sweep 2.0.1.1

SWeeP is a tool for representing large biological sequences datasets in compact vectors

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SWeeP: Sequence Window Projection

This Python package implements the SWeeP (Spaced Words Projection) algorithm for representing large biological sequence datasets in compact vectors. SWeeP allows efficient processing and analysis of sequence data by converting sequences into fixed-length feature vectors.

Installation

To use SWeeP in Python, install the package with the following command:

pip install sweep

Usage

Downloading the Default Projection Matrix

In the first use of fas2sweep with the default parameter, it will be necessary to download the default projection matrix. It is not necessary for use with custom projection matrix, as demonstrated in the "Changing Projection Matrix" topic. Here is how to perform the default matrix download:

from sweep import down_proj_mat
down_proj_mat() # Downloads the default projection matrix file

Handling Amino Acid Sequences

The default configurations of SWeeP are intended for vectorization of amino acid sequences. The default output is a matrix already projected with 600 columns. Here is an example of how to use SWeeP with amino acid sequences:

from sweep import fastaread, fas2sweep

fasta = fastaread("fasta_file_path")
vect = fas2sweep(fasta)

Changing Projection Matrix

To change the projection matrix, a new orthonormal matrix can be generated using the orthbase function. Here is an example of how to change the projection size to 300:

from sweep import fastaread, fas2sweep, orthbase

ob = orthbase(160000, 300)
fasta = fastaread("fasta_file_path")
vect = fas2sweep(fasta, orth_mat=ob)

Handling Nucleotide Sequences

For nucleotide sequences, there is no default projection matrix available in this version. Therefore, to work with nucleotides is possible to create a custom projection matrix using the orthbase function. The matrix size can be calculated using the calc_proj_mat_size function. Here is an example:

from sweep import fastaread, fas2sweep, orthbase, calc_proj_mat_size

mask = [4, 7, 4]
matrix_size = calc_proj_mat_size(mask, 'NT')
ob = orthbase(matrix_size, 600)
fasta = fastaread("fasta_file_path")
vect = fas2sweep(fasta, mask=mask, orth_mat=ob, fasta_type='NT')

Available Functions

Here is a summary of the functions available in the SWeeP package:

Function	Description	Input	Output
fastaread	Reads a FASTA file and returns a list of sequence records	fastaname (str): Path to the FASTA file	records (list): List of sequence records
fas2sweep	Converts a list of sequences into SWeeP vectors	fasta (list): List of sequence records	vect (numpy.ndarray): SWeeP vectors
orthbase	Generates an orthonormal projection matrix of the specified size	lin (int): Number of rows	mret (numpy.ndarray): Orthonormal matrix
calc_proj_mat_size	Calculates the number of lines in the projection matrix for a given mask	mask (list): Mask specifying dimensions	lines (int): Number of lines in the matrix
down_proj_mat	Downloads the default projection matrix file	destination (str): Path to the destination file (optional)	None
return_proj_mat_not_found_error	Raises an exception indicating that the default projection matrix is not found	None	None
check_default_proj_mat	Checks if the default projection matrix exists and matches the expected MD5 hash	file (str): Path to the projection matrix file	None
get_default_proj_mat	Retrieves the default projection matrix	None	orth_mat (numpy.ndarray): Projection matrix

Article Reference

If you use the SWeeP algorithm or this Python package in your research work, please cite the following article:

@article{Pierri2020,
  title={SWeeP: representing large biological sequences datasets in compact vectors},
  author={De Pierri, Camilla Reginatto and Voyceik, Ricardo and Santos de Mattos, Letícia Graziela Costa and Kulik, Mariane Gonçalves and Camargo, Josué Oliveira and Repula de Oliveira, Aryel Marlus and de Lima Nichio, Bruno Thiago and Marchaukoski, Jeroniza Nunes and da Silva Filho, Antonio Camilo and Guizelini, Dieval and Ortega, J. Miguel and Pedrosa, Fabio O. and Raittz, Roberto Tadeu},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={91},
  year={2020},
  doi={10.1038/s41598-019-55627-4},
  url={https://doi.org/10.1038/s41598-019-55627-4},
  issn={2045-2322}
}

Please refer to the article for a detailed description of the SWeeP algorithm and its applications.