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"planemo upload commit 98f9787ed5158179a5baf54cea3dbe70a02aaf59"
author galaxyp
date Wed, 21 Oct 2020 16:22:53 +0000
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1 MT2MQ
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2 ==========================================
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3
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4 Description
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5 -----------
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6
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7 For multi-omics data analysis of microbiome data, the Galaxy-P team has developed a tool – MT2MQ – which takes in metatranscriptomics gene families
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8 output from ASaiM workflow and converts it to GO/EC terms. This tool helps transform the metatranscriptomics output which can be then used as an input for
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9 comparative statistical analysis via metaQuantome.
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10
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11 Authors
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12 -------
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13
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14 Authors and contributors:
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15
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16 * Marie Crane
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17 * Praveen Kumar
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18 * Subina Mehta
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19 * Dihn Duy An Nguyen
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20 * Pratik Jagtap
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23 # Instructions to run MT2MQ:
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24 --------------------------
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25
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26 The ASAIM workflow can be run following the training module on the [GTN](https://training.galaxyproject.org/training-material/topics/metagenomics/tutorials/metatranscriptomics/tutorial.html).
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27 However, for training purposes we have provided inputs in the [test data](https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mt2mq/test-data).
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28
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29 ## Data upload
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31 - Upload the files mentioned below to the Galaxy Europe instance.
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32 ```
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33 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4A.tsv
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34 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4B.tsv
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35 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4C.tsv
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36 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7A.tsv
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37 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7B.tsv
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38 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7C.tsv
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39 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4T7_func.tsv
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40
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41 ```
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42
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43 ## Functional mode:
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44
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45 1. Build a **Dataset list** for the six .tsv files( `T4A`,`T4B`,`T4C`,`T7A`,`T7B`,`T7C`).
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46 - Click the **Operations on multiple datasets** check box at the top of the history panel.
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47 - Select the files mentioned above.
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48 - Click on ** For all selected** drop down menu and select **Build Dataset list**.
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49 - Once the collection is created, rename the dataset collection as `Input collection`.
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50
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51 2. Download the map_go_uniref50.txt file from zenodo.
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52
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53 3. Run the **Regroup a HUMAnN2 generated table by features**(Galaxy Version 0.11.1.0) tool is regrouping table features (abundances or coverage) given a table of feature values and a mapping of groups to component features. It produces a new table with group values in place of feature values.
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54 - [**Regroup a HUMAnN2 generated table by features**](https://toolshed.g2.bx.psu.edu/repository?repository_id=85391b8d5d7ad39d) with the following parameters:
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55
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56 - *"Gene/pathway table"*: `Input collection`
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57 - *"How to combine grouped features?"*: `Sum`
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58 - In *"Use built-in grouping options?"*: `No`
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59 - *"Custom groups file"*: `map_go_uniref50.txt`
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60 - *"Is the groups file reversed?"*: `No`
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61 - *"Decimal places to round to after applying function"*: `3`
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62 - *"Include an 'UNGROUPED' group to capture features that did not belong to other groups?"*: `Yes`
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63 - *"Carry through protected features, such as 'UNMAPPED'?"*: `Yes`
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64
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65 Once this tool is run, rename the dataset collection as `Regrouped collection` .
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66
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67 4. Run the **Rename features of a HUMAnN2 generated table** (Galaxy Version 0.11.1.0)tool to change the Uniref-50 values to GO term .
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68 - [**Rename features of a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=c68108109505c2f5) with the following parameters:
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69
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70 - *"Gene/pathway table"*: `Regrouped collection`
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71 - *"Type of renaming"*: `Standard renaming`
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72 - *"Table features that can be renamed?"*: `Gene Ontology (GO)`
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73 - *"Remove non-alphanumeric characters from names?"*: `No`
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74
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75 Once this tool is run, rename the dataset collection as `Renamed collection`.
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76
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77
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78 5. Run the **Join HUMAnN2 generated tables** (Galaxy Version 0.11.1.1) tool to merge all the files into one.
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79 - [**Join HUMAnN2 generated tables**](https://toolshed.g2.bx.psu.edu/repository?repository_id=9b27f096128b26ff) with the following parameters:
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80
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81 - *"Gene/pathway table"*: `Renamed collection`
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82
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83 Once this tool is run, rename the dataset collection as `Joined Data`.
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84
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85 6. Run the **Renormalize a HUMAnN2 generated table** (Galaxy Version 0.11.1.0) tool to normalize the data.
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86 - [**Renormalize a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=05a56fcdeac2a25c) with the following parameters:
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87
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88 - *"Gene/pathway table"*: `Joined Data`
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89 - *"Normalization scheme"*: `Copies per million`
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90 - *"Normalization level"*: `Normalization of all levels by community total`
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91 - *"Include the special features UNMAPPED, UNINTEGRATED, and UNGROUPED?"*: `Yes`
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92 - *"Update '-RPK' in sample names to appropriate suffix?"*: `No`
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93
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94 Once this tool is run, rename the dataset collection as `Renormalized data`.
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95
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96
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97 7. Now that the data is ready, we can run **MT2MQ Tool to prepare metatranscriptomic outputs from ASaiM for Metaquantome** (Galaxy Version 1.1.0)on this data.
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98 - [**MT2MQ Tool to prepare metatranscriptomic outputs from ASaiM for Metaquantome**](https://toolshed.g2.bx.psu.edu/repository?repository_id=cab5d81c5f0a2f94) with the
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99 following parameters:
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100 - *"Mode"*: `Function`
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101 - *"GO namespace"*: `Molecular Function` or `Biological Process` or ` Cellular Component`
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102 - *"File from HUMAnN2 after regrouping, renaming, joining, and renormalizing"*: `Renormalized data`
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103
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104 **Note** : The MT2MQ tools can be run will all three GO name space.
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105
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106 There are two tabular outputs from this tool.
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107
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108 - A f_int.tabular output which mimics the Intensity input file for metaQuantome.
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109 - A func.tabular output which mimics the Functional input file for metaQuantome.
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110
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111 The resulting output files can be used as input for metaQuatome's functional mode.
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112 To run metaQuantome Function mode. Follow the [GTN](https://github.com/subinamehta/training-material/tree/metaquantome-2-3/topics/proteomics/tutorials/metaquantome-function).