Utitilies for constructing and manipulating models for non-local structural dependencies in genomic sequences
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:
- Compute the Quasinet (Q-net) given a database of nucleic acid sequences. The Q-net is a family of conditional inference trees that capture the predictability of each nucleotide position given the rest of the genome. The constructed Q-net for COVID-19 and Influenza A H1N1 HA 2008-9 is shown below.
Compute a structure-aware evolution-adaptive notion of distance between genomes, which is demonstrably more biologically relevant compared to the standard edit distance.
Draw samples in-silico that have a high probability of being biologically correct. For example, given a database of Influenza sequences, we can generate a new genomic sequence that has a high probability of being a valid influenza sequence.
To install with pip:
pip install quasinet
To fix error with Mac or Windows:
from quasinet.osfix import osfix # for windows osfix('win') # for max x86_64 (macbook pro) osfix('macx86') # mac arm (macbook air) osfix('macarm')
NOTE: If trying to reproduce the paper below, please use
pip install quasinet==0.0.58
from quasinet import qnet # initialize qnet myqnet = qnet.Qnet() # train the qnet myqnet.fit(X) # compute qdistance qdist = qnet.qdistance(seq1, seq2, myqnet, myqnet)
Examples are located here.
For more documentation, see here.
For reference, please check out our paper:
You can reach the ZED lab at: zed.uchicago.edu
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