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31
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1 <tool id="DTmhmm" name="Transmembrane detection" version="1.0.0">
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2 <description/>
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3 <requirements>
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4 <container type="docker">jjkoehorst/sappdocker:TMHMM</container>
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5 </requirements>
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6 <command interpreter="docker">java -jar /tmhmm/target/tmhmm-0.0.1-SNAPSHOT-jar-with-dependencies.jar
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7 -input $input -output $output -format TURTLE
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8 </command>
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9 <inputs>
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10 <param format="ttl" label="genome ttl with orf prediction" name="input" type="data"/>
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11 </inputs>
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12 <outputs>
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13 <data format="ttl" label="TMHMM: ${input.name}" name="output"/>
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14 </outputs>
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15 <help>Be aware that this can only be used for academic users; other
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16 users are
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17 requested to contact CBS Software Package Manager at
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18 software@cbs.dtu.dk.
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19 We are investigating alternative prediction
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20 applications, please contact
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21 us if you are aware of such method.
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22 </help>
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23 <citations>
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24 <citation type="bibtex">@article{Krogh2001,
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25 abstract = {We describe and
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26 validate a new membrane protein topology
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27 prediction method, TMHMM,
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28 based on a hidden Markov model. We present
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29 a detailed analysis of
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30 TMHMM's performance, and show that it
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31 correctly predicts 97-98 \% of
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32 the transmembrane helices.
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33 Additionally, TMHMM can discriminate
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34 between soluble and membrane
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35 proteins with both specificity and
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36 sensitivity better than 99 \%,
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37 although the accuracy drops when signal
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38 peptides are present. This
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39 high degree of accuracy allowed us to
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40 predict reliably integral
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41 membrane proteins in a large collection of
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42 genomes. Based on these
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43 predictions, we estimate that 20-30 \% of all
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44 genes in most genomes
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45 encode membrane proteins, which is in agreement
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46 with previous
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47 estimates. We further discovered that proteins with
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48 N(in)-C(in)
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49 topologies are strongly preferred in all examined
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50 organisms, except
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51 Caenorhabditis elegans, where the large number of
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52 7TM receptors
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53 increases the counts for N(out)-C(in) topologies. We
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54 discuss the
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55 possible relevance of this finding for our understanding
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56 of membrane
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57 protein assembly mechanisms. A TMHMM prediction service is
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58 available
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59 at http://www.cbs.dtu.dk/services/TMHMM/.},
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60 author = {Krogh,
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61 A and Larsson, B and von Heijne, G and Sonnhammer, E L},
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62 doi =
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63 {10.1006/jmbi.2000.4315},
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64 issn = {0022-2836},
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65 journal = {Journal of
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66 molecular biology},
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67 keywords = {Animals,Bacterial Proteins,Bacterial
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68 Proteins:
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69 chemistry,Computational Biology,Computational Biology:
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70 methods,Databases as Topic,Fungal Proteins,Fungal Proteins:
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71 chemistry,Genome,Internet,Markov Chains,Membrane Proteins,Membrane
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72 Proteins: chemistry,Plant Proteins,Plant Proteins:
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73 chemistry,Porins,Porins: chemistry,Protein Sorting Signals,Protein
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74 Structure, Secondary,Reproducibility of Results,Research
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75 Design,Sensitivity and Specificity,Software,Solubility},
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76 month = jan,
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77 number = {3},
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78 pages = {567--80},
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79 pmid = {11152613},
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80 title = {{Predicting
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81 transmembrane protein topology with a hidden Markov
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82 model: application
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83 to complete genomes.}},
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84 url =
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85 {http://www.sciencedirect.com/science/article/pii/S0022283600943158},
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86 volume = {305},
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87 year = {2001}
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88 }
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89
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90 </citation>
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91 </citations>
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92 </tool> |
