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GET /api/t/?description=%22peptide+cleavage%22&format=api
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                        "title": "SignalP 4.0: Discriminating signal peptides from transmembrane regions",
                        "abstract": "",
                        "date": "2011-10-01T00:00:00Z",
                        "citationCount": 6571,
                        "authors": [
                            {
                                "name": "Petersen T.N."
                            },
                            {
                                "name": "Brunak S."
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                            {
                                "name": "Von Heijne G."
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                            {
                                "name": "Nielsen H."
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                        "journal": "Nature Methods"
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            "name": "TatP",
            "description": "Prediction of the presence and location of Twin-arginine signal peptide cleavage sites in bacteria.",
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                            "term": "Protein cleavage site prediction"
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                    "note": "predicts the presence and location of Twin-arginine signal peptide cleavage sites in bacteria. Signal peptide/non-signal peptide prediction based on a combination of two artificial neural networks",
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                    "term": "Sequence sites, features and motifs"
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                {
                    "url": "http://www.cbs.dtu.dk/services/TatP/instructions.php",
                    "type": [
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                    "pmid": "15992409",
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                    "version": null,
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                    "metadata": {
                        "title": "Prediction of twin-arginine signal peptides",
                        "abstract": "Background: Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides. Results: We have retrieved sequence data for Tat substrates in order to train a computational method for discrimination of Sec and Tat signal peptides. The TatP method is able to positively classify 91% of 35 known Tat signal peptides and 84% of the annotated cleavage sites of these Tat signal peptides were correctly predicted. This method generates far less false positive predictions on various datasets than using simple pattern matching. Moreover, on the same datasets TatP generates less false positive predictions than a complementary rule based prediction method. Conclusion: The method developed here is able to discriminate Tat signal peptides from cytoplasmic proteins carrying a similar motif, as well as from Sec signal peptides, with high accuracy. The method allows filtering of input sequences based on Perl syntax regular expressions, whereas hydrophobicity discrimination of Tat- and Sec-signal peptides is carried out by an artificial neural network. A potential cleavage site of the predicted Tat signal peptide is also reported. The TatP prediction server is available as a public web server at http://www.cbs.dtu.dk/ services/TatP/. © 2005 Bendtsen et al; licensee BioMed Central Ltd.",
                        "date": "2005-07-02T00:00:00Z",
                        "citationCount": 377,
                        "authors": [
                            {
                                "name": "Bendtsen J.D."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Widdick D."
                            },
                            {
                                "name": "Palmer T."
                            },
                            {
                                "name": "Brunak S."
                            }
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                        "journal": "BMC Bioinformatics"
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                    "name": "Henrik Nielsen",
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        {
            "name": "VIGOR",
            "description": "Gene prediction program for small viral genomes in support of high throughput feature prediction and annotation. Identified genome-specific features include frame shifts, ribosomal slippage, RNA editing, stop codon read-through, overlapping genes, embedded genes, and mature peptide cleavage sites.",
            "homepage": "http://www.jcvi.org/vigor",
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                            "uri": "http://edamontology.org/operation_3663",
                            "term": "Homology-based gene prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2454",
                            "term": "Gene prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0436",
                            "term": "Coding region prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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            ],
            "toolType": [
                "Web application"
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                {
                    "uri": "http://edamontology.org/topic_0781",
                    "term": "Virology"
                },
                {
                    "uri": "http://edamontology.org/topic_3512",
                    "term": "Gene transcripts"
                },
                {
                    "uri": "http://edamontology.org/topic_0114",
                    "term": "Gene structure"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                }
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                    "pmid": "22669909",
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                    "note": null,
                    "metadata": {
                        "title": "VIGOR extended to annotate genomes for additional 12 different viruses",
                        "abstract": "A gene prediction program, VIGOR (Viral Genome ORF Reader), was developed at J. Craig Venter Institute in 2010 and has been successfully performing gene calling in coronavirus, influenza, rhinovirus and rotavirus for projects at the Genome Sequencing Center for Infectious Diseases. VIGOR uses sequence similarity search against custom protein databases to identify protein coding regions, start and stop codons and other gene features. Ribonucleicacid editing and other features are accurately identified based on sequence similarity and signature residues. VIGOR produces four output files: a gene prediction file, a complementary DNA file, an alignment file, and a gene feature table file. The gene feature table can be used to create GenBank submission. VIGOR takes a single input: viral genomic sequences in FASTA format. VIGOR has been extended to predict genes for 12 viruses: measles virus, mumps virus, rubella virus, respiratory syncytial virus, alphavirus and Venezuelan equine encephalitis virus, norovirus, metapneumovirus, yellow fever virus, Japanese encephalitis virus, parainfluenza virus and Sendai virus. VIGOR accurately detects the complex gene features like ribonucleicacid editing, stop codon leakage and ribosomal shunting. Precisely identifying the mat-peptide cleavage for some viruses is a built-in feature of VIGOR. The gene predictions for these viruses have been evaluated by testing from 27 to 240 genomes from GenBank. © 2012 The Author(s).",
                        "date": "2012-07-01T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Wang S."
                            },
                            {
                                "name": "Sundaram J.P."
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                            {
                                "name": "Stockwell T.B."
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