Deducing intracellular distributions of metabolic pathways from genomic data

Gruber, Ansgar; Kroth, Peter G.

Deducing intracellular distributions of metabolic pathways from genomic data

Dateien

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Datum

2014

Publikationstyp

Beitrag zu einem Sammelband

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SRIRAM, Ganesh, ed.. Plant Metabolism. Totowa, NJ: Humana Press, 2014, pp. 187-211. 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.

Fachgebiet (DDC)

570 Biowissenschaften, Biologie

Schlagwörter

Metabolic pathways, subcellular localization, presequences, algae, diatoms

Zitieren

ISO 690

GRUBER, 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. 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.}
}

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    <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>
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