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| diff -r 9c8e7137d331 -r 08dda0f86758 READme.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/READme.md Wed Oct 21 16:22:53 2020 +0000 |
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| @@ -0,0 +1,112 @@ +MT2MQ +========================================== + +Description +----------- + +For multi-omics data analysis of microbiome data, the Galaxy-P team has developed a tool – MT2MQ – which takes in metatranscriptomics gene families +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 +comparative statistical analysis via metaQuantome. + +Authors +------- + +Authors and contributors: + +* Marie Crane +* Praveen Kumar +* Subina Mehta +* Dihn Duy An Nguyen +* Pratik Jagtap + + +# Instructions to run MT2MQ: +-------------------------- + +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). +However, for training purposes we have provided inputs in the [test data](https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mt2mq/test-data). + +## Data upload + +- Upload the files mentioned below to the Galaxy Europe instance. +``` +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4A.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4B.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4C.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7A.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7B.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T7C.tsv +https://github.com/galaxyproteomics/tools-galaxyp/blob/master/tools/mt2mq/test-data/T4T7_func.tsv + +``` + +## Functional mode: + +1. Build a **Dataset list** for the six .tsv files( `T4A`,`T4B`,`T4C`,`T7A`,`T7B`,`T7C`). + - Click the **Operations on multiple datasets** check box at the top of the history panel. + - Select the files mentioned above. + - Click on ** For all selected** drop down menu and select **Build Dataset list**. + - Once the collection is created, rename the dataset collection as `Input collection`. + +2. Download the map_go_uniref50.txt file from zenodo. + +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. + - [**Regroup a HUMAnN2 generated table by features**](https://toolshed.g2.bx.psu.edu/repository?repository_id=85391b8d5d7ad39d) with the following parameters: + + - *"Gene/pathway table"*: `Input collection` + - *"How to combine grouped features?"*: `Sum` + - In *"Use built-in grouping options?"*: `No` + - *"Custom groups file"*: `map_go_uniref50.txt` + - *"Is the groups file reversed?"*: `No` + - *"Decimal places to round to after applying function"*: `3` + - *"Include an 'UNGROUPED' group to capture features that did not belong to other groups?"*: `Yes` + - *"Carry through protected features, such as 'UNMAPPED'?"*: `Yes` + + Once this tool is run, rename the dataset collection as `Regrouped collection` . + +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 . + - [**Rename features of a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=c68108109505c2f5) with the following parameters: + + - *"Gene/pathway table"*: `Regrouped collection` + - *"Type of renaming"*: `Standard renaming` + - *"Table features that can be renamed?"*: `Gene Ontology (GO)` + - *"Remove non-alphanumeric characters from names?"*: `No` + + Once this tool is run, rename the dataset collection as `Renamed collection`. + + +5. Run the **Join HUMAnN2 generated tables** (Galaxy Version 0.11.1.1) tool to merge all the files into one. + - [**Join HUMAnN2 generated tables**](https://toolshed.g2.bx.psu.edu/repository?repository_id=9b27f096128b26ff) with the following parameters: + + - *"Gene/pathway table"*: `Renamed collection` + + Once this tool is run, rename the dataset collection as `Joined Data`. + +6. Run the **Renormalize a HUMAnN2 generated table** (Galaxy Version 0.11.1.0) tool to normalize the data. + - [**Renormalize a HUMAnN2 generated table**](https://toolshed.g2.bx.psu.edu/repository?repository_id=05a56fcdeac2a25c) with the following parameters: + + - *"Gene/pathway table"*: `Joined Data` + - *"Normalization scheme"*: `Copies per million` + - *"Normalization level"*: `Normalization of all levels by community total` + - *"Include the special features UNMAPPED, UNINTEGRATED, and UNGROUPED?"*: `Yes` + - *"Update '-RPK' in sample names to appropriate suffix?"*: `No` + + Once this tool is run, rename the dataset collection as `Renormalized data`. + + +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. +- [**MT2MQ Tool to prepare metatranscriptomic outputs from ASaiM for Metaquantome**](https://toolshed.g2.bx.psu.edu/repository?repository_id=cab5d81c5f0a2f94) with the + following parameters: + - *"Mode"*: `Function` + - *"GO namespace"*: `Molecular Function` or `Biological Process` or ` Cellular Component` + - *"File from HUMAnN2 after regrouping, renaming, joining, and renormalizing"*: `Renormalized data` + + **Note** : The MT2MQ tools can be run will all three GO name space. + + There are two tabular outputs from this tool. + + - A f_int.tabular output which mimics the Intensity input file for metaQuantome. + - A func.tabular output which mimics the Functional input file for metaQuantome. + +The resulting output files can be used as input for metaQuatome's functional mode. +To run metaQuantome Function mode. Follow the [GTN](https://github.com/subinamehta/training-material/tree/metaquantome-2-3/topics/proteomics/tutorials/metaquantome-function). |