This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
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Project Description
A frequency/bias codon optimisation system with early NGG avoidance and optional
IUPAC match avoidance.

By Cathal Garvey, released under the GNU Affero General Public License.

PySplicer is pure python and requires no compilation or building for basic function.
For optimal output, you should also install the ViennaRNA package using the downloads
[here](; you can do this before
or after installing PySplicer, it doesn't matter.

The easiest way to install pysplicer is using pip-3.2, if you have it installed.
easy-install may also work to install pysplicer without headaches.

The next best thing is to download the latest version from the github repository,
enter the directory through the terminal, and call "python3 install"
with administrator/sudo/root privileges.

PySplicer consists of a Python Module and a python script for terminal usage.
For usage information on the script, after installing this package with pip-3.2
or, try typing "pysplicer --help" into the terminal.

You'll need ViennaRNA installed to make use of the --use-vienna option; if you can,
do! This will make PySplicer use RNAfold instead of using its own highly terrible
algorithm for structure analysis, which will certainly generate better results.
Do be aware that, even with ViennaRNA installed, structure prediction is certainly
the most computationally intensive part of PySplicer's work, so expect compilations
of large sequences to take a long time.

What is PySplicer?
PySplicer is a Free Software implementation of a codon optimisation method where
codons are selected based on a frequency table, usually to match target host
frequencies but also (where possible) to match empirically determined
high-expression tables which usually give better expression results.

PySplicer does not (yet) implement all best practices for codon optimisation,
but compared to most available Free Software codon optimisation programs, it is
pretty good. While most programs still make use of the somewhat-discredited
"best pick" method, where the most common codon in "highly expressed" genes is
preferentially selected wherever possible, the frequency-matching approach appears
to deliver better results in general, and a frequency matching approach that
biases towards codons whose tRNAs remain charged under starvation conditions
appears to give even better results.

PySplicer can be directed to avoid DNA/RNA sequences in the final output, which
it attempts to accomplish firstly be generating large numbers of candidates and
walking through them to avoid such sites, and then by attempting to substitute
synonymous codons at each such site. Subsequences to avoid can be given in full
extended IUPAC notation, so that AWGS can refer to AAGG, AAGC, ATGG,
or ATGC. It *may* be relevant to note that all sequences are converted to DNA
internally prior to use for the sake of internal consistency,

Structure avoidance/removal process can have a significant and positive impact on
successful gene expression, particularly when structures in the 5' UTR or the
first few codons of a CDS are eliminated.

PySplicer can outsource structure prediction to ViennaRNA tools if they are
present and the option is selected. This is *highly recommended*. Detected
structures are then removed using the same methods as for sequences nominated
for avoidance, as above.
If ViennaRNA is not installed, PySplicer will attempt to map simple hairpin
structures, but this is far slower, less reliable, generally less useful, and
will omit searches for other or more complex structures. It's only an option
where ViennaRNA is not present (see above for a ViennaRNA download page).

PySplicer makes a special effort to avoid "NGG" codons in the first few codons
of the output sequence, as these are associated with failed transcription, and
attempts to bias slightly towards "A" nucleotides to somewhat reduce the
odds of a secondary structure forming in the critical 5' leader region.

Finally, PySplicer aims for a *lower* codon adaptation index in the initial codons
of the output sequence, in keeping with the "on-ramp" hypothesis for avoidance of
ribosomal collision. In some empirical studies a lower CAI in initial codons is
associated with higher expression overall, though PySplicer sadly cannot be compared
empirically against other adaptation solutions due to funding constraints, so for
now this is taken on faith.

Using Your Own Tables
If a codon usage table is not available, a JSON-formatted file containing a
codon usage table can be specified. The format should be a JSON Object (akin to
a python dict with the same syntax) containing single-letter amino keys, which
point to dicts containing Codon keys, which each point to float values representing
relative frequencies. Keys are all uppercase DNA, not RNA. For example:
{"A": {"GCA": 0.15, "GCC": 0.18, "GCG": 0.51, "GCT": 0.15},
"C": {"TGC": 0.65, "TGT": 0.35}...}

To simplify this process, a utility script is included, "cud-to-pysplicer", which
will accept a filename of a Codon Usage Database (CUD) frequency table and will
translate this table to a pysplicer-format JSON table. It accepts an optional
"-t" switch which specifies with codon table to use; it defaults to the "standard"
table. Calling cut-to-pysplicer with the "--help" switch will print usage
information and also a list of possible translation tables to specify.
Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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