biotext 3.1.0.0

The biotext library offers resources for natural language processing based on bioinformatics tools

Biotext

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The biotext library offers resources for natural language processing based on bioinformatics tools. With biotext, it is possible to use native bioinformatics methods to create text mining strategies. The biotext process starts by encoding natural language texts in a format based on biological sequences, which are usable in alignment and vectorization approaches.

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Features

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AMINOcode (biotext.aminocode)

• biotext.aminocode.encode_string: Encodes a string using AMINOcode.
• biotext.aminocode.encode_list: Encodes all strings in a list using AMINOcode.
• biotext.aminocode.decode_string: Decodes a string encoded with AMINOcode.
• biotext.aminocode.decode_list: Decodes all strings in a list encoded with AMINOcode.

DNAbits (biotext.dnabits)

• biotext.dnabits.encode_string: Encodes a string using DNAbits.
• biotext.dnabits.decode_string: Decodes a string encoded with DNAbits.
• biotext.dnabits.encode_list: Encodes all strings in a list using DNAbits.
• biotext.dnabits.decode_list: Decodes all strings in a list encoded with DNAbits.

FASTA Tools (biotext.fastatools)

• biotext.fastatools.import_fasta: Imports a FASTA file.
• biotext.fastatools.export_fasta: Creates a FASTA file from a list of sequences.
• biotext.fastatools.create_seqrecord_list: Creates a list of SeqRecord objects from a list of sequences.
• biotext.fastatools.run_clustalo: Performs multiple sequence alignment using Clustal Omega.
• biotext.fastatools.get_consensus: Retrieves the consensus sequence from a set of sequences.
• biotext.fastatools.get_header: Retrieves the headers from a list of SeqRecord objects.
• biotext.fastatools.get_seq: Retrieves the sequences from a list of SeqRecord objects.
• biotext.fastatools.fasta_to_mat: Converts FASTA sequences to a vectorial representation.

Word Embedding Tools (biotext.wordembtools)

• biotext.wordembtools.WordEmbedding: A class for generating word embeddings from a collection of texts.

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Installation

You can install BioText using pip:

pip install biotext

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Functions

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AMINOcode (aminocode)

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biotext.aminocode.encode_string

Encodes a string with AMINOcode.

Parameters

• input_string : str
  • Natural language text string to be encoded.
• detail : str
  • Set details in coding.
  • 'd' for details in digits; 'p' for details on the punctuation; 'dp' or 'pd' for both.
  • Default is 'dp'.

Returns

• encoded_string : string
  • Encoded text.

Example

Encode a string.

import biotext as bt
input_string = "Hello world!"
encoded_string = bt.aminocode.encode_string(input_string,'dp')
print(encoded_string)
# HYELLYQYSYWYQRLDYPW

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biotext.aminocode.encode_list

Encodes all strings in a list with AMINOcode.

Parameters

• string_list : list
  • List of string to be encoded.
• detail : str
  • Set details in coding.'d' for details in digits; 'p' for details on the punctuation; 'dp' or 'pd' for both.
  • Default is 'dp'.
• verbose : bool
  • If True displays progress.

Returns

• encoded_list : list
  • List with all encoded text in string format.

Example

Encode the strings in a list and view the result of the first item.

import biotext as bt
string_list = ['Hello','world','!']
encoded_list = bt.aminocode.encode_list(string_list,detail='dp')
print(encoded_list)
# ['HYELLYQ', 'YWYQRLD', 'YPW']

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biotext.aminocode.decode_string

Decodes a string with AMINOcode reverse.

Parameters

• input_string : str
  • Text string encoded with AMINOcode.
• detail : str
  • Set details in coding. 'd' for details in digits; 'p' for details on the punctuation; 'dp' or 'pd' for both.
  • Default is 'dp'.

Returns

• decoded_string : str
  • Decoded text.

Example

Deconde a string.

import biotext as bt
encoded_string = "HYELLYQYSYWYQRLDYPW"
decoded_string = bt.aminocode.decode_string(encoded_string,'dp')
print(decoded_string)
# hello world!

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biotext.aminocode.decode_list

Decodes all strings in a list with reverse AMINOcode.

Parameters

• string_list : list
  • List of string encoded with aminocode.
• detail : str
  • Set details in coding. 'd' for details in digits; 'p' for details on the punctuation; 'dp' or 'pd' for both.
  • Default is 'dp'.
• verbose : bool
  • If True displays progress.

Returns

• decoded_list : list of string
  • List with all decoded text.

Example

Descode the strings in a list and view the result with a loop.

import biotext as bt
encoded_list = ['HYELLYQ', 'YWYQRLD', 'YPW']
decoded_list = bt.aminocode.decode_list(encoded_list,detail='dp')
print(decoded_list)
# ['hello', 'world', '!']

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DNAbits (dnabits)

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biotext.dnabits.encode_string

Encodes a string with DNAbits.

Parameters

• input_string : string
  • Natural language text string to be encoded.

Returns

• encoded_string : string
  • Encoded text.

Example

Encode a string.

import biotext as bt
input_string = "Hello world!"
encoded_string = bt.dnabits.encode_string(input_string)
print(encoded_string)
# AGACCCGCATGCATGCTTGCAAGATCTCTTGCGATCATGCACGCCAGA

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biotext.dnabits.decode_string

Decodes a string with DNAbits reverse.

Parameters

• input_string : string
  • Text string encoded with DNAbits.

Returns

• decoded_string : string
  • Decoded text.

Example

Decode a string.

import biotext as bt
encoded_string = "AGACCCGCATGCATGCTTGCAAGATCTCTTGCGATCATGCACGCCAGA"
decoded_string = bt.dnabits.decode_string(encoded_string)
print(decoded_string)
# Hello world!

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biotext.dnabits.encode_list

Encodes all strings in a list with DNAbits.

Parameters

• string_list : list
  • List of string to be encoded.
• verbose : bool
  • If True displays progress.

Returns

• encoded_list : list
  • List with all encoded text in string format.

Example

Encode the strings in a list and view the result of the first item.

import biotext as bt
string_list = ['Hello','world','!']
encoded_list = bt.dnabits.encode_list(string_list)
print(encoded_list)
# ['AGACCCGCATGCATGCTTGC', 'TCTCTTGCGATCATGCACGC', 'CAGA']

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biotext.dnabits.decode_list

Decodes all strings in a list with reverse DNAbits.

Parameters

• string_list : list
  • List of string encoded with DNAbits. verbose : bool
  • If True displays progress.

Returns

decoded_list : list of string - List with all decoded text.

Example

Decode the strings in a list and view the result with a loop.

import biotext as bt
encoded_list = ['AGACCCGCATGCATGCTTGC', 'TCTCTTGCGATCATGCACGC', 'CAGA']
decoded_list = bt.dnabits.decode_list(encoded_list)
print(decoded_list)
# ['Hello', 'world', '!']

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FASTA Tools (fastatools)

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biotext.fastatools.import_fasta

Uses biopython to import a FASTA file.

Parameters

• input_file_name : string (valid file name)
  • Input fasta file name.

Returns

• seqrecord_list : list of SeqRecord
  • List of SeqRecord imported from file.

Example

Import a FASTA file named 'sequences.fasta'.

import biotext as bt
input_file = 'sequences.fasta'
fasta = bt.fastatools.import_fasta(input_file)
# print first sequence in input file
print(fasta[0])
# ID: 1
# Name: 1
# Description: 1
# Number of features: 0
# Seq('HYELLYQYSYWYQRLD')

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biotext.fastatools.export_fasta

Create a file using a SeqRecord (Biopython object) list.

Parameters

• output_file_name : string
  • Output fasta file name.
• seqrecord_list : list of SeqRecord
  • List of SeqRecord.

Example

Export a SeqRecord list as FASTA file named 'sequences.fasta'.

import biotext as bt
seq_list = ['ACTG','GTCA']
seqrecord_list = bt.fastatools.create_seqrecord_list(seq_list)
bt.fastatools.export_fasta(seqrecord_list,'sequences.fasta')

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biotext.fastatools.create_seqrecord_list

Create a list of SeqRecord (Biopython object) with a string list.

Parameters

• seq_list : list of string
  • List of biological sequences in string format.
• header : list of string
  • List of headers in string format, if set to 'None' the headers will be automatically defined with numbers in increasing order.

Returns

• seqrecord_list : list of SeqRecord
  • List of SeqRecord.

Example

Decode a string.

import biotext as bt
seq_list = ['ACTG','GTCA']
seqrecord_list = bt.fastatools.create_seqrecord_list(seq_list)
for i in seqrecord_list:
    print (i)
# ID: 1
# Name: <unknown name>
# Description: 1
# Number of features: 0
# Seq('ACTG')
# ID: 2
# Name: <unknown name>
# Description: 2
# Number of features: 0
# Seq('GTCA')

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biotext.fastatools.run_clustalo

Run Clustal Omega multiple sequence alignment on the input file.

Parameters

• input_file_name : str
  • Path to the input file containing the sequences to align.
• args : str, optional
  • Additional arguments to pass to Clustal Omega. Defaults to an empty string.

Returns

• align : Bio.Align.MultipleSeqAlignment
  • Aligned sequences in the Clustal format.

Example

Perform multiple sequence alignment using Clustal Omega:

import biotext as bt
input_file = 'sequences.fasta'
alignment = bt.fastatools.run_clustalo(input_file)
print(alignment)
# Alignment with 3 rows and 16 columns
# HYELLYQYSYWYQRLD 1
# HYELLYQ--------- 2
# ---------YWYQRLD 3

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biotext.fastatools.get_consensus

Get the consensus sequence from a list of sequences.

Parameters

• seq_list : list
  • List of sequences in SeqRecord object format or as strings.
• preserve_gap : bool, optional
  • If True, the consensus sequence may contain gaps ("-") based on the majority characters and the gaps present in the aligned sequences.
  • If False, the consensus sequence is determined without gaps by considering only the majority characters.

Returns

• consensus : str
  • Consensus sequence based on the majority characters, with or without gaps ("-") depending on the preserve_gap parameter.
• align : list
  • List of aligned sequences.

Example

Calculate the consensus sequence from a list of sequences:

import biotext as bt
seq_list = ['ACTG', 'ACTC', 'ACCC', 'ACC']
consensus, align = bt.fastatools.get_consensus(seq_list)
print('Consensus: ', consensus)
print('Alignment: ', align)
# Consensus:  ACCC
# Alignment:  ['ACTG', 'ACTC', 'ACCC', 'ACC-']

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biotext.fastatools.get_header

Get the header from all items in a list of SeqRecord (Biopython object).

Parameters

• seqrecord_list : list of SeqRecord
  • List of SeqRecord.

Returns

• header_list : list of string
  • List of all headers extracted from input.

Example

Create seqrecord_list, extract headers and print it.

import biotext as bt
seq_list = ['ACTG','GTCA']
seqrecord_list = bt.fastatools.create_seqrecord_list(seq_list)
extracted_header_list = bt.fastatools.get_header(seqrecord_list)
print(extracted_header_list)
# ['1', '2']

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biotext.fastatools.get_seq

Get the sequences from all items in a list of SeqRecord (Biopython object).

Parameters

• seqrecord_list : list of SeqRecord
  • List of SeqRecord.

Returns

• seq_list : list of string
  • List of all sequences extracted from input.

Example

Create seqrecord_list, extract sequences and print it.

import biotext as bt
seq_list = ['ACTG','GTCA']
seqrecord_list = bt.fastatools.create_seqrecord_list(seq_list)
extracted_seq_list = bt.fastatools.get_seq(seqrecord_list)
print(extracted_seq_list)
# ['ACTG', 'GTCA']

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biotext.fastatools.fasta_to_mat

Convert FASTA sequences to a matrix representation using SWeeP method.

Parameters

• fasta : list
  • List of sequences in SeqRecord object format or as strings.
• mask : list, optional
  • A list specifying the mask values. Defaults to [2, 1, 2].
• **kwargs : dict, optional
  • Additional keyword arguments to pass to the fas2sweep function.

Returns

• mat : numpy.ndarray or scipy.sparse.lil_matrix
  • Matrix representation of the sequences.

Example

Convert FASTA sequences to a matrix representation:

import biotext as bt
seq_list = ['HYELLYQYSYWYQRLD', 'HYELLYQ', 'YWYQRLD']
matrix = bt.fastatools.fasta_to_mat(seq_list)
print(matrix.shape)
# (3, 600)

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Word Embedding Tools (wordembtools)

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wordembtools

A class for generating word embeddings from a collection of texts.

Parameters

• data_set : list or pandas.Series
  • The collection of texts to generate embeddings.
• word_set : list, optional
  • A pre-defined set of words to use for the embedding. Defaults to None.
• remove_stopwords : bool, optional
  • Whether to remove stop words from the texts. Defaults to False.
• stopwords_list : list, optional
  • Custom list of stop words to remove from the texts. Defaults to None.
• min_occ_to_use : int, optional
  • The minimum number of occurrences of a word in the collection of texts to include it in the embedding. Defaults to 100.
• max_words : int, optional
  • The maximum number of words to include in the word set. Defaults to 10,000.
• word_tokenizer_fun : function, optional
  • Custom function for tokenizing words in each text. Defaults to None.
• sweep_projection_mat : numpy.ndarray, optional
  • The projection matrix for SWeeP vectorization. Defaults to None.
• sweep_mask : list, optional
  • The mask to apply during SWeeP vectorization. Defaults to [2, 1, 2].
• sweep_dtype : dtype, optional
  • The data type for SWeeP vectorization. Defaults to None.
• sweep_composition : str, optional
  • The composition mode for SWeeP vectorization. Defaults to 'binary'.
• preserve_data_set_splited : bool, optional
  • Whether to preserve the split data set object. Defaults to False.
• preserve_data_set_sweeped : bool, optional
  • Whether to preserve the swept data set object. Defaults to False.
• n_jobs : int, optional
  • The number of jobs to use for parallelization. Defaults to 1.
• chunk_size : int, optional
  • The size of each chunk for parallelization. Defaults to 1000.
• sweep_n_jobs : int, optional
  • The number of jobs to use for SWeeP vectorization. Defaults to None.
  • If None, it receives the value of n_jobs.
• sweep_chunk_size : int, optional
  • The size of each chunk for SWeeP vectorization. Defaults to None.
  • If None, it receives the value of chunk_size.
• verbose : bool, optional
  • Whether to print progress messages. Defaults to True.

Attributes

• word_set : list
  • Set of unique words.
• word_embedding : numpy.ndarray
  • Word embeddings for the words in the word_set.
• elapsed_time : list
  • Elapsed time for each step of the process.

Example

import biotext as bt
texts = []
with open ('texts.txt', 'r') as file:
    for line in file:
        texts.append(line)
we = bt.wordembtools.WordEmbedding(data_set = texts)
embeddings = we.word_embedding

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Usage Examples

Encoding with AMINOcode

import biotext as bt

input_string = "Hello world!"
encoded_string = bt.aminocode.encode_string(input_string, 'dp')
print(encoded_string)
# HYELLYQYSYWYQRLDYPW

string_list = ['Hello', 'world', '!']
encoded_list = bt.aminocode.encode_list(string_list, detail='dp')
print(encoded_list)
# ['HYELLYQ', 'YWYQRLD', 'YPW']

Decoding with AMINOcode

import biotext as bt

encoded_string = "HYELLYQYSYWYQRLDYPW"
decoded_string = bt.aminocode.decode_string(encoded_string, 'dp')
print(decoded_string)
# hello world!

encoded_list = ['HYELLYQ', 'YWYQRLD', 'YPW']
decoded_list = bt.aminocode.decode_list(encoded_list, detail='dp')
print(decoded_list)
# ['hello', 'world', '!']

Importing and Exporting FASTA Files

import biotext as bt

input_file = 'sequences.fasta'
fasta = bt.fastatools.import_fasta(input_file)
print(fasta[0])  # Print the first sequence in the input file

seq_list = ['ACTG', 'GTCA']
seqrecord_list = bt.fastatools.create_seqrecord_list(seq_list)
bt.fastatools.export_fasta(seqrecord_list, 'sequences.fasta')

Generating Word Embeddings

import biotext as bt

texts = [
    'This is the first text.',
    'This is the second text.',
    'And this is the third text.'
]

we = bt.wordembtools.WordEmbedding(data_set=texts, min_occ_to_use=0)
embeddings = we.word_embedding
print(embeddings)

Vectorizing FASTA Sequence

import biotext as bt

seq_list = ['HYELLYQ', 'YWYQRLD', 'YPW']
matrix = bt.fastatools.fasta_to_mat(seq_list)
print(matrix.shape)
# (3, 600)

Encoding Text with AMINOcode and Vectorizing

import biotext as bt

texts = [
    'This is the first text.',
    'This is the second text.',
    'And this is the third text.'
]
encoded_texts = bt.aminocode.encode_list(texts, 'dp')
matrix = bt.fastatools.fasta_to_mat([encoded_texts])
print(matrix.shape)
# (3, 600)