About

CoreTracker detects evidences of codon reassignment from the protein repertoire of a set of genomes by successively applying different algorithms. It’s a filtering approach that explore all possible reassignments in every genomes from the input set, and retain only the most promising one.

Detailed information about the package, installation and tutorials are available here ==> http://udem-lbit.github.io/CoreTracker/

Installation

First install the dependencies which include gfortran, PyQt4 muscle, mafft and hmmer. PyQt4 also require Sip and qt. It’s easier to install those two using distribution specific packages.

You can now download the github project and install using python setup.py install or pip (pip install coretracker).

I recommend setting a virtual environment through virtualenv.

Alternatively, you can also install it with conda, which is the easiest way : conda install -c maclandrol coretracker.

Help for installation is available at Coretracker: Installation

Basic Help

After installation, run coretracker -h for help.

An example of execution is :

coretracker -t speciestree.nw -p protein.ali -n nucsequences.core --gapfilter 0.4 --iccontent 0.3 --idfilter 0.5 --norefine --wdir outdir --params param.yml

Additional parameters can be set using the --params option. See the provided template (param.yml).

Citation

If you use coretracker, please cite:

Noutahi E, Calderon V, Blanchette M, Lang FB, El-Mabrouk N. CoreTracker: accurate codon reassignment prediction, applied to mitochondrial genomes. Bioinformatics. 2017 Jun 26;33(21):3331-9.

@article{noutahi2017coretracker,
  title={CoreTracker: accurate codon reassignment prediction, applied to mitochondrial genomes},
  author={Noutahi, Emmanuel and Calderon, Virginie and Blanchette, Mathieu and Lang, Franz B and El-Mabrouk, Nadia},
  journal={Bioinformatics},
  volume={33},
  number={21},
  pages={3331--3339},
  year={2017},
  publisher={Oxford University Press}
}