Solving Multiple Sequence Alignments with Python
Project description
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
).
- Single-point crossover (
- 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
).
- Shift closest gap group (
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
License
This project is licensed under the terms of the MIT - see the LICENSE file for details.
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