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annotate READme.md @ 4:b0de3731dca5 draft

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"planemo upload commit 987f052820b4b382b1ba65f32893fc0b6c8bccf9"
author galaxyp
date Wed, 21 Oct 2020 16:26:01 +0000
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1 MT2MQ
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2 ==========================================
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4 Description
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5 -----------
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7 In order to enable multi-omics data analysis of microbiome data, the Galaxy-P team has developed a tool – MT2MQ – which processes metatranscriptomics gene families output from ASaiM workflow and converts it to Gene Ontology (GO) or EC terms. The processed metatranscriptomics output can be subsequently used as an input for comparative statistical analysis via [metaQuantome](https://www.mcponline.org/content/18/8_suppl_1/S82) software suite.
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9 Authors
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10 -------
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12 Authors and contributors:
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14 * Marie Crane
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15 * Praveen Kumar
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16 * Subina Mehta
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17 * Dihn Duy An Nguyen
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18 * Pratik Jagtap
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21 # Instructions to run MT2MQ:
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22 --------------------------
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23
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24 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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25 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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27 ## Data upload
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29 - Upload the files mentioned below to the Galaxy Europe instance.
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30 ```
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31 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4A.tsv
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32 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4B.tsv
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33 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4C.tsv
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34 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7A.tsv
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35 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7B.tsv
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36 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7C.tsv
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37 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4T7_func.tsv
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38
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39 ```
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40
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41 ## Functional mode:
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42
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43 1. Build a **Dataset list** for the six .tsv files( `T4A`,`T4B`,`T4C`,`T7A`,`T7B`,`T7C`).
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44 - Click the **Operations on multiple datasets** check box at the top of the history panel.
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45 - Select the files mentioned above.
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46 - Click on ** For all selected** drop down menu and select **Build Dataset list**.
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47 - Once the collection is created, rename the dataset collection as `Input collection`.
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49 2. Download the map_go_uniref50.txt file from zenodo.
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50
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51 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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52 - [**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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53
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54 - *"Gene/pathway table"*: `Input collection`
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55 - *"How to combine grouped features?"*: `Sum`
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56 - In *"Use built-in grouping options?"*: `No`
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57 - *"Custom groups file"*: `map_go_uniref50.txt`
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58 - *"Is the groups file reversed?"*: `No`
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59 - *"Decimal places to round to after applying function"*: `3`
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60 - *"Include an 'UNGROUPED' group to capture features that did not belong to other groups?"*: `Yes`
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61 - *"Carry through protected features, such as 'UNMAPPED'?"*: `Yes`
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62
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63 Once this tool is run, rename the dataset collection as `Regrouped collection` .
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64
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65 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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66 - [**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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67
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68 - *"Gene/pathway table"*: `Regrouped collection`
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69 - *"Type of renaming"*: `Standard renaming`
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70 - *"Table features that can be renamed?"*: `Gene Ontology (GO)`
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71 - *"Remove non-alphanumeric characters from names?"*: `No`
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72
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73 Once this tool is run, rename the dataset collection as `Renamed collection`.
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75
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76 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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77 - [**Join HUMAnN2 generated tables**](https://toolshed.g2.bx.psu.edu/repository?repository_id=9b27f096128b26ff) with the following parameters:
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78
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79 - *"Gene/pathway table"*: `Renamed collection`
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80
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81 Once this tool is run, rename the dataset collection as `Joined Data`.
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82
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83 6. Run the **Renormalize a HUMAnN2 generated table** (Galaxy Version 0.11.1.0) tool to normalize the data.
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84 - [**Renormalize a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=05a56fcdeac2a25c) with the following parameters:
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85
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86 - *"Gene/pathway table"*: `Joined Data`
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87 - *"Normalization scheme"*: `Copies per million`
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88 - *"Normalization level"*: `Normalization of all levels by community total`
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89 - *"Include the special features UNMAPPED, UNINTEGRATED, and UNGROUPED?"*: `Yes`
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90 - *"Update '-RPK' in sample names to appropriate suffix?"*: `No`
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91
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92 Once this tool is run, rename the dataset collection as `Renormalized data`.
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93
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94
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95 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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96 - [**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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97 following parameters:
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98 - *"Mode"*: `Function`
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99 - *"GO namespace"*: `Molecular Function` or `Biological Process` or ` Cellular Component`
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100 - *"File from HUMAnN2 after regrouping, renaming, joining, and renormalizing"*: `Renormalized data`
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101
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102 **Note** : The MT2MQ tools can be run will all three GO name space.
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103
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104 There are two tabular outputs from this tool.
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105
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106 - A f_int.tabular output which mimics the Intensity input file for metaQuantome.
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107 - A func.tabular output which mimics the Functional input file for metaQuantome.
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108
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109 The resulting output files can be used as input for metaQuatome's functional mode.
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110 To run metaQuantome Function mode. Follow the [GTN](https://github.com/subinamehta/training-material/tree/metaquantome-2-3/topics/proteomics/tutorials/metaquantome-function).