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
>>> from GeneVecTools import reader
>>> from GeneVecTools import mapper
>>> from GeneVecTools import encoder

.. 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=file, length=10000, encoding="one-hot-encoding",bits=8)
>>> sequences = VECSS.readq()

.. code-block:: bash

# The function "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
>>> assert VECSS.unembed(VECSS.embed(VECSS.s)) == VECSS.s

.. code-block:: bash

# Extracting sequences
>>> R = reader.Reader()
>>> mp, count, total_len, quality = R.read_fastq(dir)
>>> sequences_dict_items = mp.values()
>>> sequences = list(sequences_dict_items)
>>> print(sequences)

.. code-block:: bash

# Clustering
>>> mapObj = mapper.Mapper(sequences, 2, 3)
>>> groups_of_similar_kmers = mapper.cluster(mapObj.hfs)
>>> cluster_of_sequences = mapper.groupings(groups_of_similar_kmers, sequences)
>>> print(cluster_of_sequences)

.. code-block:: bash

# Encoding
>>> encoder =encoder. Encoder(4)
>>> c = encoder.encode_sequences(sequences)
>>> print(c)

.. code-block:: bash

# Compress
>>> encoded_clusters_compressed = encoder.encode_clusters(cluster_of_sequences)
>>> print(encoded_clusters_compressed)

.. code-block:: bash

# Decompress
>>> decoded_clusters_compressed = encoder.decode_clusters(encoded_clusters_compressed)
>>> print(decoded_clusters_compressed)

.. code-block:: bash

# Testing the compressing and decompressing process
>>> assert cluster_of_sequences == decoded_clusters_compressed

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Source Distribution

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