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Calculate allele frequency from a sequence multialignment.

Project description

allfreqs

https://img.shields.io/pypi/v/allfreqs.svg Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. https://travis-ci.com/robertopreste/allfreqs.svg?branch=master Documentation Status Python 3 https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg

Calculate allele frequencies from a sequence multialignment.

Features

Calculate allele frequencies from a nucleotide multialignment in fasta or csv format.

Allele frequencies will be returned as a table in which each row is a nucleotide position (based on the provided reference sequence) and columns are A, C, G, T frequencies as well as gaps and other non-canonical nucleotides.

For example, given the following multialignment:

ID Sequence
ref ACGTACGT
seq1 A-GTAGGN
seq2 ACCAGCGT

the resulting allele frequencies will be:

position A C G T gap oth
1.0_A 1.0 0.0 0.0 0.0 0.0 0.0
2.0_C 0.0 0.5 0.0 0.0 0.5 0.0
3.0_G 0.0 0.5 0.5 0.0 0.0 0.0
4.0_T 0.5 0.0 0.0 0.5 0.0 0.0
5.0_A 0.5 0.0 0.5 0.0 0.0 0.0
6.0_C 0.0 0.5 0.5 0.0 0.0 0.0
7.0_G 0.0 0.0 1.0 0.0 0.0 0.0
8.0_T 0.0 0.0 0.0 0.5 0.0 0.5

allfreqs can be used either as a command line tool or through its Python API.

For more information, please refer to the Usage section of the documentation.

Installation

PLEASE NOTE: allfreqs only supports Python >= 3.6!

The preferred installation method for allfreqs is using pip:

$ pip install allfreqs

For more information, please refer to the Installation section of the documentation.

Credits

This package was created with Cookiecutter and the cc-pypackage project template.

History

0.1.0 (2019-07-08)

  • First release.

0.1.1 (2019-08-08)

  • Read and process multialignments from fasta and csv files (Python module only).

0.1.2 (2019-10-17)

  • Add tests with and without reference included in multialignments;
  • Add tests with real datasets (coming from haplogroup-specific multialignments).

0.1.3 (2019-10-18)

  • Add more detailed tests for real datasets;
  • Implement more efficient frequency calculation;
  • Add dunder methods and sanity checks;
  • Fix requirements and testing framework;
  • Clean code.

Project details


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