Metadata-Version: 1.1
Name: cobilib
Version: 1.0.0
Summary: Optimizing Codon Usage with a Quasispecies Model
Home-page: TODO.orggithub?
Author: Jan-Hendrik Trösemeier, Christel Kamp, Susanne Lipp
Author-email: jan-hendrik.troesemeier@pei.de
License: LICENSE
Download-URL: TODO.orggithub?.tar.gz
Description: Summary
        -------
        We provide a library that enables us to select a number of reference genes to
        which codon usage should be optimized. Furthermore, we allow for input of a
        variable amount of fitness factors: translation speed of codons, tRNA
        abundance, etc.  Given these contributing fitness factors the result is
        displayed as the strength of the respective fitness factors that lead to the
        best resemblance between simulated and reference codon usage. In a next step,
        the strengths can be tuned and a codon usage can be generated that can
        afterwards be used to adapt a gene sequence with the help of classic codon
        optimization tools as OPTIMIZER.
        
        Example
        -------
        In an example workflow you might want to select a fasta file that
        contains the genes you want use. You can either select them from a file
        or a url. In both cases a histogram of codon usage and amino acid usage
        is generated.
        
        You can then (optionally) load a list of highly expressed genes, we
        support the format from the HEG database.  Visualizing the codon usage
        bias for e.g. checking if the CUB as you expect can be done by plotting
        various methods of dimensionality reduction.
        
        If you do not want to use all the genes you can enter a
        number n. The first n genes will only be analysed.
        
        You now have to select a fitness matrix which gives the probability of
        one amino acid to be represented by another one.
        
        Additionally, you can select a number of fitnessfunctions that assign
        to each codon a fitness. These functions will be normalized!
        If you want to perform a test run you
        have to enter the parameters: alpha,beta,selection,t_i for every
        testfunction. alpha and beta are parameters for the <todo> model of
        codon substitution and are related to transition/transversion bias.
        Input is either comma or whitespace/tab separated (or a combination of
        those).
        
        You can compare the absolute codon usage and relative (normalized for
        each amino acid) codon usage by plot comparison.  For optimizing the
        distance you can try optimizing the first gene and again regard the
        comparison to see if the algorithm works at all.
        
        In a last step you can optimize all genes you have read in. Returned
        are the optimal parameters, a goodness of fit and the RSCU that you can
        use for optimizing with the help of, e.g., OPTIMIZER.
        
        Authors and License
        -------------------
        GPLv3
        Jan-Hendrik Trösemeier,
        Susanne Lipp,
        Christel Kamp
        
        Contact: name.lastname at pei.de
        
Keywords: bioinformatics
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Environment :: Console
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires: sklearn
Requires: matplotlib
Requires: inspyred
