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https://github.com/pockerman/hmmtuf_app

hmmtuf is a simple mapping tool that uses hidden Markov models in order to label DNA regions.
https://github.com/pockerman/hmmtuf_app

bioinformatics biology celery dash django django-application dna hidden-markov-model pomegranate python3 spark

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hmmtuf is a simple mapping tool that uses hidden Markov models in order to label DNA regions.

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README 

        
# hmmtuf



Thermodynamically ultra-fastened (TUF) regions are stretches of the DNA which fail to denature

even after the application of extreme melting conditions [1]. This behavior effectively reduces the amplification efficiency in these regions. 

It has also been reported that  TUF regions contain a core sequence which exhibits an increased GC concentration relative to the surrounding DNA. 

It is in fact these locally concentrated spikes of GC content which is believed to remain duplexed despite the application of denaturation processes [1].



Computational modelling and analysis of TUF regions requires the ability to somehow identify these in the 

DNA strand. However, visually, identifying and labeling TUF regions, although feasible, is time consuming to say the least. 



```hmmtuf``` is an simple mapping tool that uses hidden Markov models in order to label regions of DNA. Users can load

their own developed models. The application uses pomegranate for

representing a hidden Markov model. We provide a default Markov model which can be found at ```hmmtuf_app/hmm_files/HMM1.json```. 

The report in ```hmmtuf_app/notes/hmmtuf_model_report.pdf``` describes how this model was developed.



The application currently supports the following computations



- Compute the Viterbi path of a single region

- Compute the Viterbi path over all regions of a chromosome

- Compute the repeats over a computed Viterbi path

- Compute distances between repeats

- Visualize distances distributions



The following image displays the marked windows accoding to the computed Viterbi path in the IGV browser.






The following schematic displays approximately the role of the various component.






## Dependencies



- django

- celery

- pomegranate

- SQLite or MySQL

- pysam

- biopython

- textdistance



#### Note



You also need to install one of the backends supported by Celery. For more

information see here.



A Dockerize version of the project can be found in this repository.



## Configuration 



The general configuration is controlled by the ```config.py``` script in the ```hmmtuf/```

application directory. The following variables can be configured



- ```DEBUG``` 

- ```LOCAL_DEPLOY```

- ```SESSION_COOKIE_SECURE```

- ```CSRF_COOKIE_SECURE```

- ```USE_CELERY```

- ```ENABLE_SPADE```

- ```SPADE_PATH```

- ```DATA_PATH```

- ```USE_DJANGO_EXTENSIONS```



## Setup the project locally



1. ```cd``` to the top-level directory of the project i.e. where the ```manage.py``` file is located

2. Issue: ```python manage.py makemigrations```

3. Issue: ```python manage.py migrate```



The last two commands will setup the needed DB tables. You can now launch the development server.



Alternatively, you can apply migrations only for a specific application:



2. ```python manage.py makemigrations app_name```

3. ```python manage.py migrate app_name```



```

python manage.py runserver

```



## Setup ```admin``` site



1. Make sure that you have applied the migrations from ```admin``` and ```auth``` applications

2. Issue: ```python manage.py createsuperuser```

3. Fill in the details asked

4. Fire up the development server and visit ```http://127.0.0.1:8000/admin``` to login



## DB Handling



Currently, the project uses SQLite the default backend 

in Django. The following are some commands we can use during the

development



- Launch the ```dbshell```



```python manage.py dbshell```



- View tables and databases



```.tables```

```.databases```



- Drop all model tables



```python manage.py migrate zero``` 



## Monitoring Celery



You can use flower in order to monitor Celery



## Deploy with Apache with ```mod_wsgi```



Some instructions on how to deploy with Apache and ```mod_wsgi``` can be found here.



## Deploy with Gunicorn



There are several tools that can be used for deployment. A common scenario is using 

Gunicorn a Python WSGI HTTP Server for UNIX and 

nginx an HTTP and reverse proxy server. 



You can find instructions how to deploy the application using Gunicorn and 

nginx here.

A minimum configuration that most likely is only suitable for local development/testing, can be:



#### gunicorn configuration



**gunicorn.socket** 



```

[Unit]

Description=gunicorn socket



[Socket]

ListenStream=/run/gunicorn.sock



[Install]

WantedBy=multi-user.target

```



**gunicorn.service**



```

[Unit]

Description=gunicorn daemon

Requires=gunicorn.socket

After=network.target



[Service]

User=alex

Group=www-data

WorkingDirectory=/path/to/hmmtuf

ExecStart=gunicorn --access-logfile - \

		  --workers 3 \

          --bind unix:/run/gunicorn.sock \

          hmmtuf.wsgi:application



[Install]

WantedBy=multi-user.target

```



#### nginx configuration



```

upstream app_server { 

server unix:/run/gunicorn.sock fail_timeout=0;

}



server {

	listen 80 default_server;

	listen [::]:80 default_server ipv6only=on;



	# set the correct hosts

	server_name localhost;



    access_log /path/to/where/logs/should/go/nginx-access.log;

    error_log /path/to/where/logs/should/go/nginx-error.log;



	location  /favicon.ico {

			access_log off; log_not_found off;

	}



	location /static {

		autoindex on;

        # this should point to where collectstatic

        # appends the files

		alias /path/to/hmmtuf/static;

	}



	location /viterbi_paths {

		autoindex on;

		alias /path/to/hmmtuf/viterbi_paths/;

	}



	location / {

		proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;

		proxy_set_header Host $http_host;

		proxy_pass http://app_server;

	}

}

```



Provided that everything has worked, you can access the application at: ```http://localhost/```



## References



1. Colin D. Veal and Peter J. Freeman and  Kevin Jacobs and Owen Lancaster and Stéphane Jamain and Marion Leboyer and Demetrius Albanes and Reshma R. Vaghela and Ivo Gut and Stephen J. Chanock and Anthony J. Brookes, ```A mechanistic basis for amplification differences between samples and between genome regions```, BMC Genomics, 2012

2. Rabiner L. R., ```A tutorial on Hidden Markov Models and selected applications in speech recognition```,