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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/main/tools/lastz commit 04ec440aef00477a46c89fdb9b74e9bade5ffd94
author iuc
date Thu, 05 Feb 2026 12:51:47 +0000
parents da6f44d06921
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1 <tool id="lastz_d_wrapper" name="LASTZ_D" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
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2 <description>estimate substitution scores matrix</description>
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3 <macros>
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4 <import>lastz_macros.xml</import>
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5 </macros>
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6 <expand macro="bio_tools"/>
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7 <expand macro="requirements"/>
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8 <command detect_errors="exit_code"><![CDATA[
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9 lastz_D
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10 @TARGET_INPUT_COMMAND_LINE@
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11 @query_input@
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12 #if $score_file:
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13 '--inferonly=${score_file}'
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14 #else:
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15 --inferonly
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16 #end if
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17 '--infscores=${output}'
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18 ]]>
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19 </command>
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20 <inputs>
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21 <expand macro="target_input"/>
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22 <param name="query" format="fasta,fastq,fasta.gz,fastq.gz,fastq.bz2" type="data" label="Select QUERY sequence(s)" help="These are the sequences that you are aligning against TARGET"/>
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23 <param name="score_file" type="data" format="txt" optional="true" label="Control file for inference" argument="--inferonly[=control_file]" help="Optional controf file. If nothing is selected, LASTZ_D uses default described in the manual"/>
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24 </inputs>
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25 <outputs>
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26 <data format="txt" name="output" label="${tool.name} on ${on_string}: substitution matrix"/>
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27 </outputs>
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28 <tests>
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29 <test>
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30 <conditional name="source">
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31 <param name="ref_source" value="history" />
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32 <param name="target" value="chrM_human.fa" />
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33 </conditional>
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34 <param name="query" value="chrM_mouse.fa" />
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35 <output name="output" value="lastz_d_test1.out" />
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36 </test>
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37 <test>
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38 <conditional name="source">
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39 <param name="ref_source" value="history" />
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40 <param name="target" value="chrM_human.fa" />
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41 </conditional>
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42 <param name="query" value="chrM_mouse.fa" />
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43 <param name="score_file" value="lastz_d_ctrl_file.txt" />
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44 <output name="output" value="lastz_d_test2.out" />
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45 </test>
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46 </tests>
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48 <help><![CDATA[
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50 **What is does**
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51
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52 LASTZ_D is a non-integer (**D** stands for Double) version of LASTZ that can be used to estimate substitution matrix that
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53 will be used to score alignments. It was developed by `Bob Harris <http://www.bx.psu.edu/~rsharris/>`_ in the lab of
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54 Webb Miller at Penn State as a part of LASTZ. Matrix computed by this tool is to be used by LASTZ (see below).
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55
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56 .. class:: warningmark
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57
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58 **Read documentation** before proceeding. LASTZ is a complex tool with many parameter options. Fortunately, there is
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59 a `great manual <https://lastz.github.io/lastz/>`_ maintained by its author. The two sections that are particularly
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60 relevant to the inference of substitution matrix are
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61 `Inferring Score Sets <https://www.bx.psu.edu/~rsharris/lastz/README.lastz-1.04.03.html#adv_inference>`_ and
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62 `Inference Control File <https://www.bx.psu.edu/~rsharris/lastz/README.lastz-1.04.03.html#fmt_inference>`_.
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63
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64 **Notes on the inference**
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65
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66 Inference is achieved by computing the probability of each of the 18 different alignment events (gap open, gap extend, and 16 substitutions).
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67 These probabilities are estimated from alignments of the sequences. Of course, at first we don't have alignments, so the process
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68 begins by using a generic scoring set to create alignments, infer scores from those, then realign, and so on, until the scores stabilize
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69 or "converge". Ungapped alignments are performed until the substitution scores converge, then gapped alignments are performed
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70 (holding the substitution scores constant) until the gap penalties converge. In the end you get a matrix like this::
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71
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72 # (a LASTZ scoring set, created by "LASTZ --infer")
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73
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74 bad_score = X:-1781 # used for sub[X][*] and sub[*][X]
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75 fill_score = -178 # used when sub[*][*] not otherwise defined
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76 gap_open_penalty = 400
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77 gap_extend_penalty = 30
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78
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79 A C G T
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80 A 72 -79 -49 -97
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81 C -79 100 -178 -49
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82 G -49 -178 100 -79
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83 T -97 -49 -79 72
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84
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85 This dataset can then be used as an input to the **Read the substitution scores** parameter of LASTZ (Parameter section *Scoring*).
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86
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87 The iterative process can fail if there's not a lot of sequence to align. E.g. if after the 4th iteration there's nothing in the central 50% denominators go to zero and the process fails.
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88
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89 If the sequences you are aligning have GC content different than the usual ACGT 30-20-20-30 split, scoring inference should discover this and give you better alignments.
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90
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91
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92 ]]>
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93 </help>
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94 <expand macro="citations"/>
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95 </tool>