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## Table of contents
* [Using Kleborate with Microreact](#using-kleborate-with-microreact)
* [Preparing Kleborate's resistance gene database files](#preparing-kleborates-resistance-gene-database-files)
## Using Kleborate with Microreact
If you have a phylogenetic tree of your samples, you can view Kleborate's results on the tree using [Microreact](https://microreact.org/).
#### Requirements
* Kleborate output table (including resistance results)
* Phylogenetic tree (Newick or Nexus format)
Importantly, the sample names in the table and tree must match.
#### Usage
```
kleborate_to_microreact.py --kleborate_in Kleborate_results.txt --tree_in tree.nwk --csv_out microreact.csv --tree_out microreact.nwk
```
If that command completes successfully, you can go to [Microreact's upload page](https://microreact.org/upload) and drag the `microreact.csv` and `microreact.nwk` files onto the page. Then explore the data with the available colours, labels and blocks.
If you have source information or geographical information, you will need to add this to the microreact.csv file yourself, following the instructions at [https://microreact.org/instructions](https://microreact.org/instructions)
#### Example
This directory includes an example of microreact-formated tree + Kleborate results that we generated using the output of [Kleborate example command #2](https://github.com/katholt/Kleborate#test-commands) and a mash distance tree constructed from the same assemblies.
```
kleborate_to_microreact.py --kleborate_in results_res.txt --tree_in tree.nwk --csv_out microreact.csv --tree_out microreact.nwk
```
This is how it looks in microreact:
<p align="center"><img src="microreact_screenshot.png" alt="Microreact_screenshot"></p>
Note to achieve this view, you need to turn on 'Metadata blocks' in Microreact like this:
<p align="center"><img src="metadata_options.png" alt="Microreact_options" width="200"></p>
## Preparing Kleborate's resistance gene database files
These instructions describe how to generate Kleborate's resistance gene database files: `ARGannot_r3.fasta` and `ARGannot_clustered80_r3.csv`.
This is necessary for a couple reasons:
1) The [ARG-ANNOT](http://en.mediterranee-infection.com/article.php?laref=283%26titre=arg-annot) labels beta-lactamase genes all together in the 'Bla' category. However, this is too general for _Klebsiella_, so Kleborate uses multiple categories for beta-lactamases, e.g. 'Bla_ESBL' for extended-spectrum beta-lactamases and 'Bla_Carb' for carbapenemases.
2) Some resistance genes don't apply to _Klebsiella_. These are removed from the database to keep Kleborate's output a bit simpler.
#### Requirements
* Python 3 (with BioPython)
* [SRST2](https://github.com/katholt/srst2) (for the files in its data directory)
#### 1. Download beta-lactamase information
The first file you need is from the [Lahey Clinic beta-lactamase classifications](https://www.lahey.org/Studies/). While that site is no longer active, it still contains useful information that is not available elsewhere. Download the data table [here](ftp://ftp.ncbi.nlm.nih.gov/pathogen/betalactamases/Lahey.tab).
The second file you need is the GenBank from [BioProject PRJNA313047](https://www.ncbi.nlm.nih.gov/bioproject/?term=313047). Go to [this list of nucleotide records in that project](https://www.ncbi.nlm.nih.gov/nuccore?term=313047%5BBioProject%5D), click 'Send to', choose 'File', set format to 'GenBank' and then click 'Create File' to start the download.
#### 2. Make the beta-lactamase information table
Run the `bla_info.py` script to create a table containing a description and class for each beta-lactamase allele. It takes as input the two files you just downloaded:
```
./bla_info.py Lahey.tab sequence.gb > bla_info_table
```
#### 3. Make the ARG-ANNOT csv file
Kleborate uses the `ARGannot_r3.fasta` and `ARGannot_clustered80_r3.csv` files. These are included in [SRST2](https://github.com/katholt/srst2), but Kleborate needs slightly modified versions.
Run the `make_argannot_csv.py` to add the beta-lactamase information to SRST2's csv:
```
./make_argannot_csv.py path/to/srst2/data/ARGannot_clustered80_r3.csv bla_info_table > ARGannot_clustered80_r3.csv
```
#### 4. Make the ARG-ANNOT fasta file
Now you need to make a fasta file corresponding to the csv file you just made. This removes alleles that aren't in the csv:
```
./make_argannot_fasta.py path/to/srst2/data/ARGannot_r3.fasta ARGannot_clustered80_r3.csv > ARGannot_r3.fasta
```
That's it! Put the two new files (`ARGannot_r3.fasta` and `ARGannot_clustered80_r3.csv`) in Kleborate's data directory (`Kleborate/kleborate/data/`).
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