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https://github.com/tiagofilipe12/patlas

Plasmid Atlas - A web interface to browse for plasmids and their associated genes. Visit us at:
https://github.com/tiagofilipe12/patlas

database flask mash plasmids visualization vivagraph

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Plasmid Atlas - A web interface to browse for plasmids and their associated genes. Visit us at:

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README 

        


  

    pATLAS logo

  

  





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# Table of contents



* [Description](#description)

    * [Citation](#citation)

* [Documentation](#documentation)

* [Development](#development)

    * [Dependencies](#dependencies)

    * [Backend scripts](#backend-scripts)

        * [MASHix.py](#mashixpy)

        * [Database customization](#database-customization)

    * [Supplementary scripts](#supplementary-scripts)

        * [abricate2db.py](#abricate2dbpy)

        * [taxa_fetch.py](#taxa-fetchpy)

* [pATLAS API](#patlas-api)

    * [Schematics](#schematics-of-the-patlas-database-creation)

    * [Workflow for database creation](#workflow-for-database-creation)

    * [Run pATLAS locally](#run-patlas-locally)

        * [Docker compose](#docker-compose)

        * [From scratch](#from-scratch)

    * [Optimization of the resources usage by the web page](#optimization-of-the-resources-usage-by-the-web-page)



# Description



[Plasmid Atlas](http://www.patlas.site) is a web-base tool that empowers

researchers to easily and rapidly access

information related with plasmids present in `NCBI's refseq` database.

In pATLAS each node (or circle) represents

a plasmid and each link between two plasmids means that those two plasmids

share around 90% average nucleotide identity.



With this tool we have two main goals:



1. Increase the accessibility of plasmid relevant metadata to users as

well as facilitate the access to that metadata.

2. Improve the ease of interpreting results from High Throughput Sequencing

   (HTS) for plasmid detection.



## Citation



Tiago F Jesus, Bruno Ribeiro-Gonçalves, Diogo N Silva, Valeria Bortolaia, 

Mário Ramirez, João A Carriço; Plasmid ATLAS: plasmid visual analytics and 

identification in high-throughput sequencing data, Nucleic Acids Research,

gky1073, [https://doi.org/10.1093/nar/gky1073](https://doi.org/10.1093/nar/gky1073)



# Documentation



If are interested in learning how to use pATLAS, please refer to

[gitbook documentation](https://patlas.gitbook.io/docs).



---



# Development



## Dependencies



* **Mash** (2.0) - You can download mash version 2.0.0 directly here:

[linux](https://github.com/marbl/Mash/releases/download/v2.0.0/mash-Linux64-v1.1.1.tar.gz) and [OSX](https://github.com/marbl/Mash/releases/download/v1.1.1/mash-OSX64-v1.1.1.tar.gz). Other releases were not tested but may be downloaded in Mash git [releases page](https://github.com/marbl/Mash/releases).



* **Postgresql** (>= 10.0) - This script uses Postgres database to store the database:

[releases page](https://www.postgresql.org/download/)



* **Python 3** and the respective **pip**.



* To install all other dependencies just run: _pip install -r requirements.txt_



## Backend Scripts



### MASHix.py



MASHix.py is the main script to generate the database. This script generates

a matrix of pairwise comparisons between sequences in input fasta(s) file(s).

Note that it reads multifastas, i.e., each header in fasta is a reference sequence.



#### Options:



##### Main options:



```

'-i','--input_references' - 'Provide the input fasta files to parse.

                            This will inputs will be joined in a

                            master fasta.'



'-o','--output' - 'Provide an output tag'



'-t', '--threads' - 'Provide the number of threads to be used'



'-db', '--database_name' - 'This argument must be provided as the last

argument. It states the database name that must be used.'

```



##### MASH related options:

```

'-k','--kmers' - 'Provide the number of k-mers to be provided to mash

                sketch. Default: 21'



'-p','--pvalue' - 'Provide the p-value to consider a distance

                significant. Default: 0.05'



'-md','--mashdist' - 'Provide the maximum mash distance to be

                    parsed to the matrix. Default:0.1'

```



##### Other options:



```

'-no_rm', '--no-remove' - 'Specify if you do not want to remove the

                        output concatenated fasta.'



'-hist', '--histograms' - 'Checks the distribution of distances

                        values ploting histograms.'



'-non', '--nodes_ncbi' - 'specify the path to the file containing

                        nodes.dmp from NCBI'



'-nan', '--names_ncbi' - 'specify the path to the file containing

                        names.dmp from NCBI'



'--search-sequences-to-remove' - 'this option allows to only run the

                                 part of the script that is required

                                 to generate the filtered fasta.

                                 Allowing for instance to debug

                                 sequences that shoudn't be removed

                                 using 'cds' and 'origin' keywords'.

```



---



#### Database customization:



##### I don't like database name! How do I change it?



Go to `db_manager/config_default.py` and edit the following line:



```python

SQLALCHEMY_DATABASE_URI = 'postgresql:///'

```



##### I don't like table name inside database! How do I change it?



Go to db_manager/db_app/models.py and edit the following line:



```python

 __tablename__ = ""

```



---



#### Database migration from one server to another



##### Database export



```

pg_dump  > 

```



##### Database import



```

psql -U  -d  -f 

```



---



## Supplementary scripts



### abricate2db.py



This script inherits a class from

[ODiogoSilva/Templates](https://github.com/ODiogoSilva/templates) and uses it to

parse abricate outputs and dumps abricate outputs to a psql database, depending

on the input type provided.



#### Options:



```

"-i", "--input_file" - "Provide the abricate file to parse to db.

                        It can accept more than one file in the case of

                        resistances."



"-db_psql", "--database_name" - "his argument must be provided as the

                                last argument. It states the database

                                name that must be used."



"-db", "--db" - "Provide the db to output in psql models."



"-id", "--identity" - "minimum identity to be reported to db"



"-cov", "--coverage" - "minimum coverage do be reported to db"



"-csv", "--csv" - "Provide card csv file to get correspondence between

                    DNA accessions and ARO accessions. Usually named

                    aro_index.csv. By default this file is already

                    available in patlas repo with a specific path:

                    'db_manager/db_app/static/csv/aro_index.csv'"



```



### taxa_fetch.py



This script is located in `utils` folder and can be used to generate a

JSON file with the corresponding taxonomic tree. It fetches for a given

species, the genera, family and order to which it belongs.

Note: for plasmids I have to make some filtering in the resulting taxids

and list of species that other users may want to skip



#### Options:



```

-i INPUT_LIST, --input_list INPUT_LIST

                        provide a file with a listof species. Each

                        speciesshould be in each line.

-non NODES_FILE, --nodes_ncbi NODES_FILE

                        specify the path to the file containing

                        nodes.dmp from NCBI

-nan NAMES_FILE, --names_ncbi NAMES_FILE

                        specify the path to the file containing

                        names.dmp from NCBI

-w, --weirdos         This option allows the userto add a checks for

                        weirdentries. This is mainly usedto parse the

                        plasmids refseq, so if you do not want this to

                        be used, use this option.



```



##### List of entries that will be filtered from `weirdos` option



* From taxonomy levels:

    * "bug"

    * "insect"

    * "angiosperm"

    * "fungus"

    * "cnidarian"

    * "mudpuppy"

    * "mantid"

    * "mussel"



* From species in fasta headers:

    * "orf"

    * "unknown"

    * "Uncultured"

    * "uncultured"

    * "Peanut"

    * "Pigeon"

    * "Wheat"

    * "Beet"

    * "Blood"

    * "Onion"

    * "Tomato"

    * "Zea"

    * "Endosymbiont"

    * "Bacillaceae"

    * "Comamonadaceae"

    * "Enterobacteriaceae"

    * "Opitutaceae"

    * "Rhodobacteraceae"

    * "Bacterium"

    * "Endophytic"



* It also attempts to fix some bugs in species naming like the

following:

    * "B bronchiseptica"

    * "S pyogenes"



Note: Yes people like to give interesting names to bacteria...



# pATLAS API



## Schematics of the pATLAS database creation



![Workflow db creation](docs/gitbook/images/pATLAS_schematics.png)



## Workflow for database creation



1) Download plasmid sequences available in [NCBI refseq](ftp://ftp.ncbi.nlm.nih.gov/refseq/release/plasmid/)

2) Extract fasta from tar.gz

3) Download and extract [NCBI taxonomy](ftp://ftp.ncbi.nih.gov/pub/taxonomy/taxdump.tar.gz),

which will be fed to pATLAS.

4) Clone this repository:

```

git clone https://github.com/tiagofilipe12/pATLAS

```

5) Install its [dependencies](#dependencies)



6) Configure the database:

```

createdb 

pATLAS/patlas/db_manager/db_create.py 

```

7) run [MASHix.py](#mashixpy) - the output will include a filtered

fasta file (`master_fasta_*.fas`).

8) run [ABRicate](https://github.com/tseemann/abricate), with CARD,

ResFinder, PlasmidFinder, VFDB databases.

```

# e.g.

abricate --db card  > abr_card.tsv

abricate --db resfinder  > abr_resfinder.tsv

abricate --db vfdb  > abr_vfdb.tsv

abricate --db plasmidfinder  > abr_plasmidfinder.tsv

```

9) Download the [card index](https://card.mcmaster.ca/download/0/broadstreet-v2.0.2.tar.gz)

necessary for the abricate2db.py script (aro_index.csv).

10) run [abricate2db.py](#abricate2dbpy) - using all the previous tsv as

input.

```

# e.g.

abricate2db.py -i abr_plasmidfinder.tsv -db plasmidfinder \

    -id 80 -cov 90 -csv aro_index.csv -db_psql 

```

11) [dump database to a sql file](#database-export).



### Automation of this steps



This steps are fully automated in the nextflow pipeline

[pATLAS-db-creation](https://github.com/tiagofilipe12/pATLAS-db-creation).



### Creating a custom version of pATLAS



If you require to add your own plasmids to pATLAS database

without asking to add them to [pATLAS website](www.patlas.site),

you can provide custom fasta files when building the database using

the `-i` option of [MASHix.py](#mashixpy).

Then follow the steps [described above](#workflow-for-database-creation).



## Run pATLAS locally



### Docker compose



You can run pATLAS locally without much requirements by using

[patlas-compose](https://github.com/bfrgoncalves/patlas-compose). This

will automatically handle the installation of the version 1.5.2 of pATLAS

and launch the service in a local instance. For that you just require:



- [docker](https://docs.docker.com/install/#supported-platforms)

- [docker-compose](https://docs.docker.com/compose/install/)



Then, follow this simple steps:



- Clone the repository [patlas-compose](https://github.com/bfrgoncalves/patlas-compose).



```

git clone https://github.com/bfrgoncalves/patlas-compose

```



- Enter the *patlas-compose* folder



```

cd patlas-compose

```



- Launch the compose:



```

docker-compose up

```



- Wait for the line `* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)

` to show up, meaning that the service is now running.



- Access on `127.0.0.1:5000` or `0.0.0.0:5000`.



**Note: This methodology is highly recommended.**



### From scratch



pATLAS can be run locally if you have PostgreSQL installed and

configured. After, you just need to:



1) Clone this repository:

```

git clone https://github.com/tiagofilipe12/pATLAS

```



2) [Create your custom database version](database_creation.md#creating-a-custom-version-of-pATLAS)

 or [generate the default pATLAS database](database_creation.md) or

 [download sql file from version 1.5.2](https://github.com/tiagofilipe12/pATLAS/releases/tag/1.5.2)

 (the `tar.gz` archive).

 **Note:** if you download the sql file from version 1.5.2 you may skip

 steps 3 to 4 and **continue with step 5**.



3) Make sure all the necessary files are in place.



- by default pATLAS generates a `import_to_vivagraph.json` file in

  the folder `/results`. Place this file in the

  `patlas/db_manager/db_app/static/json` folder.

- change session to read the new `import_to_vivagraph.json` file by

  changing from `false` to `true` a variable named `devel` in

  `patlas/db_manager/db_app/static/js/pATLASGlobals.js`



4) Create the database that the front end will run:

```

createdb 

```



5) [load the generated sql file](https://github.com/tiagofilipe12/pATLAS#database-import)



6) Install backend dependencies:



```

# within the root directory of this repository

pip install -r requirements.txt

```



7) Install frontend dependencies:



```

# change directory to static direcoty where `index.html` will look for

# its depdendenies

cd patlas/db_manager/db_app/static/

# then install them (package.json is located in this directory)

yarn install

```



8) Compile node modules so that the html can understand, using webpack:



```

# You can also user a local installation of webpack.

# entry-point.js is the config file where all the imported modules are

# called

node_modules/webpack/bin/webpack.js entry-point.js

```



9) Then execute the script `run.py`.

```

# within the root directory of this repository

cd patlas/db_manager

./run.py 

```

Note: the database name is utterly important to properly say to the

frontend where to get the data.



10) Go to `127.0.0.1:5000`.



# Optimization of the resources usage by the web page



Using the `devel = true` isn't very efficient, so you can allow the

force directed graph to render in a `devel = true` session, then when

you are satisfied pause the force layout using the buttons available in

pATLAS and click at the same time `Shift+Ctrl+Space`. This will take a

while but eventually it will generate a file named `filtered.json`.

Once you have this file you can add it to the

`patlas/db_manager/db_app/static/json` folder and change the

`devel` variable to `false`. This will use the previously saved

positions to render a pre rendered network.