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A Multi-Objective algorithm for DNA Design and Assembly

Project description

MOODA: Multi-Objective Optimization for DNA design and assembly

Current version: 0.9.5

build platform anaconda

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-specified objectives.

Currently, some of the most common common operations in synthetic biology are implemented:

  • The GC content operator reduces the difference between the GC content of a sequence and the GC content set as the target. It introduces silent mutation inside CDSs, to increase or decrease the GC content.

  • The Codon usage operator allows the recoding of CDSs according to the specified codon distribution. At each iteration, a specified number of codons is replaced by synonymous

  • The BlockJoin and BlockSplit operators allow the division of the sequence into blocks, given a minimum and maximum size. After the optimisation, each block is then adapted to the selected assembly method. Currently, only Gibson assembly is supported.

New operators, objective functions or assembly method can be added by extending the Operator, ObjectiveFunction and Assembly classes.


The easiest and fastest method to use mooda is using Docker:

    docker pull

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

Extract test data as follows:

    $ tar xvzf mooda-example1.tar.gz

Run mooda as follows:

    $ docker run -it --rm -v $PWD:$PWD -w $PWD -ag mo -i  -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

  • -ag: Algorithm to run can be either mo for Multi-Objective, either mc for Monte Carlo, mo is suggested for long sequences, Monte Carlo for small sequences and codon usage optimization. Default: mo.

  • -i: Input DNA sequence to process.

  • -c: Configuration file to set MOODA operators, objective functions and their parameters.

  • -p: Pool size. The -p parameter should increase with the sequence size. It improves solution quality, however the computing time increase as well.

  • -it: Number of iterations. The -it parameter should increase with the sequence size. It improves solution quality more than -p parameter, however the computing time increase as well.

  • -a: Archive size, amount of non-dominated solutions to store at each algorithm iteration, allow to use smaller values for the pool size.

  • -mns: Sequence block minimum size.

  • -mxs: Sequence block maximum size.

  • -bss: Sequence block step size, define the minimum variance between block lengths. Default: 50.

  • -js: Sequence block assembly overlap size, define the amount of overlap between sequence blocks. Default: 40.

  • -dir: Output directory for MOODA results.

  • -gf: Allow the writing of FASTA and GenBank files, related to MOODA solution if set as True. Default: False.



Design and assembly of DNA molecules using multi-objective optimisation. Angelo Gaeta, Valentin Zulkower and Giovanni Stracquadanio. bioRxiv.


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