DOCSLIB.ORG

Brede Tools and Federating Online Neuroinformatics Databases∗

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Brede Tools and Federating Online Neuroinformatics Databases∗ Brede tools and federating online neuroinformatics databases∗ Finn Arup˚ Nielsen May 14, 2013 Abstract Several tools have been described for integrating or federating neuroscience databases (Gupta et al, As open science neuroinformatics databases the 2008; Ashish et al, 2010; Cheung et al, 2009). One of Brede Database and Brede Wiki seek to make dis- the major database federation efforts is Neuroscience tribution and federation of their content as easy and Information Framework (NIF) that uses the Neuro- transparent as possible. The databases rely on sim- science Information Framework standardized (NIF- ple formats and allow other online tools to reuse their STD) ontology (Bug et al, 2008). With this ontology content. This paper describes the possible intercon- NIF performs term expansion from a user query. The nections on different levels between the Brede tools expanded query is translated to queries for the differ- and other databases. ent source databases and through a data mediator the queries are sent to these external databases and aggregated. The system relies on tools, e.g., for reg- 1 Introduction istering the schema of the external database and for full text search. The complete system may search on The concept open science entails the free access to web pages, databases, Extensible Markup Language data and methods. In neuroinformatics open science (XML) and other documents. would allow federation of databases, so researchers can make queries across data sets and ontologies. One way of database federation is by the so- Open science represents the first step enabling called Semantic Web. First prominently described data sharing. As the next step we would like to by Berners-Lee et al(2001) its community has now query across the different databases, and in a fur- established a number of technologies around the con- ther step we would like to use the data across mul- cept, e.g., triple stores, Resource Description Frame- tiple databases in statistical computations so meta- work (RDF), Notation3 (N3) and the Web Ontology analytic consensus emerge. For neuroinformatics Language (OWL). Building and using actual Seman- these last two steps are hindered by the plethora of tic Web databases has gained momentum especially different data formats, brain atlases and terminology with the so-called Linked Data approach, and the and the division of data between several databases, Linking Open Data cloud (http://lod-cloud.net) or- | making even the discovery of relevant resources ganizes many open data sets. IBM Watson DeepQA difficult. We would like the databases to expose their has recently shown the powerful applicability of the data in both human-readable and machine-readable Semantic Web as a part in a system for general format. With a machine-readable format neuroinfor- question answering in the Jeopardy televised game maticians can work with data en masse and merge where the system operated on \structured and semi- the data across databases. However, even with open structured knowledge available from for example the science data in a machine-readable format one still Semantic Web" (IBM, 2012). Among more than 100 has to match and link heterogenous data in different techniques DeepQA relied on the Semantic Web re- formats. This is where integrative neuroinformatics sources DBpedia and Yago ontology (Ferrucci et al, tools come into play. For information retrieval these 2010). Widespread use of Semantic Web technologies tools should have an understanding of concepts rather have not yet taken a strong foothold in neuroinfor- than just keywords (Gupta et al, 2008). matics, but some projects have used it, e.g., Cognitive Paradigm Ontology (CogPO), based on the Brain- ∗The final publication is available at link.springer.com: Map taxonomy, expresses its components in the Se- http://dx.doi.org/10.1007/s12021-013-9183-4 mantic Web OWL format (Turner and Laird, 2011). 1 Other neuroinformatics efforts AlzPharm (Lam et al, is to provide an open science data source for visu- 2007), Cognitive Atlas (Poldrack et al, 2011) and the alization and meta-analysis in neuroimaging. The NIFSTD ontology with NeuroLex (Larson et al, 2009) Brede Wiki (http://neuro.compute.dtu.dk/wiki/) also apply Semantic Web technologies. (Nielsen, 2009a) has a broader scope than the One of the software packages that allows users to Brede Database, not only recording studies with collaboratively construct Semantic Web resources on- stereotaxic coordinates, but also brain morphometry line is the Semantic MediaWiki extension that ex- and personality genetics studies as well as studies tends MediaWiki-based wikis, so wiki links can be outside neuroscience. It contains descriptions of typed and semantic information queried (Kr¨otzsch 869 journal papers and 174 conference papers, 129 et al, 2006). Further extensions on top of Seman- pages with stereotaxic coordinates, 479 pages each tic MediaWiki allow for, e.g., form-based input and describing a brain region and 599 pages describing a import and export of data in comma-separated val- `topic'. In total the wiki has presently 3,291 content ues. Semantic MediaWiki has been embraced in neu- pages (This number can be compared with the 7,555 roinformatics with NeuroLex and ConnectomeWiki content pages in NeuroLex). Apart from providing (Gerhard et al, 2010). open science data, the purpose of the Brede Wiki The Semantic Web is no silver bullet for neuroin- is also to provide a more direct way to contribute formatics. Data may not align well with the frame- data and ontology information, to be able to handle work provided by the Semantic Web. For instance, many different forms of data (not just stereotaxic central to neuroimaging is the volume files with asso- coordinates) and to provide means for freeform ciated image processing and statistical analysis. Such textual annotation of scientific publications. The files can be linked and described by the Semantic Brede Wiki for Personality Genetics (Nielsen, 2010) Web, but it does not make specialized data analysis has data from 87 published personality genetics methods or image-based queries available. In some studies with information about personality scores, cases neuroscience data are conveniently described in genotype and subject group. Its purpose is to tabular form and indeed many current neuroinfor- perform a mass meta-analysis of personality genetics matics databases use relational databases where ma- and to provide a testing ground for an open science ture database engines provide complex query facili- collaborative online \real-time" mass meta-analysis ties. Sufficiently complex queries required for scien- with visualization. tific data may simply not be available in the Seman- K¨otter(2001) laid out the main points to consider tic Web query language SPARQL (Gray et al, 2009). in the construction of neuroscience databases. The Furthermore, for neuroimaging results we found that following paragraphs will use these main points to Semantic MediaWiki does not provide sufficient func- describe the Brede Database and Brede Wiki. tionality so the statistical analysis for meta-analysis can be implemented (Nielsen et al, 2012). Data acquisition: Results from published neu- The following sections first introduce the Brede roimaging experiments are manually entered in the Database and Wiki and then describe how the neu- Brede Database via a graphical user interface imple- roinformatics databases link up with other databases. mented in Matlab and available via the Brede Tool- box. Other users can download the toolbox and enter data for inclusion in the Brede Database. In Brede 2 Brede tools Wiki registered and \anonymous" wiki users can en- ter data and text directly in the standard wiki in- The Brede Database (http://neuro.compute.- terface. To aid data entry, web services can extract dtu.dk/services/brededatabase/)(Nielsen, 2003) bibliographic information from PubMed and extract contains data from 186 published neuroimaging part of the brain coordinate information from scien- articles that include stereotaxic coordinates. In tific Portable Document Format (PDF) documents a fashion inspired by the BrainMap database, the by converting the PDFs to text and matching brain Brede Database structures data from each article into coordinates with regular expressions. It is a method one or multiple experiments, that each may contain related to the extraction method in connection with one or more stereotaxic coordinates. There are 586 the NeuroSynth database (Yarkoni et al, 2011). To experiments, and the Brede Database is supported by handle the slight differences in stereotaxic coordinate simple ontologies for topics, brain regions, journals spaces the Brede Toolbox transforms brain coordi- and persons. The purpose of the Brede Database nates to the Talairach atlas during data entry, storing 2 both the original reported and the transformed coor- System implementation: The philosophy be- dinates in the Brede Database. For the Brede Wiki hind the Brede Database and Wiki of using sim- the original reported coordinates are entered along ple and standardized components makes it easy with a field indicating the stereotaxic space. A tem- to migrate and distribute. We use standard free plate in the Brede Wiki creates links to these two software for the online components of the Brede web services based on the PubMed identifier and a tools: Apache, MediaWiki, Perl and Python run- link to an accessible PDF. The Brede Wiki for per- ning on a Debian/Ubuntu computer. The Brede sonality genetics provides online form-based input
Load more

Recommended publications
  • Waxholm Space Atlas of the Sprague Dawley Rat Brain
    NeuroImage 97 (2014) 374–386 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Waxholm Space atlas of the Sprague Dawley rat brain Eszter A. Papp a,TrygveB.Leergaarda, Evan Calabrese b, G. Allan Johnson b, Jan G. Bjaalie a,⁎ a Department of Anatomy, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway b Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC, USA article info abstract Article history: Three-dimensional digital brain atlases represent an important new generation of neuroinformatics tools for Accepted 1 April 2014 understanding complex brain anatomy, assigning location to experimental data, and planning of experiments. Available online 12 April 2014 We have acquired a microscopic resolution isotropic MRI and DTI atlasing template for the Sprague Dawley rat brain with 39 μm isotropic voxels for the MRI volume and 78 μm isotropic voxels for the DTI. Building on this Keywords: template, we have delineated 76 major anatomical structures in the brain. Delineation criteria are provided for Digital brain atlas Waxholm Space each structure. We have applied a spatial reference system based on internal brain landmarks according to the Sprague Dawley Waxholm Space standard, previously developed for the mouse brain, and furthermore connected this spatial Rat brain template reference system to the widely used stereotaxic coordinate system by identifying cranial sutures and related Segmentation stereotaxic landmarks in the template using contrast given by the active staining technique applied to the tissue. Magnetic resonance imaging With the release of the present atlasing template and anatomical delineations, we provide a new tool for spatial Diffusion tensor imaging orientationanalysis of neuroanatomical location, and planning and guidance of experimental procedures in the Neuroinformatics rat brain.
    [Show full text]
  • The Neuron Phenotype Ontology: a FAIR Approach to Proposing and Classifying Neuronal Types
    bioRxiv preprint doi: https://doi.org/10.1101/2020.09.01.278879; this version posted September 2, 2020. 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. The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types Thomas H. Gillespie 1 (https://orcid.org/0000-0002-7509-4801) Shreejoy Tripathy2,3 (https://orcid.org/0000-0002-1007-9061) Mohameth François Sy (https://orcid.org/0000-0002-4603-9838) Maryann E. Martone 1, (https://orcid.org/0000-0002-8406-3871) Sean L. Hill2,3,4 (https://orcid.org/0000-0001-8055-860X) 1Department of Neuroscience, University of California, San Diego 2Department of Psychiatry, University of Toronto 3Krembil Center for Neuroinformatics, Centre for Addiction and Mental Health: Toronto 4Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland Corresponding Author: Sean Hill, Ph.D., [email protected] Blue Brain Project Campus Biotech, EPFL chemin des Mines, 9 Geneva, Switzerland CH-1202 Acknowledgements: This work was supported by NIH Brain Initiative award 5U24MH114827-04, a Canadian Institute for Health Research post-doctoral fellowship and National Institutes of Health grant MH111099, the Krembil Foundation, and by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology . Meetings supporting this work were facilitated by the International Neuroinformatics Coordinating Facility (INCF) through the Neuroinformatics for cell types special interest group.
    [Show full text]
  • Neuroinformatics.Pdf
    1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be self- archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Neuroinform DOI 10.1007/s12021-013-9210-5 ORIGINAL ARTICLE Action and Language Mechanisms in the Brain: Data, Models and Neuroinformatics Michael A. Arbib & James J. Bonaiuto & Ina Bornkessel-Schlesewsky & David Kemmerer & Brian MacWhinney & Finn Årup Nielsen & Erhan Oztop # Springer Science+Business Media New York 2013 Abstract We assess the challenges of studying action and Databasing empirical data . Federation of databases . language mechanisms in the brain, both singly and in relation Collaboratory workspaces to each other to provide a novel perspective on neuroinformatics, integrating the development of databases for encoding – separately or together – neurocomputational models and Bridging the Gap Between Models and Experiments empirical data that serve systems and cognitive neuroscience. The present article offers a perspective on the neuroinformatics Keywords Linking models and experiments . Models, challenges of linking neuroscience data with models of the neurocomputational .
    [Show full text]
  • Foundational Model of Structural Connectivity in the Nervous System
    Foundational model of structural connectivity in the INAUGURAL ARTICLE nervous system with a schema for wiring diagrams, connectome, and basic plan architecture Larry W. Swanson1 and Mihail Bota Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089 This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2010. Contributed by Larry W. Swanson, October 8, 2010 (sent for review September 13, 2010) The nervous system is a biological computer integrating the body’s ular, cellular, systems, and behavioral organization levels. A reflex and voluntary environmental interactions (behavior) with Human Connectome Project goal might be framed as providing a relatively constant internal state (homeostasis)—promoting sur- the detailed structural data needed to create a foundational vival of the individual and species. The wiring diagram of the nervous system structural model analogous to the DNA double- nervous system’s structural connectivity provides an obligatory helix structural model. foundational model for understanding functional localization at Neuroinformatics offers powerful new tools to store, share, molecular, cellular, systems, and behavioral organization levels. mine, analyze, and model data about neural connectivity in- This paper provides a high-level, downwardly extendible, concep- cluding the human brain—by far the most complex system tual framework—like a compass and map—for describing and known. Databases and inference engines for automatic reasoning exploring in neuroinformatics systems (such as our Brain Architec- in neuroinformatics workbenches require an integrated concep- ture Knowledge Management System) the structural architecture tual framework: (i)adefined universe of discourse (concept of the nervous system’s basic wiring diagram.
    [Show full text]
  • Neuroanatomical Domain of the Foundational Model of Anatomy
    Nichols et al. Journal of Biomedical Semantics 2014, 5:1 http://www.jbiomedsem.com/content/5/1/1 JOURNAL OF BIOMEDICAL SEMANTICS DATABASE Open Access Neuroanatomical domain of the foundational model of anatomy ontology B Nolan Nichols1*, Jose LV Mejino1, Landon T Detwiler1, Trond T Nilsen1, Maryann E Martone2, Jessica A Turner3, Daniel L Rubin4 and James F Brinkley1 Abstract Background: The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging. Description: The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications. Conclusions: A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain.
    [Show full text]
  • A Community-Based Transcriptomics Classification and Nomenclature Of
    COMMENT | FOCUS comment | FOCUS A community-based transcriptomics classifcation and nomenclature of neocortical cell types To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unifed taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the frst time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defned by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classifcation should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic defnition of a cell type and incorporate data from diferent approaches, developmental stages and species. A community-based classifcation and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classifcation, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body. Rafael Yuste, Michael Hawrylycz, Nadia Aalling, Argel Aguilar-Valles, Detlev Arendt, Ruben Armananzas Arnedillo, Giorgio A. Ascoli, Concha Bielza, Vahid Bokharaie, Tobias Borgtoft Bergmann, Irina Bystron, Marco Capogna, Yoonjeung Chang, Ann Clemens, Christiaan P. J. de Kock, Javier DeFelipe, Sandra Esmeralda Dos Santos, Keagan Dunville, Dirk Feldmeyer, Richárd Fiáth, Gordon James Fishell, Angelica Foggetti, Xuefan Gao, Parviz Ghaderi, Natalia A.
    [Show full text]
  • Text-Mining Tools for Optimizing Community Database Curation Workflows in Neuroscience
    Text-mining Tools for Optimizing Community Database Curation Workflows in Neuroscience Kyle H. Ambert Department of Biomedical Informatics Oregon Health & Science University A thesis submitted for the degree of Doctor of Philosophy April 29th, 2013 ! Contents Contents ii List of Figuresv List of Tables xi Nomenclature xii 1 Introduction9 1.1 The Importance of Terminologies & Data Integration to Neuroscience 11 1.1.1 NeuroNames: A Neuroanatomical Nomenclature . 12 1.1.2 Leveraging Neuroscience Ontologies & Vocabularies in New Resources . 12 1.2 Information Retrieval in Neuroscience . 15 1.2.0.1 Textpresso for Neuroscience: A Combination In- formation Retrieval & Extraction System . 17 1.2.0.2 Information Retrieval Using the Neuroscience In- formation Framework . 19 1.3 Supervised Text Classification in the Neurosciences . 23 1.3.0.3 Classification for the CoCoMac Database { An Example of Text-mining for the Neurosciences . 25 1.3.0.4 Efficient Approaches to Classification: Knowledge Mining . 33 1.4 A Case Study in Neuroinformatics Knowledge Base Maintenance: The Neuron Registry . 35 1.4.1 Databases & Research Science in the Information Age . 36 1.4.2 The Importance of Databases to Neuroscience Research . 37 1.4.3 The Neuron Registry: A Community-Curated Knowledge Base for Neuroscience . 38 1.4.3.1 Do we need another knowledge base? . 39 ii CONTENTS 1.4.3.2 A Clinically-relevant Use Case for the Neuron Reg- istry . 40 1.4.3.3 The Neuron Registry as an Aid to Developing Neuroinformatics . 42 1.5 Key Contributions of this Dissertation . 46 1.6 Thesis Overview . 47 2 Virk: An Active Learning System for Bootstrapping New Cu- rated Neuroinformatics Knowledge Bases 50 2.1 Introduction .
    [Show full text]
  • Neurolex: a Semantic Wiki for Neuroscience
    Stephen D. Larson Implementing 'Big Open Data' in government through Open Collaboration - case examples and possibilities Joint Data.Gov – Ontolog “Big Open Data” Session 05/17/12 A multi-scale data problem Whole brain data (20 um microscopic MRI) Mosiac LM images (1 GB+) Conventional LM images Individual cell morphologies Scale EM volumes & reconstructions Solved molecular structures 2 Framework of parts 3 Multi-scale synthesis in biology Watson & Crick, 1953 4 Organizing frameworks for knowledge Knowledge in words, terminologies and logical relationships (the “what”) 5 Past 6 Shared building blocks Calbindin Calbindin Has Cerebellum Has part Purkinje neuron part Cerebellum Purkinje cell Cerebellum axon Has Purkinje cell part dendrite Is part of Cerebellum Is part of Purkinje cell Cerebellum granule soma cell layer Cerebellum molecular layer Is part of Has part Has part Cerebellum Purkinje cell layer Cerebellar cortex Has part 7 Larson et al., 2007 8 Larson et al., 2007 9 Present 10 Organizing knowledge online 11 Organizing knowledge online 12 Top 25 accessed databases in NIF 1.Grants.gov / Opportunity 14.OMIM / Genes 2.SumsDB / Activation Foci 15.DrugBank / Drugs 3.CCDB / All Information 16.ModelDB / Models 4.Antibody Registry / Abs 17.NIF Integrated Animals / Available 5.GENSAT / GENSAT 18.Gemma / Microarray 6.Research Crossroads / Grants 19.NIF Integrated Brain 7.BrainInfo /Brain Region Gene/Expression 8.Drug Related Gene Database 20.EntrezGene / NCBIGene 9.NIF Integrated Nervous 21.NIF Integrated Software / Info System/Connectivity
    [Show full text]
  • Locus Coeruleus 1 Locus Coeruleus
    Locus coeruleus 1 Locus coeruleus Brain: Locus coeruleus Rhomboid fossa. (Locus coeruleus not labeled, but region is very near colliculus facialis, which is labeled at center left.) Latin locus caeruleus [1] Gray's subject #187 778 [2] NeuroNames hier-578 [3] NeuroLex ID birnlex_905 The Locus coeruleus, also spelled locus caeruleus, is a nucleus in the brain stem involved with physiological responses to stress and panic. It was discovered in the 1700s by Félix Vicq-d'Azyr. Its name is derived from the Latin words "coeruleus" and "locus". Literally, this means "the blue spot", a name derived from its azure appearance in unstained brain tissue. The color is due to light scattering from melanin in noradrenergic nerve cell bodies. Caeruleus is the classical Latin spelling, but coeruleus, a more archaic form, is the more common spelling. The spelling ceruleus, formed by contraction of the digraph ae or oe into e, is an American English form. Anatomy The locus coeruleus (or "LC") is in the dorsal wall of the rostral pons in the lateral floor of the fourth ventricle. This nucleus is the principal site for brain synthesis of noradrenaline (or "NA", also known as norepinephrine or "NE"). It is composed of mostly medium-size neurons. Melanin granules inside the neurons of the LC contribute to its blue color. Thus, it is also known as the nucleus pigmentosus pontis, meaning "heavily pigmented nucleus of the pons." The neuromelanin is formed by the polymerization of noradrenaline and is analogous to the black dopamine-based neuromelanin in the substantia nigra. In adult humans (19-78) the locus coeruleus has 22,000 to 51,000 total pigmented neurons that range in size between 31,000 to 60, 000 μm3.[4] Locus coeruleus 2 Connections The projections of this nucleus reach far and wide.
    [Show full text]
  • Brain Cell Type Specific Gene Expression and Co-Expression
    www.nature.com/scientificreports OPEN Brain Cell Type Specifc Gene Expression and Co-expression Network Architectures Received: 21 November 2017 Andrew T. McKenzie1,2,3, Minghui Wang 1,2, Mads E. Hauberg1,2,4,5,6, John F. Fullard1,2,7, Accepted: 31 May 2018 Alexey Kozlenkov7,8, Alexandra Keenan1,2,3, Yasmin L. Hurd4,7,9, Stella Dracheva4,7, Patrizia Published: xx xx xxxx Casaccia1,2,4,9,10, Panos Roussos1,2,4,7,8 & Bin Zhang1,2 Elucidating brain cell type specifc gene expression patterns is critical towards a better understanding of how cell-cell communications may infuence brain functions and dysfunctions. We set out to compare and contrast fve human and murine cell type-specifc transcriptome-wide RNA expression data sets that were generated within the past several years. We defned three measures of brain cell type-relative expression including specifcity, enrichment, and absolute expression and identifed corresponding consensus brain cell “signatures,” which were well conserved across data sets. We validated that the relative expression of top cell type markers are associated with proxies for cell type proportions in bulk RNA expression data from postmortem human brain samples. We further validated novel marker genes using an orthogonal ATAC-seq dataset. We performed multiscale coexpression network analysis of the single cell data sets and identifed robust cell-specifc gene modules. To facilitate the use of the cell type-specifc genes for cell type proportion estimation and deconvolution from bulk brain gene expression data, we developed an R package, BRETIGEA. In summary, we identifed a set of novel brain cell consensus signatures and robust networks from the integration of multiple datasets and therefore transcend limitations related to technical issues characteristic of each individual study.
    [Show full text]
  • Neuro Informatics 2013
    Neuro Informatics 2013 August 27 - 29 Stockholm, Sweden Artwork by Nina Johansson | ninajohansson.se ABSTRACT BOOK Neuroinformatics 2013 6th INCF Congress Program & abstracts August 27 - 29, 2012 Stockholm, Sweden Neuroinformatics 2013 3 What is INCF? The International Neuroinformatics Coordinating Facility (INCF), together with its 17 member countries, coordinates collaborative informatics infrastructure for neuroscience and manages scientific programs to develop standards for data sharing, analysis, modeling and simulation in order to catalyze insights into brain function in health and disease. INCF is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD to establish international coordination and collaborative informatics infrastructure for neuroscience. INCF is hosted by Karolinska Institutet and the Royal Institute of Technology, and the Secretariat is located on the Karolinska Institute Campus in Solna. INCF currently has 17 member countries across North America, Europe, Australia and Asia. Each member country establishes an INCF National Node to further the development of Neuroinformatics and to interface with the INCF Secretariat. The mission of INCF is to share and integrate neuroscience data and knowledge worldwide, with the aim to catalyze insights into brain function in health and disease. To fulfill this mission, INCF establishes and operates scientific programs to develop standards for neuroscience data sharing, analysis, modeling and simulation. Currently there are 4 program areas: Digital Brain Atlasing; Ontologies for Neural Structures, Multi-scale modeling, and Standards for Data Sharing. More than 180 leading international researchers are involved in the programs. A cloud-based data federation - the INCF Dataspace - has been developed to enable collaboration between researchers through the sharing of neuroscience data, text, images, sounds, movies, models and simulations.
    [Show full text]
  • Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders
    Interdisciplinary Bio Central Open Access IBC 2012;4:7, 1-8 • DOI: 10.4051/ibc.2012.4.3.0007 REVIEW Combining Neuroinformatics Databases for Multi-Level Analysis of Brain Disorders Ha Sun Yu1, Joon Bang2, Yousang Jo1 and Doheon Lee1,* 1Department of Bio and Brain Engineering, KAIST, Daejeon, Korea 2Winchester College, College Street, Winchester, UK Subject areas; Biological computation/ SYNOPSIS Database Author contribution; H.S.Y. writing and design With the development of many methods of studying the brain, the field of neuroscience of the manuscript; J.B. writing part of ‘brain has generated large amounts of information obtained from various techniques: imaging imaging databases’, ‘electrophysiological techniques, electrophysiological techniques, techniques for analyzing brain connectivity, databases’, and ‘databases of genetic information of brain’; Y.J. writing part of ‘brain techniques for getting molecular information of the brain, etc. A plenty of neuroinfor- connectivity database’ and ‘multi-level analysis matics databases have been made for storing and sharing this useful information and combining different types of databases’; D.L. those databases can be publicly accessed by researchers as needed. However, since there study design, management, and advice of the are too many neuroinformatics databases, it is difficult to find the appropriate database study and manuscript writing. depending on the needs of researcher. Moreover, many researchers in neuroscience fields *Correspondence and requests for materials are unfamiliar with using neuroinformatics databases for their studies because data is should be addressed to D.L. ([email protected]). too diverse for neuroscientists to handle this and there is little precedent for using neu- Editor; Keun Woo Lee, Gyeongsang National roinformatics databases for their research.
    [Show full text]