Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.

By Olof Emanuelsson, Henrik Nielsen, Søren Brunak, G. von Heijne. Journal of Molecular Biology, 2000

4,387 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bebaee82a5d98dd4b7cd2b41f669fec74c243f48

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Citations in the corpus

Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms (2010, methodology): “For instance, the cutoff threshold to reduce the homology bias for the benchmark dataset used in Predotar [111] was set at 80%, meaning that only those sequences which have 80%  pairwise sequence identity to any other in a same subset were excluded [111]; while for the benchmark dataset used in Tar”
Comparing function and structure between entire proteomes (2001, background): “The following results are based on predictions that may be wrong.”
Prediction of protein subcellular localization (2006, background): “The most used features are the short N-terminal amino acid sequences[6, 7, 14-16] (i.”
iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins (2011, background): “, [11,12,13,14,15,16,17,18,19,20,21,22,23,24] as well as a long list”
Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones (2004, neutral): “Green boxes show internal viewers for the results of clustering (Clustering Viewer showing results by H-Inv, STACK, TIGR, UniGene, etc.), the prediction of subcellular localization (TOPOViewer showing results of TMHMM, SOSUI, TargetP, and PsortII), and the disease-related information (DiseaseInfo Vi”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, positive): “, 2004) and TargetP (Emanuelsson et al., 2000) clearly assigned PPR8522 to the chloroplast, and the SignalP (Emanuelsson et al.”
IRT1, an Arabidopsis Transporter Essential for Iron Uptake from the Soil and for Plant Growth Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.001388. (2002, background): “IRT1 Is a Plasma Membrane Transporter The PsortI (Nakai and Kanehisa, 1992) and TargetP (Emanuelsson et al., 2000) programs predicted a plasma membrane localization of the IRT1 protein.”
Predicting subcellular localization of proteins using machine-learned classifiers (2004, background): “Name Acc Coverage Technique PSORT-B .75 1443 GN bacterial combination LOCkey .87 1161 assorted homology .91 291 prokaryotic SubLoc .79 2427 eukaryotic AA composition .85 940 plantTargetP .90 2738 non-plant signal prediction .93 16284 animal .93 3420 plant .81 2104 fungal .92 3218 GN bacterial Proteo”
Locating proteins in the cell using TargetP, SignalP and related tools (2007, background): “A successor of ChloroP is TargetP, which provides prediction of cTPs, mTPs and secretory SP”
SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis (2012, background): “These bioinformatic programs have become increasingly important for annotating newly sequenced genes and for providing testable hypotheses regarding protein localization and function (5).”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, methodology): “The MultiLoc2 approach was compared with current stateof-the-art tools (BaCelLo [19], LOCtree [2], Protein Prowler [9], TargetP [5] and WoLF PSORT [22]) using independent datasets sharing very low sequence identity with the training datasets of all compared tools.”
Better prediction of sub‐cellular localization by combining evolutionary and structural information (2003, methodology): “Additional methods: PSORT II: knowledge-based expert system using amino acid composition and sequence motifs, 46, (20); TargetP: combined method predicting extra-cellular (SignalP), chloroplast (ChloroP) and mitochondrial proteins.(21) Significant differences: For our networks the standard deviation”