annotate tools/protein_analysis/rxlr_motifs.xml @ 26:20139cb4c844 draft

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1 <tool id="rxlr_motifs" name="RXLR Motifs" version="0.0.11">
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2 <description>Find RXLR Effectors of Plant Pathogenic Oomycetes</description>
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3 <requirements>
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4 <!-- Need SignalP for all the models -->
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5 <requirement type="binary">signalp</requirement>
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6 <requirement type="package">signalp</requirement>
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7 <!-- Need HMMER for Whisson et al. (2007) -->
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8 <requirement type="binary">hmmsearch</requirement>
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9 <requirement type="package">hmmsearch</requirement>
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10 </requirements>
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11 <stdio>
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12 <!-- Anything other than zero is an error -->
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13 <exit_code range="1:" />
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14 <exit_code range=":-1" />
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15 </stdio>
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16 <version_command interpreter="python">rxlr_motifs.py -v</version_command>
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17 <command interpreter="python">
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18 rxlr_motifs.py "$fasta_file" "\$GALAXY_SLOTS" $model "$tabular_file"
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19 </command>
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20 <inputs>
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21 <param name="fasta_file" type="data" format="fasta" label="FASTA file of protein sequences" />
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22 <param name="model" type="select" label="Which RXLR model?">
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23 <option value="Bhattacharjee2006">Bhattacharjee et al. (2006) RXLR</option>
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24 <option value="Win2007">Win et al. (2007) RXLR</option>
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25 <option value="Whisson2007" selected="True">Whisson et al. (2007) RXLR-EER with HMM</option>
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26 </param>
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27 </inputs>
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28 <outputs>
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29 <data name="tabular_file" format="tabular" label="$model.value_label" />
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30 </outputs>
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31 <tests>
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32 <test>
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33 <param name="fasta_file" value="rxlr_win_et_al_2007.fasta" ftype="fasta" />
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34 <param name="model" value="Win2007" />
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35 <output name="tabular_file" file="rxlr_win_et_al_2007.tabular" ftype="tabular" />
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36 </test>
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37 <test>
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38 <param name="fasta_file" value="empty.fasta" ftype="fasta"/>
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39 <param name="model" value="Bhattacharjee2006"/>
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40 <output name="tabular_file" file="empty_rxlr.Bhattacharjee2006.tabular" ftype="tabular"/>
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41 </test>
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42 <test>
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43 <param name="fasta_file" value="empty.fasta" ftype="fasta"/>
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44 <param name="model" value="Win2007"/>
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45 <output name="tabular_file" file="empty_rxlr.Win2007.tabular" ftype="tabular"/>
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46 </test>
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47 <test>
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48 <param name="fasta_file" value="empty.fasta" ftype="fasta"/>
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49 <param name="model" value="Whisson2007"/>
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50 <output name="tabular_file" file="empty_rxlr.Whisson2007.tabular" ftype="tabular"/>
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51 </test>
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52 </tests>
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53 <help>
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54
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55 **Background**
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56
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57 Many effector proteins from oomycete plant pathogens for manipulating the host
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58 have been found to contain a signal peptide followed by a conserved RXLR motif
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59 (Arg, any amino acid, Leu, Arg), and then sometimes EER (Glu, Glu, Arg). There
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60 are striking parallels with the malarial host-targeting signal (Plasmodium
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61 export element, or "Pexel" for short).
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63 -----
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64
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65 **What it does**
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66
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67 Takes a protein sequence FASTA file as input, and produces a simple tabular
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68 file as output with one line per protein, and two columns giving the sequence
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69 ID and the predicted class. This is typically just whether or not it had the
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70 selected RXLR motif (Y or N).
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72 -----
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73
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74 **Bhattacharjee et al. (2006) RXLR Model**
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75
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76 Looks for the oomycete motif RXLR as described in Bhattacharjee et al. (2006).
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77
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78 Matches must have a SignalP Hidden Markov Model (HMM) score of at least 0.9,
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79 a SignalP Neural Network (NN) predicted cleavage site giving a signal peptide
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80 length between 10 and 40 amino acids inclusive, and the RXLR pattern must be
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81 after but within 100 amino acids of the cleavage site.
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82 SignalP is run truncating the sequences to the first 70 amino acids, which was
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83 the default on the SignalP webservice used in Bhattacharjee et al. (2006).
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84
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85
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86 **Win et al. (2007) RXLR Model**
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87
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88 Looks for the protein motif RXLR as described in Win et al. (2007).
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89
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90 Matches must have a SignalP Hidden Markov Model (HMM) score of at least 0.9,
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91 a SignalP Neural Network (NN) predicted cleavage site giving a signal peptide
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92 length between 10 and 40 amino acids inclusive, and the RXLR pattern must be
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93 after the cleavage site and start between amino acids 30 and 60.
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94 SignalP is run truncating the sequences to the first 70 amino acids, to match
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95 the methodology of Torto et al. (2003) followed in Win et al. (2007).
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96
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97
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98 **Whisson et al. (2007) RXLR-EER with HMM**
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99
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100 Looks for the protein motif RXLR-EER using the heuristic regular expression
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101 methodolgy, which was an extension of the Bhattacharjee et al. (2006) model,
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102 and a HMM as described in Whisson et al. (2007).
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103
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104 All the requirements described above for Bhattacharjee et al. (2006) apply,
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105 but rather than just looking for RXLR with the regular expression R.LR the
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106 more complicated regular expression R.LR.{,40}[ED][ED][KR] is used. This means
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107 RXLR (Arg, any amino acid, Leu, Arg), then a stretch of up to forty amino
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108 acids before Glu/Asp, Glu/Asp, Lys/Arg. The EER part of the name is perhaps
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109 misleading as it also allows for DDR, EEK, and so on.
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110
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111 Unlike Bhattacharjee et al. (2006) which used the SignalP webservice which
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112 defaults to truncating the sequences at 70 amino acids, Whisson et al. (2007)
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113 used the SignalP 3.0 command line tool with its default of not truncating the
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114 sequences. This does alter some of the scores, and also takes a little longer.
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115
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116 Additionally HMMER 2.3.2 is run to look for a cross validated HMM for the
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117 RXLR-ERR domain based on known positive examples. There are no restrictions
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118 on where within the protein the HMM match must be found.
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119
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120 The output of this model has four classes:
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121 * Y = Yes, both the heuristic motif and HMM were found.
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122 * re = Only the heuristic SignalP with regular expression motif was found.
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123 * hmm = Only the HMM was found.
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124 * neither = Niether the heuristic motif nor HMM was found.
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125
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126 -----
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127
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128 **Note**
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129
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130 Both Bhattacharjee et al. (2006) and Win et al. (2007) used SignalP v2.0, which
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131 is no longer available. The current release is SignalP v3.0 (Mar 5, 2007), so
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132 this is used instead. SignalP is called with the Eukaryote model and the short
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133 output (one line per protein). Any sequence truncation (e.g. to 70 amino acids)
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134 is handled via the intemediate sequence files.
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135
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136 -----
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137
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138 **References**
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139
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140 If you use this Galaxy tool in work leading to a scientific publication please
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141 cite Cock et al. (2013) and the appropriate method paper(s):
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142
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143 Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013).
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144 Galaxy tools and workflows for sequence analysis with applications
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145 in molecular plant pathology. PeerJ 1:e167
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146 http://dx.doi.org/10.7717/peerj.167
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147
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148 Stephen C. Whisson, Petra C. Boevink, Lucy Moleleki, Anna O. Avrova, Juan G. Morales, Eleanor M. Gilroy, Miles R. Armstrong, Severine Grouffaud, Pieter van West, Sean Chapman, Ingo Hein, Ian K. Toth, Leighton Pritchard and Paul R. J. Birch (2007).
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149 A translocation signal for delivery of oomycete effector proteins into host plant cells.
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150 Nature 450:115-118.
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151 http://dx.doi.org/10.1038/nature06203
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152
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153 Joe Win, William Morgan, Jorunn Bos, Ksenia V. Krasileva, Liliana M. Cano, Angela Chaparro-Garcia, Randa Ammar, Brian J. Staskawicz and Sophien Kamoun (2007).
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154 Adaptive evolution has targeted the C-terminal domain of the RXLR effectors of plant pathogenic oomycetes.
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155 The Plant Cell 19:2349-2369.
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156 http://dx.doi.org/10.1105/tpc.107.051037
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157
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158 Souvik Bhattacharjee, N. Luisa Hiller, Konstantinos Liolios, Joe Win, Thirumala-Devi Kanneganti, Carolyn Young, Sophien Kamoun and Kasturi Haldar (2006).
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159 The malarial host-targeting signal is conserved in the Irish potato famine pathogen.
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160 PLoS Pathogens, 2(5):e50.
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161 http://dx.doi.org/10.1371/journal.ppat.0020050
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162
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163 Trudy A. Torto, Shuang Li, Allison Styer, Edgar Huitema, Antonino Testa, Neil A.R. Gow, Pieter van West and Sophien Kamoun (2003).
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164 EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen *phytophthora*.
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165 Genome Research, 13:1675-1685.
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166 http://dx.doi.org/10.1101/gr.910003
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167
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168 Sean R. Eddy (1998).
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169 Profile hidden Markov models.
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170 Bioinformatics, 14(9):755–763.
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171 http://dx.doi.org/10.1093/bioinformatics/14.9.755
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172
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173 Nielsen, Engelbrecht, Brunak and von Heijne (1997).
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174 Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
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175 Protein Engineering, 10:1-6.
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176 http://dx.doi.org/10.1093/protein/10.1.1
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177
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178 This wrapper is available to install into other Galaxy Instances via the Galaxy
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179 Tool Shed at http://toolshed.g2.bx.psu.edu/view/peterjc/tmhmm_and_signalp
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180 </help>
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181 <citations>
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182 <citation type="doi">10.7717/peerj.167</citation>
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183 <!-- TODO - select from these citations depending on method picked -->
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184 <citation type="doi">10.1038/nature06203</citation>
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185 <citation type="doi">10.1105/tpc.107.051037</citation>
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186 <citation type="doi">10.1371/journal.ppat.0020050</citation>
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187 <citation type="doi">10.1101/gr.910003</citation>
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188 <citation type="doi">10.1093/bioinformatics/14.9.755</citation>
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189 <citation type="doi">10.1093/protein/10.1.1</citation>
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190 </citations>
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191 </tool>