sweep 2.0.0.0

  -----------------------------------

  SWeeP: Sequence Window Projection

  -----------------------------------



This Python package implements the SWeeP (Sequence Window 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.



    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\'s 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. For example,

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  |

| ``md5``                             | Calculates the MD5 hash of a file                                                | ``fname`` (str): Path to the file                              | ``hash_md5`` (str): MD5 hash of the file        |

| ``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:



``` none

@article{De 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.