Utitilies for constructing and manipulating models for non-local structural dependencies in genomic sequences
Project description
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:
- 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 |
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Computing a structure-aware evolution-adaptive notion of distance between genomes, which demonstrably is much more biologically relevant compared to the standard edit distance
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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
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