Solving Multiple Sequence Alignments with Python

Sequoya is an open source software tool aimed at for solving Multiple Sequence Alignment problems with multi-objective metaheuristics.

This tool implements a distributed async version of the M2Align algorithm as shown in:

"M2Align: parallel multiple sequence alignment with a multi-objective metaheuristic". Cristian Zambrano-Vega, Antonio J. Nebro José García-Nieto, José F. Aldana-Montes. Bioinformatics, Volume 33, Issue 19, 1 October 2017, Pages 3011–3017 (DOI).

Features

• Score functions:
  • Sum of pairs,
  • Star,
  • Minimum entropy,
  • Percentage of non-gaps,
  • Percentage of totally conserved columns,
  • STRIKE.
• Algorithm:
  • NSGA-II,
  • Distributed NSGA-II
• Crossover operator:
  • Single-point crossover (GapSequenceSolutionSinglePoint).
• Mutation operators:
  • Shift closest gap group (ShiftClosedGapGroups),
  • Shift gap group (ShiftGapGroup),
  • Random gap insertion (OneRandomGapInsertion),
  • Merge two random adjacent gaps group (TwoRandomAdjacentGapGroup),
  • Multiple mutation (MultipleMSAMutation).

Install

To download and install Sequoya just clone the Git repository hosted in GitHub:

git clone https://github.com/benhid/Sequoya.git
cd Sequoya
python setup.py install

Or via pip:

pip install Sequoya

Usage

Examples of running Sequoya are located in the examples folder:

Dask distributed

For running Sequoya in a cluster of machines, first setup a network with at least one dask-cheduler node and several dask-worker nodes:

conda create --name dask-cluster
conda activate dask-cluster

pip install git+https://github.com/benhid/Sequoya.git@develop

Then, on the master node run:

dask-scheduler

On each slave node run:

dask-worker <master-ip>:8786 --nprocs <total-cores> --nthreads 1

Authors

Active development team

• Antonio Benítez-Hidalgo antonio.b@uma.es
• Antonio J. Nebro antonio@lcc.uma.es

License

This project is licensed under the terms of the MIT - see the LICENSE file for details.