Deducing intracellular distributions of metabolic pathways from genomic data

Lade...
Vorschaubild
Dateien
Gruber_258202.pdf
Gruber_258202.pdfGröße: 8.62 MBDownloads: 767
Datum
2014
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Sammlungen
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Sammelband
Publikationsstatus
Published
Erschienen in
SRIRAM, Ganesh, ed.. Plant Metabolism. Totowa, NJ: Humana Press, 2014, pp. 187-211. Methods in Molecular Biology. 1083. ISBN 978-1-62703-660-3. Available under: doi: 10.1007/978-1-62703-661-0_12
Zusammenfassung

In the recent years, a large number of genomes from a variety of different organisms have been sequenced. Most of the sequence data has been publicly released and can be assessed by interested users. However, this wealth of information is currently underexploited by scientists not directly involved in genome annotation. This is partially because sequencing, assembly, and automated annotation can be done much faster than the identification, classification, and prediction of the intracellular localization of the gene products. This part of the annotation process still largely relies on manual curation and addition of contextual information. Users of genome databases who are unfamiliar with the types of data available from (whole) genomes might therefore find themselves either overwhelmed by the vast amount and multiple layers of data or dissatisfied with less-than-meaningful analyses of the data.
In this chapter we present procedures and approaches to identify and characterize gene models of enzymes involved in metabolic pathways based on their similarity to known sequences. Furthermore we describe how to predict the subcellular location of the proteins using publicly available prediction servers and how to interpret the obtained results. The strategies we describe are generally applicable to organisms with primary plastids such as land plants or green algae. Additionally, we describe strategies suitable for those groups of algae with secondary plastids (for instance diatoms), which are characterized by a different cellular topology and a larger number of intracellular compartments compared to plants.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
Metabolic pathways, subcellular localization, presequences, algae, diatoms
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690GRUBER, Ansgar, Peter G. KROTH, 2014. Deducing intracellular distributions of metabolic pathways from genomic data. In: SRIRAM, Ganesh, ed.. Plant Metabolism. Totowa, NJ: Humana Press, 2014, pp. 187-211. Methods in Molecular Biology. 1083. ISBN 978-1-62703-660-3. Available under: doi: 10.1007/978-1-62703-661-0_12
BibTex
@incollection{Gruber2014Deduc-25820,
  year={2014},
  doi={10.1007/978-1-62703-661-0_12},
  title={Deducing intracellular distributions of metabolic pathways from genomic data},
  number={1083},
  isbn={978-1-62703-660-3},
  publisher={Humana Press},
  address={Totowa, NJ},
  series={Methods in Molecular Biology},
  booktitle={Plant Metabolism},
  pages={187--211},
  editor={Sriram, Ganesh},
  author={Gruber, Ansgar and Kroth, Peter G.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/25820">
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/25820"/>
    <dc:contributor>Kroth, Peter G.</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/25820/2/Gruber_258202.pdf"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-13T11:05:16Z</dc:date>
    <dcterms:abstract xml:lang="eng">In the recent years, a large number of genomes from a variety of different organisms have been sequenced. Most of the sequence data has been publicly released and can be assessed by interested users. However, this wealth of information is currently underexploited by scientists not directly involved in genome annotation. This is partially because sequencing, assembly, and automated annotation can be done much faster than the identification, classification, and prediction of the intracellular localization of the gene products. This part of the annotation process still largely relies on manual curation and addition of contextual information. Users of genome databases who are unfamiliar with the types of data available from (whole) genomes might therefore find themselves either overwhelmed by the vast amount and multiple layers of data or dissatisfied with less-than-meaningful analyses of the data.&lt;br /&gt;In this chapter we present procedures and approaches to identify and characterize gene models of enzymes involved in metabolic pathways based on their similarity to known sequences. Furthermore we describe how to predict the subcellular location of the proteins using publicly available prediction servers and how to interpret the obtained results. The strategies we describe are generally applicable to organisms with primary plastids such as land plants or green algae. Additionally, we describe strategies suitable for those groups of algae with secondary plastids (for instance diatoms), which are characterized by a different cellular topology and a larger number of intracellular compartments compared to plants.</dcterms:abstract>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/25820/2/Gruber_258202.pdf"/>
    <dcterms:title>Deducing intracellular distributions of metabolic pathways from genomic data</dcterms:title>
    <dcterms:bibliographicCitation>Plant Metabolism : Methods and Protocols / Ganesh Sriram (ed.). - Totowa, NJ : Humana Press, 2014. - S. 187-211. - (Methods in Molecular Biology, Methods and Protocols ; 1083). - ISBN 978-162-703-660-3</dcterms:bibliographicCitation>
    <dcterms:issued>2014</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-13T11:05:16Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dc:creator>Kroth, Peter G.</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:creator>Gruber, Ansgar</dc:creator>
    <dc:contributor>Gruber, Ansgar</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen