Deducing intracellular distributions of metabolic pathways from genomic data


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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, pp. 187-211. ISBN 978-1-62703-660-3

@incollection{Gruber2014Deduc-25820, title={Deducing intracellular distributions of metabolic pathways from genomic data}, year={2014}, doi={10.1007/978-1-62703-661-0_12}, number={1083}, isbn={978-1-62703-660-3}, address={Totowa, NJ}, publisher={Humana Press}, series={Methods in Molecular Biology}, booktitle={Plant Metabolism}, pages={187--211}, editor={Sriram, Ganesh}, author={Gruber, Ansgar and Kroth, Peter G.} }

Deducing intracellular distributions of metabolic pathways from genomic data eng 2014 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 deposit-license Kroth, Peter G. Gruber, Ansgar 2014-01-13T11:05:16Z Kroth, Peter G. 2014-01-13T11:05:16Z 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.<br />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. Gruber, Ansgar

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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