Metadata-Version: 2.1
Name: mooda-dna
Version: 0.11.0
Summary: A Multi-Objective algorithm for DNA Design and Assembly
Home-page: https://github.com/stracquadaniolab/mooda
Author: Angelo Gaeta, Giovanni Stracquadanio
Author-email: a.gaeta@sms.ed.ac.uk
License: MIT
Description: # MOODA: Multi-Objective Optimization for DNA design and assembly
        
        Current version: 0.11.0
        
        ![build](https://github.com/stracquadaniolab/mooda/workflows/release/badge.svg)
        ![platform](https://anaconda.org/stracquadaniolab/mooda/badges/platforms.svg)
        ![anaconda](https://anaconda.org/stracquadaniolab/mooda/badges/version.svg)
        
        MOODA is a multi-objective optimisation algorithm for DNA sequence design and assembly.
        
        It takes in input an annotated sequence in GenBank format, and optimize it with
        respect to user-defined objectives.
        
        Currently, some of the most common common operations in synthetic biology are
        built-in, including:
        
        - The `GCOptimizationOperator` introduces silent mutation in coding regions to
          obtain DNA constructs with a user-defined GC content.
        
        - The `CodonUsageOperator` probabilistically recodes coding regions by
          probabilistically selecting the most frequent codon for an aminoacid in a host
          organism.
        
        - The `BlockJoin` and `BlockSplit` operators allow the division of a sequence
          into fragments (or blocks). After the optimisation, each block is then adapted
          to the assembly method selected by the user. Currently, only the Gibson
          assembly is supported.
        
        New operators, objective functions or assembly method can be added by extending
        the `Operator`, `ObjectiveFunction` and `Assembly` classes.
        
        ## Installation
        
        The easiest and fastest method to use `mooda` is using Docker:
        
        ```
            docker pull ghcr.io/stracquadaniolab/mooda
        ```
        
        You can also install `mooda` using `conda`:
        
        ```
            $ conda install -c stracquadaniolab -c bioconda -c conda-forge mooda
        ```
        
        or using `pip`:
        
        ```
            $ pip install mooda-dna
        ```
        
        Please note, that `pip` will not install non Python requirements.
        
        ## Getting started
        
        A typical `mooda` analysis consists of 3 steps:
        
        1. Select a DNA sequence in Genbank format.
        
        2. Write a MOODA configuration file. A `.yaml` file defining operators,
           objective functions, assemblies strategy and their parameters.
        
        3. Run MOODA.
        
        ### Example: optimizing GC content, E. coli codon usage, number of fragments and the variance of their length
        
        Create a test directory as follows:
        
        ```
            $ mkdir example-run
        ```
        
        Move to your test directory as follows:
        
        ```
            $ cd example-run
        ```
        
        Download test data from Github as follows:
        
        ```
            $ curl -LO https://github.com/stracquadaniolab/mooda/raw/master/examples/mooda-example1.tar.gz
        ```
        
        Extract test data as follows:
        
        ```
            $ tar xvzf mooda-example1.tar.gz
        ```
        
        Run `mooda` as follows:
        
        ```
            $ docker run -it --rm -v $PWD:$PWD -w $PWD ghcr.io/stracquadaniolab/mooda -i seq_5_5.gb  -c config.yaml -p 10 -it 20 -a 100 -mns 200 -mxs 2000 -bss 50 -js 40 -dir example-opt -gf True
        ```
        
        Results will be available in the `example-opt` directory, where you will find:
        
        - `Genbank` files of the Pareto optimal sequence.
        - `FASTA` files with the fragments for Gibson assembly for each Pareto optimal
          sequence.
        - `_logfile.yaml` file with information about the analysis.
        - `_mooda_result.csv` file with objective function value information for each
          sequence.
        
        #### Command line options
        
        - **-i**: Input DNA sequence to process.
        
        - **-c**: Configuration file to set operators, objective functions and their
          parameters.
        
        - **-p**: Pool size. Number of candidate solutions sampled at each iteration.
          The pool size should increase with the length and complexity of the input
          sequence.
        
        - **-it**: Number of iterations.  The number of iterations should increase with
          the length and complexity of the input sequence, although it will take longer
          to run.
        
        - **-a**: Archive size. The number of non-dominated solutions to store at each
          iteration, which allows to use smaller pools for improved efficiency.
        
        - **-mns**: Block minimum size.
        
        - **-mxs**: Block maximum size.
        
        - **-bss**: Sequence block step size, define the minimum variance between block
          size. Default: 50.
        
        - **-js**: Sequence block assembly overlap size, define the amount of overlap
          between blocks. Default: 40.
        
        - **-dir**: Output directory for MOODA results.
        
        - **-gf**: Allow the writing of FASTA and GenBank files. Default: False.
        
        ## Authors
        
        - Angelo Gaeta, a.gaeta@sms.ed.ac.uk
        - Giovanni Stracquadanio, giovanni.stracquadanio@ed.ac.uk
        
        ## Citation
        
        [Design and assembly of DNA molecules using multi-objective optimization](https://academic.oup.com/synbio/article-abstract/6/1/ysab026/6387748).
        A Gaeta, V Zulkower, G Stracquadanio - Synthetic Biology, 2021
        
        ```
        @article{10.1093/synbio/ysab026,
            author = {Gaeta, Angelo and Zulkower, Valentin and Stracquadanio, Giovanni},
            title = "{Design and assembly of DNA molecules using multi-objective optimization}",
            journal = {Synthetic Biology},
            volume = {6},
            number = {1},
            year = {2021},
            month = {10},
            issn = {2397-7000},
            doi = {10.1093/synbio/ysab026},
            url = {https://doi.org/10.1093/synbio/ysab026},
            note = {ysab026},
            eprint = {https://academic.oup.com/synbio/article-pdf/6/1/ysab026/40977182/ysab026.pdf},
        }
        ```
        
        ## Issues
        
        Please post an issue to report a bug or request new features.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
