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ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites.

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TLDR
An analysis of 715 Arabidopsis thaliana sequences from SWISS‐PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome‐wide sequence data.
Abstract
We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +/-2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at http://www.cbs.dtu.dk/services/ChloroP/.

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Journal ArticleDOI

An improved prediction of chloroplast proteins reveals diversities and commonalities in the chloroplast proteomes of Arabidopsis and rice.

TL;DR: A subset of around 900 chloroplast proteins, predominantly derived from the cyanobacterial endosymbiont and with functions mostly related to metabolism, energy and transcription, is shared by the two species, and points to the existence of both conserved nucleus-encoded chlorOPlast proteins that are predominantly of prokaryotic origin, and a large fraction of taxon-specific chloropleft-targeted proteins, in flowering plants.
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The Arabidopsis gene YS1 encoding a DYW protein is required for editing of rpoB transcripts and the rapid development of chloroplasts during early growth.

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Characterizing the Anaerobic Response of Chlamydomonas reinhardtii by Quantitative Proteomics

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A novel isoform of glucan, water dikinase phosphorylates pre-phosphorylated alpha-glucans and is involved in starch degradation in Arabidopsis.

TL;DR: Data indicate that the C-3 linked phospho-ester in starch plays a so far unnoticed specific role in the degradation of transitory starch.
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Molecular mechanisms for photosynthetic carbon partitioning into storage neutral lipids in Nannochloropsis oceanica under nitrogen-depletion conditions

TL;DR: Transcriptional expression analysis revealed that the beta-1,3-glucan degradation and pyruvate dehydrogenases pathways were the main regulatory components involved in driving carbon flow to TAG synthesis, and temporal changes of lipidomes and transcripts of glycerolipid metabolism genes were indicative of possible conversion of membrane lipids to TAG, especially under an early stage of nitrogen deprivation conditions.
References
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A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Journal ArticleDOI

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

TL;DR: A new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence that performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets.

SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

TL;DR: In this paper, a new method for the identification of in performance compared with the weight matrix method signal peptides and their cleavage sites based on neural (Arrigo et al., 1991; Ladunga et al, 1991; Schneider and networks trained on separate sets of prokaryotic and eukaryotic sequence.
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