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author galaxyp
date Wed, 21 Oct 2020 16:22:53 +0000
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1 MT2MQ
2 ==========================================
3
4 Description
5 -----------
6
7 For multi-omics data analysis of microbiome data, the Galaxy-P team has developed a tool – MT2MQ – which takes in metatranscriptomics gene families
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
9 comparative statistical analysis via metaQuantome.
10
11 Authors
12 -------
13
14 Authors and contributors:
15
16 * Marie Crane
17 * Praveen Kumar
18 * Subina Mehta
19 * Dihn Duy An Nguyen
20 * Pratik Jagtap
21
22
23 # Instructions to run MT2MQ:
24 --------------------------
25
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).
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).
28
29 ## Data upload
30
31 - Upload the files mentioned below to the Galaxy Europe instance.
32 ```
33 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4A.tsv
34 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4B.tsv
35 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4C.tsv
36 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7A.tsv
37 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7B.tsv
38 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7C.tsv
39 https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4T7_func.tsv
40
41 ```
42
43 ## Functional mode:
44
45 1. Build a **Dataset list** for the six .tsv files( `T4A`,`T4B`,`T4C`,`T7A`,`T7B`,`T7C`).
46 - Click the **Operations on multiple datasets** check box at the top of the history panel.
47 - Select the files mentioned above.
48 - Click on ** For all selected** drop down menu and select **Build Dataset list**.
49 - Once the collection is created, rename the dataset collection as `Input collection`.
50
51 2. Download the map_go_uniref50.txt file from zenodo.
52
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.
54 - [**Regroup a HUMAnN2 generated table by features**](https://toolshed.g2.bx.psu.edu/repository?repository_id=85391b8d5d7ad39d) with the following parameters:
55
56 - *"Gene/pathway table"*: `Input collection`
57 - *"How to combine grouped features?"*: `Sum`
58 - In *"Use built-in grouping options?"*: `No`
59 - *"Custom groups file"*: `map_go_uniref50.txt`
60 - *"Is the groups file reversed?"*: `No`
61 - *"Decimal places to round to after applying function"*: `3`
62 - *"Include an 'UNGROUPED' group to capture features that did not belong to other groups?"*: `Yes`
63 - *"Carry through protected features, such as 'UNMAPPED'?"*: `Yes`
64
65 Once this tool is run, rename the dataset collection as `Regrouped collection` .
66
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 .
68 - [**Rename features of a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=c68108109505c2f5) with the following parameters:
69
70 - *"Gene/pathway table"*: `Regrouped collection`
71 - *"Type of renaming"*: `Standard renaming`
72 - *"Table features that can be renamed?"*: `Gene Ontology (GO)`
73 - *"Remove non-alphanumeric characters from names?"*: `No`
74
75 Once this tool is run, rename the dataset collection as `Renamed collection`.
76
77
78 5. Run the **Join HUMAnN2 generated tables** (Galaxy Version 0.11.1.1) tool to merge all the files into one.
79 - [**Join HUMAnN2 generated tables**](https://toolshed.g2.bx.psu.edu/repository?repository_id=9b27f096128b26ff) with the following parameters:
80
81 - *"Gene/pathway table"*: `Renamed collection`
82
83 Once this tool is run, rename the dataset collection as `Joined Data`.
84
85 6. Run the **Renormalize a HUMAnN2 generated table** (Galaxy Version 0.11.1.0) tool to normalize the data.
86 - [**Renormalize a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=05a56fcdeac2a25c) with the following parameters:
87
88 - *"Gene/pathway table"*: `Joined Data`
89 - *"Normalization scheme"*: `Copies per million`
90 - *"Normalization level"*: `Normalization of all levels by community total`
91 - *"Include the special features UNMAPPED, UNINTEGRATED, and UNGROUPED?"*: `Yes`
92 - *"Update '-RPK' in sample names to appropriate suffix?"*: `No`
93
94 Once this tool is run, rename the dataset collection as `Renormalized data`.
95
96
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.
98 - [**MT2MQ Tool to prepare metatranscriptomic outputs from ASaiM for Metaquantome**](https://toolshed.g2.bx.psu.edu/repository?repository_id=cab5d81c5f0a2f94) with the
99 following parameters:
100 - *"Mode"*: `Function`
101 - *"GO namespace"*: `Molecular Function` or `Biological Process` or ` Cellular Component`
102 - *"File from HUMAnN2 after regrouping, renaming, joining, and renormalizing"*: `Renormalized data`
103
104 **Note** : The MT2MQ tools can be run will all three GO name space.
105
106 There are two tabular outputs from this tool.
107
108 - A f_int.tabular output which mimics the Intensity input file for metaQuantome.
109 - A func.tabular output which mimics the Functional input file for metaQuantome.
110
111 The resulting output files can be used as input for metaQuatome's functional mode.
112 To run metaQuantome Function mode. Follow the [GTN](https://github.com/subinamehta/training-material/tree/metaquantome-2-3/topics/proteomics/tutorials/metaquantome-function).