comparative phenomics. Bioinformatics 21(3):418-20 (2005) http://www.worm.mpi-cbg.de/phenobank2/cgi-bin/MenuPage.pyPhenoBank http://www.informatics.jax.org/ MGI - Mouse Genome Informatics Eppig JT, Bult CJ, Kadin JA, et al.: The Mouse Genome Database (MGD): from genes to mice--a community resource for mouse biology. Nucleic Acids Res. 33(Database issue):D471-5 (2005) http://flight.licr.org/ FLIGHT Sims D, Bursteinas B, Gao Q, et al.: FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets. Nucleic Acids Res. 34(Database issue):D479-83 (2006) http://www.wormbase.org/ WormBase Schwarz EM, Antoshechkin I, Bastiani C, et al.: WormBase: better software, richer content. Nucleic Acids Res. 34(Database issue):D475-8 (2006) http://www.yeastgenome.org/ SGD (yeast) Christie KR, Weng S, Balakrishnan R, et al.: Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 32(Database issue):D311-4 (2004) http://www.pharmgkb.org/ PharmGkb Thorn CF, Klein TE, Altman RB.: PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Methods Mol Biol. 311:179-91 (2005) http://www-deletion.stanford.edu/YDPM/YDPM_index.htmlYDPM Prokisch H, Scharfe C, Camp DG 2nd, et al.: Integrative analysis of the mitochondrial proteome in yeast. PLoS Biol. 2(6):e160 (2004) http://www.ebi.ac.uk/arrayexpress/ ArrayExpress Brazma A, Parkinson H, Sarkans U, et al.: ArrayExpress--a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31(1):68-71 (2003) http://staffa.wi.mit.edu/chipdb/public/ ChipDB http://twod.med.harvard.edu/ExpressDB/ ExpressDB Aach J, Rindone W, Church GM.: Systematic management and analysis of yeast gene expression data. Genome Res. 10(4):431-45 (2000) http://symatlas.gnf.org/SymAtlas/ Gene Expression Atlas Su AI, Wiltshire T, Batalov S, et al.: A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 101(16):6062-7 (2004) http://www.ncbi.nlm.nih.gov/geo/ Gene Expression Omnibus Barrett T, Suzek TO, Troup DB, et al.: NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res. 33(Database issue):D562-6 (2005) http://www.germonline.org/ GermOnline Primig M, Wiederkehr C, Basavaraj R, et al.: GermOnline, a new cross-species community annotation database on germ-line development and gametogenesis. Nat Genet. 35(4):291-2 (2003) http://www.biotechnologycenter.org/hio/ Human Gene Expression Index Haverty PM, Weng Z, Best NL, et al.: HugeIndex: a database with visualization tools for high-density oligonucleotide array data from normal human tissues. Nucleic Acids Res. 30(1):214-7 (2002) http://www.dkfz-heidelberg.de/mchips/ M-CHiPS Fellenberg K, Hauser NC, Brors B, et al.: Microarray data warehouse allowing for inclusion of experiment annotations in statistical analysis. Bioinformatics 18(3):423-33 (2002) http://affymetrix.arabidopsis.info/narrays/experimentbrowse.plNASCArrays Craigon DJ, James N, Okyere J, et al.: NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. Nucleic Acids Res. 32(Database issue):D575-7 (2004) http://www.oncomine.org/main/index.jsp Oncomine Rhodes DR, Yu J, Shanker K, et al.: ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6(1):1-6 (2004) http://read.gsc.riken.go.jp/ Riken Expression Array Database Bono H, Kasukawa T, Hayashizaki Y, et al.: READ: RIKEN Expression Array Database. Nucleic Acids Res. 30(1):211-3 (2002) http://red.dna.affrc.go.jp/RED/ Rice Expression Database Yazaki J, Kishimoto N, Ishikawa M, et al.: Rice Expression Database: the gateway to rice functional genomics. Trends in Plant Science 7 (12):563-564 (2002) http://psi081.ba.ars.usda.gov/SGMD/default.htmSoybean Genomics and Microarray DatabaseAlkharouf NW, Matthews BF.: SGMD: the Soybean Genomics and Microarray Database. Nucleic Acids Res. 32(Database issue):D398-400 (2004) http://genome-www5.stanford.edu/ Stanford Microarray Database Sherlock G, Hernandez-Boussard T, Kasarskis A, et al.: The Stanford Microarray Database. Nucleic Acids Res. 29(1):152-5 (2001) http://info.med.yale.edu/microarray/ Yale Microarray Database Cheung KH, White K, Hager J, et al.: YMD: a microarray database for large-scale gene expression analysis. Proc AMIA Symp. 140-144 (2002) http://www.transcriptome.ens.fr/ymgv/ Yeast Microarray Global Viewer Lelandais G, Le Crom S, Devaux F, et al.: yMGV: a cross-species expression data mining tool. Nucleic Acids Res. 32(Database issue):D323-5 (2004) http://cibex.nig.ac.jp/index.jsp CIBEX Ikeo K, Ishi-i J, Tamura T, et al.: CIBEX: center for information biology gene expression database. C R Biol. 326(10-11):1079-82 (2003) http://proteomics.cancer.dk/cgi-bin/CelisWeb.exe?MsetList.htmDCTB 2D-PAGE http://prospector.ucsf.edu/ ProteinProspector Clauser KR, Baker P, Burlingame AL.: Role of accurate mass measurement (+/- 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal Chem. 71(14):2871-82 (1999) http://www.mpiib-berlin.mpg.de/2D-PAGE/ 2D-PAGE / DIFF Pleissner KP, Schmelzer P, Wehrl W, et al.: Presentation of differentially regulated proteins within a web-accessible proteome database system of microorganisms. Proteomics. 4(10):2987-90 (2004) http://dbptm.mbc.nctu.edu.tw/ dbPTM Lee TY, Huang HD, Hung JH. et al.: dbPTM: an information repository of protein post-translational modification. Nucleic Acids Res. 34(Database issue):D622-7 (2006) http://gelbank.anl.gov/ GELBANK Babnigg G, Giometti CS.: GELBANK: a database of annotated two-dimensional gel electrophoresis patterns of biological systems with completed genomes. Nucleic Acids Res. 32(Database issue):D582-5 (2004) http://www.thegpm.org/TANDEM/ X! Tandem Craig R, Beavis RC.: TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20(9):1466-7 (2004) http://www.expasy.org/ ExPASy Proteomics Server Gasteiger E, Gattiker A, Hoogland C, et al.: ExPASy:The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31(13):3784-8 (2003) http://proteomics.jhu.edu/dl/pathidb.php JHU ProteinDB2 http://gene64.dna.affrc.go.jp/RPD/main_en.htmlRice Proteome Database Komatsu S, Kojima K, Suzuki K, et al.: Rice Proteome Database based on two-dimensional polyacrylamide gel electrophoresis: its status in 2003. Nucleic Acids Res. 32(Database issue):D388-92 (2004) http://www.matrixscience.com/ Mascot Hirosawa M, Hoshida M, Ishikawa M, et al.: MASCOT: multiple alignment system for protein sequences based on three-way dynamic programming. Comput Appl Biosci. 9(2):161-7 (1993) http://bioinformatics.icmb.utexas.edu/OPD/ OPD Prince JT, Carlson MW, Wang R, et al.: The need for a public proteomics repository. Nat Biotechnol. 22(4):471-2 (2004) http://www.wzw.tum.de/proteomik/lactis/ DynaProt 2D Drews O, Gorg A.: DynaProt 2D: an advanced proteomic database for dynamic online access to proteomes and two-dimensional electrophoresis gels. Nucleic Acids Res. 33(Database issue):D583-7 (2005) http://www.ebi.ac.uk/pride/ PRIDE Martens L, Hermjakob H, Jones P. et al.: PRIDE: the proteomics identifications database. Proteomics 5(13):3537-45 (2005) http://www.ebi.ac.uk/RESID/ RESID Garavelli JS.: The RESID Database of Protein Modifications as a resource and annotation tool. Proteomics 4(6):1527-33 (2004) http://www.expasy.org/ch2d/ SWISS-2DPAGE Hoogland C, Mostaguir K, Sanchez JC, et al.: SWISS-2DPAGE, ten years later. 4(8):2352-6 (2004) http://www.proteinatlas.org/object.php HPA Uhlen M, Bjorling E, Agaton C, et al.: A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteomics. 4(12):1920-32 (2005) http://nwsr.bms.umist.ac.uk/cgi-bin/pepseeker/pepseek.plPepSeeker McLaughlin T, Siepen JA, Selley J, et al.: PepSeeker: a database of proteome peptide identifications for investigating fragmentation patterns. 34(Database issue):D649-54. (2006) http://proteom.biomed.cas.cz/ SWICZ http://www.peptideatlas.org/ PeptideAtlas Desiere F, Deutsch EW, King NL, et al.: The PeptideAtlas project. Nucleic Acids Res. 34(Database issue):D655-8 (2006) http://www.metabolomics.ca/ Human Metabolome Project / Human Metabolite Database http://metlin.scripps.edu/ METLIN Metabolite Database Smith CA, O'Maille G, Want EJ, et al.: METLIN: a metabolite mass spectral database. Ther Drug Monit. 27(6):747-51 (2005) http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.htmlGolm Metabolome Database Kopka J, Schauer N, Krueger S, et. al.: [email protected]: the Golm Metabolome Database. Bioinformatics 21(8):1635-8 (2005) http://www.nugo.org/metabolomics/ The European Nutrigenomics Organisation http://www.brenda.uni-koeln.de/ BRENDA Schomburg I., Chang A., Ebeling C., et al.: BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32 Database issue:D431-3 (2004) www.empproject.com/ EMP Selkov E, Basmanova S, Gaasterland T, et al.: The metabolic pathway collection from EMP: the enzymes and metabolic pathways database.
Load more

Recommended publications
  • Pathways and Networks Biological Meaning of the Gene Sets
    Pathways and Networks Biological meaning of the gene sets Gene ontology terms ? Pathway mapping Linking to Pubmed abstracts or associted MESH terms Regulation by the same transcription factor (module) Protein families and domains Gene set enrichment analysis Over representation analysis 1 Gene set enrichment analysis 1. Given an a priori defined set of genes S. 2. Rank genes (e.g. by t‐value between 2 groups of microarray samples) ranked gene list L. 3. Calculation of an enrichment score (ES) that reflects the degree to which a set S is overrepresented at the extremes (top or bottom) of the entire ranked list L. 4. Estimation the statistical significance (nominal P value) of the ES by using an empirical phenotype‐based permutation test procedure. 5. Adjustment for multiple hypothesis testing by controlling the false discovery rate (FDR). Gene set enrichment analysis Subramanian A et al. Proc Natl Acad Sci (2005) 2 Biochemical and Metabolic Pathways Böhringer Mannheim Signaling networks (in cancer cell) Hannah, Weinberg, Cell. 2000 137 NCI curated pathway maps (http://pid.nci.nih.gov) Signal Transduction Knowledge Environment http://stke.sciencemag.org/cm/ 3 Pathways • Pathways are available mechanistic information •Kyoto Encylopedia of Genes and Genomes, KEGG (http://www.genome.jp/kegg/) BioCYc • EcoCYC Metabolic Pathway Database (E. Coli) •Also for other organisms (e.g. HumanCYC) 4 Pathways • Pathways from Biocarta • http://www.biocarta.com/genes/index.asp Transpath Part of larger BioBase package (commercial) • PathwayBuilder package
    [Show full text]
  • Genmapp 2: New Features and Resources for Pathway Analysis Nathan Salomonis Gladstone Institute of Cardiovascular Disease
    Washington University School of Medicine Digital Commons@Becker Open Access Publications 2007 GenMAPP 2: New features and resources for pathway analysis Nathan Salomonis Gladstone Institute of Cardiovascular Disease Kristina Hanspers Gladstone Institute of Cardiovascular Disease Alexander C. Zambon Gladstone Institute of Cardiovascular Disease Karen Vranizan Gladstone Institute of Cardiovascular Disease Steven C. Lawlor Gladstone Institute of Cardiovascular Disease See next page for additional authors Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Part of the Medicine and Health Sciences Commons Recommended Citation Salomonis, Nathan; Hanspers, Kristina; Zambon, Alexander C.; Vranizan, Karen; Lawlor, Steven C.; Dahlquist, Kam D.; Doniger, Scott .;W Stuart, Josh; Conklin, Bruce R.; and Pico, Alexander R., ,"GenMAPP 2: New features and resources for pathway analysis." BMC Bioinformatics.,. 217. (2007). https://digitalcommons.wustl.edu/open_access_pubs/208 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Nathan Salomonis, Kristina Hanspers, Alexander C. Zambon, Karen Vranizan, Steven C. Lawlor, Kam D. Dahlquist, Scott .W Doniger, Josh Stuart, Bruce R. Conklin, and Alexander R. Pico This open access publication is available at Digital Commons@Becker: https://digitalcommons.wustl.edu/open_access_pubs/208
    [Show full text]
  • Genmapp 2: New Features and Resources for Pathway Analysis Nathan Salomonis Gladstone Institute of Cardiovascular Disease
    Washington University School of Medicine Digital Commons@Becker Open Access Publications 2007 GenMAPP 2: New features and resources for pathway analysis Nathan Salomonis Gladstone Institute of Cardiovascular Disease Kristina Hanspers Gladstone Institute of Cardiovascular Disease Alexander C. Zambon Gladstone Institute of Cardiovascular Disease Karen Vranizan Gladstone Institute of Cardiovascular Disease Steven C. Lawlor Gladstone Institute of Cardiovascular Disease See next page for additional authors Follow this and additional works at: http://digitalcommons.wustl.edu/open_access_pubs Part of the Medicine and Health Sciences Commons Recommended Citation Salomonis, Nathan; Hanspers, Kristina; Zambon, Alexander C.; Vranizan, Karen; Lawlor, Steven C.; Dahlquist, Kam D.; Doniger, Scott .;W Stuart, Josh; Conklin, Bruce R.; and Pico, Alexander R., ,"GenMAPP 2: New features and resources for pathway analysis." BMC Bioinformatics.8,. 217. (2007). http://digitalcommons.wustl.edu/open_access_pubs/208 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. Authors Nathan Salomonis, Kristina Hanspers, Alexander C. Zambon, Karen Vranizan, Steven C. Lawlor, Kam D. Dahlquist, Scott .W Doniger, Josh Stuart, Bruce R. Conklin, and Alexander R. Pico This open access publication is available at Digital Commons@Becker: http://digitalcommons.wustl.edu/open_access_pubs/208
    [Show full text]
  • Transcriptomic Uniqueness and Commonality of the Ion Channels and Transporters in the Four Heart Chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A
    www.nature.com/scientificreports OPEN Transcriptomic uniqueness and commonality of the ion channels and transporters in the four heart chambers Sanda Iacobas1, Bogdan Amuzescu2 & Dumitru A. Iacobas3,4* Myocardium transcriptomes of left and right atria and ventricles from four adult male C57Bl/6j mice were profled with Agilent microarrays to identify the diferences responsible for the distinct functional roles of the four heart chambers. Female mice were not investigated owing to their transcriptome dependence on the estrous cycle phase. Out of the quantifed 16,886 unigenes, 15.76% on the left side and 16.5% on the right side exhibited diferential expression between the atrium and the ventricle, while 5.8% of genes were diferently expressed between the two atria and only 1.2% between the two ventricles. The study revealed also chamber diferences in gene expression control and coordination. We analyzed ion channels and transporters, and genes within the cardiac muscle contraction, oxidative phosphorylation, glycolysis/gluconeogenesis, calcium and adrenergic signaling pathways. Interestingly, while expression of Ank2 oscillates in phase with all 27 quantifed binding partners in the left ventricle, the percentage of in-phase oscillating partners of Ank2 is 15% and 37% in the left and right atria and 74% in the right ventricle. The analysis indicated high interventricular synchrony of the ion channels expressions and the substantially lower synchrony between the two atria and between the atrium and the ventricle from the same side. Starting with crocodilians, the heart pumps the blood through the pulmonary circulation and the systemic cir- culation by the coordinated rhythmic contractions of its upper lef and right atria (LA, RA) and lower lef and right ventricles (LV, RV).
    [Show full text]
  • A Bayesian Inference Transcription Factor Activity Model for the Analysis of Single-Cell Transcriptomes
    Downloaded from genome.cshlp.org on October 7, 2021 - Published by Cold Spring Harbor Laboratory Press Method A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes Shang Gao,1,2,3 Yang Dai,1 and Jalees Rehman1,2,3,4 1Department of Bioengineering, 2Department of Medicine, Division of Cardiology, 3Department of Pharmacology and Regenerative Medicine, University of Illinois at Chicago, Chicago, Illinois 60612, USA; 4University of Illinois Cancer Center, Chicago, Illinois 60612, USA Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference tran- scription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies bio- logically meaningful transcription factor activities,
    [Show full text]
  • Data Management in Systems Biology I
    Data management in systems biology I – Overview and bibliography Gerhard Mayer, University of Stuttgart, Institute of Biochemical Engineering (IBVT), Allmandring 31, D-70569 Stuttgart Abstract Large systems biology projects can encompass several workgroups often located in different countries. An overview about existing data standards in systems biology and the management, storage, exchange and integration of the generated data in large distributed research projects is given, the pros and cons of the different approaches are illustrated from a practical point of view, the existing software – open source as well as commercial - and the relevant literature is extensively overviewed, so that the reader should be enabled to decide which data management approach is the best suited for his special needs. An emphasis is laid on the use of workflow systems and of TAB-based formats. The data in this format can be viewed and edited easily using spreadsheet programs which are familiar to the working experimental biologists. The use of workflows for the standardized access to data in either own or publicly available databanks and the standardization of operation procedures is presented. The use of ontologies and semantic web technologies for data management will be discussed in a further paper. Keywords: MIBBI; data standards; data management; data integration; databases; TAB-based formats; workflows; Open Data INTRODUCTION the foundation of a new journal about biological The large amount of data produced by biological databases [24], the foundation of the ISB research projects grows at a fast rate. The 2009 (International Society for Biocuration) and special edition of the annual Nucleic Acids Research conferences like DILS (Data Integration in the Life database issue mentions 1170 databases [1]; alone Sciences) [25].
    [Show full text]
  • 1471-2105-8-217.Pdf
    BMC Bioinformatics BioMed Central Software Open Access GenMAPP 2: new features and resources for pathway analysis Nathan Salomonis1,2, Kristina Hanspers1, Alexander C Zambon1, Karen Vranizan1,3, Steven C Lawlor1, Kam D Dahlquist4, Scott W Doniger5, Josh Stuart6, Bruce R Conklin1,2,7,8 and Alexander R Pico*1 Address: 1Gladstone Institute of Cardiovascular Disease, 1650 Owens Street, San Francisco, CA 94158 USA, 2Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, 513 Parnassus Avenue, San Francisco, CA 94143, USA, 3Functional Genomics Laboratory, University of California, Berkeley, CA 94720 USA, 4Department of Biology, Loyola Marymount University, 1 LMU Drive, MS 8220, Los Angeles, CA 90045 USA, 5Computational Biology Graduate Program, Washington University School of Medicine, St. Louis, MO 63108 USA, 6Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064 USA, 7Department of Medicine, University of California, San Francisco, CA 94143 USA and 8Department of Molecular and Cellular Pharmacology, University of California, San Francisco, CA 94143 USA Email: Nathan Salomonis - [email protected]; Kristina Hanspers - [email protected]; Alexander C Zambon - [email protected]; Karen Vranizan - [email protected]; Steven C Lawlor - [email protected]; Kam D Dahlquist - [email protected]; Scott W Doniger - [email protected]; Josh Stuart - [email protected]; Bruce R Conklin - [email protected]; Alexander R Pico* - [email protected] * Corresponding author Published: 24 June 2007 Received: 16 November 2006 Accepted: 24 June 2007 BMC Bioinformatics 2007, 8:217 doi:10.1186/1471-2105-8-217 This article is available from: http://www.biomedcentral.com/1471-2105/8/217 © 2007 Salomonis et al; licensee BioMed Central Ltd.
    [Show full text]
  • Chromatin Remodelling Complex Dosage Modulates Transcription Factor Function in Heart Development
    ARTICLE Received 5 Aug 2010 | Accepted 11 Jan 2011 | Published 8 Feb 2011 DOI: 10.1038/ncomms1187 Chromatin remodelling complex dosage modulates transcription factor function in heart development Jun K. Takeuchi1,2,*, Xin Lou3,*, Jeffrey M. Alexander1,4, Hiroe Sugizaki2, Paul Delgado-Olguín1, Alisha K. Holloway1, Alessandro D. Mori1, John N. Wylie1, Chantilly Munson5,6, Yonghong Zhu3, Yu-Qing Zhou7, Ru-Fang Yeh8, R. Mark Henkelman7,9, Richard P. Harvey10,11, Daniel Metzger12, Pierre Chambon12, Didier Y. R. Stainier4,5,6, Katherine S. Pollard1,8, Ian C. Scott3,13 & Benoit G. Bruneau1,4,5,14 Dominant mutations in cardiac transcription factor genes cause human inherited congenital heart defects (CHDs); however, their molecular basis is not understood. Interactions between transcription factors and the Brg1/Brm-associated factor (BAF) chromatin remodelling complex suggest potential mechanisms; however, the role of BAF complexes in cardiogenesis is not known. In this study, we show that dosage of Brg1 is critical for mouse and zebrafish cardiogenesis. Disrupting the balance between Brg1 and disease-causing cardiac transcription factors, including Tbx5, Tbx20 and Nkx2–5, causes severe cardiac anomalies, revealing an essential allelic balance between Brg1 and these cardiac transcription factor genes. This suggests that the relative levels of transcription factors and BAF complexes are important for heart development, which is supported by reduced occupancy of Brg1 at cardiac gene promoters in Tbx5 haploinsufficient hearts. Our results reveal complex dosage-sensitive interdependence between transcription factors and BAF complexes, providing a potential mechanism underlying transcription factor haploinsufficiency, with implications for multigenic inheritance of CHDs. 1 Gladstone Institute of Cardiovascular Disease, San Francisco, California 94158, USA.
    [Show full text]
  • Standards and Tools for Model Exchange and Analysis in Systems Biology
    Standards and Tools for Model Exchange and Analysis in Systems Biology Ralph Gauges Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences presented by Diplom-Biochemiker Ralph Gauges born in: Sigmaringen, Germany Oral-examination: 07/11/2011 Standards and Tools for Model Exchange and Analysis in Systems Biology Referees: Prof. Dr. Ursula Kummer Dr. Rebecca Wade Contents Zusammenfassung vii Summary x Abbreviations xvii 1 Introduction 1 2 Materials & Methods 19 2.1 Operating Systems . 19 2.2 Programming Languages . 20 2.3 Unit Testing . 24 2.4 Debugging & Profiling Tools . 26 2.5 Libraries & Standards . 29 3 SBML Layout & Render Extension 46 3.1 SBML & Diagrams . 46 3.2 Alternative Diagram Formats . 47 3.3 Design & History . 49 3.4 The SBML Layout Extension Specification . 51 3.5 Implementation Of The Layout Extension . 57 3.6 The SBML Render Extension . 61 3.7 Third Party Implementations . 86 3.8 The SBML Layout And Render Extension In NF-κB Modeling 87 4 Standards In COPASI 93 4.1 SBML Support In COPASI . 93 4.2 Layout And Render Information In COPASI . 112 4.3 Graphical Display Of Time Course Simulation Data . 115 4.4 Graphical Display Of Elementary Modes . 116 4.5 COPASI Language Bindings . 117 4.6 NF-κB Modeling with COPASI . 120 i ii CONTENTS 4.7 Work Contributions . 128 5 Expression Normalization 130 5.1 Normal Form Classes . 131 5.2 Expression Tree Classes . 137 5.3 Normalization Algorithm .
    [Show full text]
  • Endocrine System Local Gene Expression
    Copyright 2008 By Nathan G. Salomonis ii Acknowledgments Publication Reprints The text in chapter 2 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC, Dahlquist KD, Doniger SW, Stuart J, Conklin BR, Pico AR. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics. 2007 Jun 24;8:218. The co-authors listed in this publication co-wrote the manuscript (AP and KH) and provided critical feedback (see detailed contributions at the end of chapter 2). The text in chapter 3 of this dissertation contains a reprint of materials as it appears in: Salomonis N, Cotte N, Zambon AC, Pollard KS, Vranizan K, Doniger SW, Dolganov G, Conklin BR. Identifying genetic networks underlying myometrial transition to labor. Genome Biol. 2005;6(2):R12. Epub 2005 Jan 28. The co-authors listed in this publication developed the hierarchical clustering method (KP), co-designed the study (NC, AZ, BC), provided statistical guidance (KV), co- contributed to GenMAPP 2.0 (SD) and performed quantitative mRNA analyses (GD). The text of this dissertation contains a reproduction of a figure from: Yeo G, Holste D, Kreiman G, Burge CB. Variation in alternative splicing across human tissues. Genome Biol. 2004;5(10):R74. Epub 2004 Sep 13. The reproduction was taken without permission (chapter 1), figure 1.3. iii Personal Acknowledgments The achievements of this doctoral degree are to a large degree possible due to the contribution, feedback and support of many individuals. To all of you that helped, I am extremely grateful for your support.
    [Show full text]
  • Systematic and Integrative Analysis of Proteomic Data Using Bioinformatics Tools
    (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 5, 2011 Systematic and Integrative Analysis of Proteomic Data using Bioinformatics Tools Rashmi Rameshwari Dr. T. V. Prasad Asst. Professor, Dept. of Biotechnology, Dean (R&D), Lingaya‟s University, Manav Rachna International University, Faridabad, India Faridabad, India Abstract— The analysis and interpretation of relationships or prediction. Expression and interaction experiments tend to between biological molecules is done with the help of networks. be on such a large scale that it is difficult to analyze them, or Networks are used ubiquitously throughout biology to represent indeed grasp the meaning of the results of any analysis. Visual the relationships between genes and gene products. Network representation of such large and scattered quantities of data models have facilitated a shift from the study of evolutionary allows trends that are difficult to pinpoint numerically to stand conservation between individual gene and gene products towards out and provide insight into specific avenues of molecular the study of conservation at the level of pathways and complexes. functions and interactions that may be worth exploring first out Recent work has revealed much about chemical reactions inside of the bunch, either through confirmation or rejection and then hundreds of organisms as well as universal characteristics of later of significance or insignificance to the research problem at metabolic networks, which shed light on the evolution of the hand. With a few recent exceptions, visualization tools were networks. However, characteristics of individual metabolites have been neglected in this network. The current paper provides not designed with the intent of being used for analysis so much an overview of bioinformatics software used in visualization of as to show the workings of a molecular system more clearly.
    [Show full text]
  • Mappfinder: Using Gene Ontology and Genmapp to Create a Global
    Method Open Access MAPPFinder: using Gene Ontology and GenMAPP to create a comment global gene-expression profile from microarray data Scott W Doniger*, Nathan Salomonis*, Kam D Dahlquist*†, Karen Vranizan*‡, Steven C Lawlor* and Bruce R Conklin*†§ Addresses: *Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, CA 94141-9100, USA. †Cardiovascular Research Institute, and §Departments of Medicine and Cellular and Molecular Pharmacology, University of California, San Francisco, ‡ CA 94143, USA. Functional Genomics Lab, University of California, Berkeley, CA 94720, USA. reviews Correspondence: Bruce R Conklin. E-mail: [email protected] Published: 6 January 2003 Received: 11 September 2002 Revised: 8 October 2002 Genome Biology 2003, 4:R7 Accepted: 8 November 2002 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2003/4/1/R7 reports © 2003 Doniger et al.; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract deposited research MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP (http://www.GenMAPP.org). The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene- expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.
    [Show full text]