quasinet 0.0.49

Utitilies for constructing and manipulating models for non-local structural dependencies in genomic sequences

Quasinet

Description

Infer non-local structural dependencies in genomic sequences. Genomic sequences are esentially compressed encodings of phenotypic information. This package provides a novel set of tools to extract long-range structural dependencies in genotypic data that define the phenotypic outcomes. The key capabilities implemented here are as follows:

1. computing the q-net given a database of nucleic acid sequences, which is a family of conditional inference trees capturing the predictability of each nucleotide position given the rest of the genome.

COVID-19	INFLUENZA

2. Computing a structure-aware evolution-adaptive notion of distance between genomes, which demonstrably is much more biologically relevant compared to the standard edit distance

3. Ability to draw samples in-silico, that have a high probability of being biologically correct. For example, given a database of HIV sequences, we can generate a new genomic sequence, which has a high probability of being a valid encoding of a HIV virion. The constructed q-net for long term non-progressor clinical phenotype in HIV-1 infection is shown below.

Installation

To install with pip:

pip install quasinet

To install with conda:

conda install quasinet

Dependencies

• scikit-learn
• scipy
• numpy
• numba
• pandas
• joblib
• biopython

Usage

from quasinet import qnet

# initialize qnet
myqnet = qnet.Qnet()

# train the qnet
myqnet.fit(X)

# compute qdistance
qdist = qnet.qdistance(seq1, seq2, myqnet, myqnet) 

Authors

You can read the ZED lab at: zed.uchicago.edu