jlsteenwyk / biokit

a versatile toolkit for processing and analyzing diverse types of sequence data

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BioKIT is a UNIX shell toolkit for processing molecular sequence data.

If you found biokit useful, please cite the following: BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. Genetics. doi: 10.1101/2021.10.02.462868.

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This documentation covers downloading and installing BioKIT. Details about each function as well as tutorials for using BioKIT are available in the online documentation.

Installation

If you are having trouble installing BioKIT, please contact the lead developer, Jacob L. Steenwyk, via email or twitter to get help.

To install using pip, we strongly recommend building a virtual environment to avoid software dependency issues. To do so, execute the following commands:

# create virtual environment
python -m venv .venv
# activate virtual environment
source .venv/bin/activate
# install biokit
pip install jlsteenwyk-biokit

Note, the virtual environment must be activated to use biokit.

After using biokit, you may wish to deactivate your virtual environment and can do so using the following command:

# deactivate virtual environment
deactivate

Similarly, to install from source, we strongly recommend using a virtual environment. To do so, use the following commands:

# download
git clone https://github.com/JLSteenwyk/BioKIT.git
cd biokit/
# create virtual environment
python -m venv .venv
# activate virtual environment
source .venv/bin/activate
# install
make install

To deactivate your virtual environment, use the following command:

# deactivate virtual environment
deactivate

Note, the virtual environment must be activated to use biokit.

To install via anaconda, execute the following command:

conda install -c jlsteenwyk jlsteenwyk-biokit

Visit here for more information: https://anaconda.org/JLSteenwyk/jlsteenwyk-biokit

To test biokit installation, launch the help message

biokit -h

biokit's Issues

RSCU issue

Hi,

I'm noticing some inconsistencies with rscu results.

Here's an example sequence:

>seq
ATGCGTCTGTACTTGATTCTGTCAGCATCGATGGTCATCAGCTATTTTGTGACCTGTAACGCGGCTACGAGTTCCGATCA
GACCAAGCTCTCGAGAATGGAGCCATCACTCCTCGTCCACTCACTCGGTGCAGCACTGAACGTCGATGCCGTCGACAGAA
GATTTCTTCGAGAGCGAAAAGAAAGTATGGAAGAGCGAGGATATCATCTGACGCTAAAAGGGGAAGTAAAAGCATTGGCC
AAGAAAATCATTGCAGATTTCGACACGGCCGACGACGTGTACAAGAAGTGGAATGAGAACGGCCACTCGTTGAACAAAAT
CGCGAACTTGCTGAAAGTGTCGGAAAAGGAAAAATACGTACCGGTGTACAACGGCTATCTGGCCTACCTGAACAGGATAA
ATAGCTGA

And the biokit rscu output:

CGA	4.5
AGA	4.5
ACG	4.0
UGA	3.0
ACC	2.6667
CUG	2.4706
UCG	2.0
CCA	2.0
CCG	2.0
CAG	2.0
...
...

As an example codon, I think the RSCU of CGA should be 2.25?
3/(8/6)

Looking through the code, it looks like the observed_sum value is wrong as it is reset to 0 when a KeyError exception is thrown:

BioKIT/biokit/services/coding_sequences/base.py

Lines 54 to 60 in 595a79d

	for _, codons in codon_table.items():
	observed_sum = 0
	for codon in codons:
	try:
	observed_sum += codon_counts[codon]
	except KeyError:
	observed_sum = 0

If I replace this with a pass statement, I get the expected result 2.25.

Conda version of Biokit

Hi!

I just wonder could the newest version of biokit also be uploaded in conda?

Cheers