GeneVecTools 1.35

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GeneVecTools

Reading in Variety of Genetic File Types

Vector Embedding Algorithms

Byte Array Encoders

Clustering and Preprocessing Steps for Compression

Similarity Search Tools for FASTA/FASTQ files

Installing

Tester files: https://tinyurl.com/cDNALibraryExampleFiles

.. code-block:: bash

pip install GeneVecTools

Usage

.. code-block:: bash

>>> from GeneVecTools import SimSearch

.. code-block:: bash

"""
file is location of the "small_cDNA_Sequences_pbmc_1k_v2_S1_L002_R2_001.fastq" 
that you downloaded from https://tinyurl.com/cDNALibraryExampleFiles
if it is in current directory, just use file name
"""
>>> file = "small_cDNA_Sequences_pbmc_1k_v2_S1_L002_R2_001.fastq"

.. code-block:: bash

"""
f is the file location and name
length is the number of sequences we want in our scope
encoding is one of three choices: "one-hot-encoding", "standard", or "no-encoding"
bits is one of three choices: 2, 4, or 8
"""
>>> VECSS = SimSearch.VecSS(f=dir, length=10000, encoding="one-hot-encoding",bits=8)
>>> sequences = VECSS.readq()

.. code-block:: bash

# embed produces the vector embedding of the sequence
>>> embedded = VECSS.embed(VECSS.s)
>>> print(embedded)

.. code-block:: bash

"""
similarity search
I are the indices of the similar sequences
D are how different the similar sequences are from the query sequence
time is the time it takes to perform this similarity search query
"""
>>> D, I, time = VECSS.run_search()
>>> print(D,I,time)

.. code-block:: bash

#Testing the embedding and umembedding process
>>> print(VECSS.unembed(VECSS.embed(VECSS.s)) == VECSS.s)

'True'