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/.read more
Citations
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Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.
TL;DR: A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed and it is estimated that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%.
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Locating proteins in the cell using TargetP, SignalP and related tools
TL;DR: The properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts are described and a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties are sketched.
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Prediction of protein subcellular localization.
TL;DR: An approach based on a two‐level support vector machine (SVM) system, which performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity and when compared with other approaches, this approach performed significantly better.
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Sequence and analysis of chromosome 2 of the plant Arabidopsis thaliana
Xiaoying Lin,Samir Kaul,Steve Rounsley,Terrance Shea,Maria-Ines Benito,Christopher D. Town,Claire Fujii,Tanya Mason,Cheryl Bowman,Mary Barnstead,Tamara Feldblyum,C. Robin Buell,Karen A. Ketchum,John Lee,Catherine M. Ronning,Hean L. Koo,Kelly Moffat,Lisa A. Cronin,Mian Shen,Grace Pai,Susan Van Aken,Lowell Umayam,Luke J. Tallon,John Gill,Mark Raymond Adams,Ana J. Carrera,Todd Creasy,Howard M. Goodman,Chris Somerville,Gregory P. Copenhaver,Daphne Preuss,William C. Nierman,Owen White,Jonathan A. Eisen,Steven L. Salzberg,Claire M. Fraser,J. Craig Venter +36 more
TL;DR: The sequence of chromosome 2 from the Columbia ecotype is reported in two gap-free assemblies (contigs) of 3.6 and 16 megabases, which represents the longest published stretch of uninterrupted DNA sequence assembled from any organism to date.
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Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions
TL;DR: This method uses the support vector machines trained by multiple feature vectors based on n‐peptide compositions to predict subcellular localization for Gram‐negative bacteria, and achieves the highest prediction rate ever reported.
References
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