Dsembler is an easy to use web program for assembling short genomes that selects the best possible oligomer sequences based on the users' target parameters.
Dsembler can be installed via GitHub.
$ git clone https://github.com/sblabkribb/dsembler.git
Users should have docker installed on their computer to run Dsembler locally. Docker can be downloaded from https://docs.docker.com/get-docker/ [Ensure your BIOS settings are compatible with the docker application]. Run the following commands on your terminal. Move to the dsembler directory
$ docker build -t dsembler:latest .
Or pull the docker image from Dockerhub
$ docker pull sblabkribb/dsembler:latest
$ docker run -d --rm -v $(pwd):/app --publish 5000:5000 --name dsembler dsembler> docker run -d --rm -v %cd%:/app --publish 5000:5000 --name dsembler dsemblerReplace $(pwd) and %cd% with the directory on your local machine you want to store the outputs
Users can directly run dsembler via Python. However, many packages will be required to be installed before a successful run (check requirements.txt).
$ export FLASK_APP=app
$ flask run> $env:FLASK_APP = "app"
> flask run=======================================================================
Detailed User Manual can be found at https://github.com/sblabkribb/dsembler/blob/main/docs/manual.pdf
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- Biopython: This extensive library was used to parse the FASTA files, calculate melting temperature, calculate GC, check Alignment scores between two sequences.
- Flask: This library/tool was used to make a simple web-based user interface. Related libraries such as Flask_bootstrap, wtforms, flask_SQLite supported the page as well
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The algorithm workflow is outlined below figure:
Dsembler is an easy to use web program for assembling short genomes that selects the best possible oligomer sequences based on the users' target parameters.
Dsembler can be installed via GitHub. Users should have docker installed on their computer to run Dsembler locally. Docker can be downloaded from https://docs.docker.com/get-docker/ [Ensure your BIOS settings are compatible with the docker application]. Run the following commands on your terminal
$ git clone https://github.com/sblabkribb/dsembler.git
$ ./docker_build.sh
$ ./docker_run.sh
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Detailed User Manual can be found at https://github.com/sblabkribb/dsembler/blob/main/documents/manual.pdf
The user interface is simple to navigate through as shown below:
First enter in the required parameters to generate appropriate oligomers for your gene assembly.
- Gene Sequence
- Oligomer Size
- Overlap Size
- Target Melting Temperature of overlaps
- Acceptable range of target melting temperature
- Sequence Orientation
- User ID (if saving query)
Note: Melting Temperature of overlaps are calculated based on the Nearest Neighbour Equation and the Sugimoto (1996) thermodynamic table.
Two buttons will appear on the same page once the target parameters are submitted:
- Download the Excel file
- Download the FASTA file
| Login | Previous Work |
|---|---|
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Users can sign up and login to access their previous data. The database was created using the Flask-SQLite.
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- Biopython: This extensive library was used to parse the FASTA files, calculate melting temperature, calculate GC, check Alignment scores between two sequences.
- Flask: This library/tool was used to make a simple web-based user interface. Related libraries such as Flask_bootstrap, wtforms, flask_SQLite supported the page as well
=======================================================================
The algorithm workflow is outlined below figure:
