backbone package

Submodules

backbone.bipopulations module

Module containing the BIPopulations class and the command line interface.

class backbone.bipopulations.BIPopulations(input_epsilC_path, input_epsilW_path, input_zetaC_path, input_zetaW_path, output_csv_path, output_jpg_path, properties=None, **kwargs)[source]

Bases: biobb_common.generic.biobb_object.BiobbObject

biobb_dna BIPopulations
Calculate BI/BII populations from epsilon and zeta parameters.
Parameters
  • input_epsilC_path (str) – Path to .ser file for helical parameter ‘epsilC’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_epsilW_path (str) –

    Path to .ser file for helical parameter ‘epsilW’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_zetaC_path (str) –

    Path to .ser file for helical parameter ‘zetaC’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_zetaW_path (str) –

    Path to .ser file for helical parameter ‘zetaW’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • output_csv_path (str) –

    Path to .csv file where output is saved. File type: output. Sample file. Accepted formats: csv (edam:format_3752).

  • output_jpg_path (str) –

    Path to .jpg file where output is saved. File type: output. Sample file. Accepted formats: jpg (edam:format_3579).

  • properties (dict) –

    • sequence (str) - (None) Nucleic acid sequence corresponding to the input .ser file. Length of sequence is expected to be the same as the total number of columns in the .ser file, minus the index column (even if later on a subset of columns is selected with the seqpos option).

    • seqpos (list) - (None) list of sequence positions (columns indices starting by 0) to analyze. If not specified it will analyse the complete sequence.

Examples

This is a use example of how to use the building block from Python:

from biobb_dna.backbone.bipopulations import bipopulations

prop = {
    'sequence': 'GCAT',
}
bipopulations(
    input_epsilC_path='/path/to/epsilC.ser',
    input_epsilW_path='/path/to/epsilW.ser',
    input_zetaC_path='/path/to/zetaC.ser',
    input_zetaW_path='/path/to/zetaW.ser',
    output_csv_path='/path/to/table/output.csv',
    output_jpg_path='/path/to/table/output.jpg',
    properties=prop)
Info:
get_angles_difference(epsilC, zetaC, epsilW, zetaW)[source]
get_xlabels(strand1, strand2)[source]
launch() int[source]

Execute the BIPopulations object.

backbone.bipopulations.bipopulations(input_epsilC_path: str, input_epsilW_path: str, input_zetaC_path: str, input_zetaW_path: str, output_csv_path: str, output_jpg_path: str, properties: Optional[dict] = None, **kwargs) int[source]

Create BIPopulations class and execute the: meth: launch() <dna.backbone.bipopulations.BIPopulations.launch> method.

backbone.bipopulations.main()[source]

Command line execution of this building block. Please check the command line documentation.

backbone.canonicalag module

Module containing the CanonicalAG class and the command line interface.

class backbone.canonicalag.CanonicalAG(input_alphaC_path, input_alphaW_path, input_gammaC_path, input_gammaW_path, output_csv_path, output_jpg_path, properties=None, **kwargs)[source]

Bases: biobb_common.generic.biobb_object.BiobbObject

biobb_dna CanonicalAG
Calculate Canonical Alpha/Gamma populations from alpha and gamma parameters.
Parameters
  • input_alphaC_path (str) –

    Path to .ser file for helical parameter ‘alphaC’. File type: input. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_alphaW_path (str) –

    Path to .ser file for helical parameter ‘alphaW’. File type: input. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_gammaC_path (str) –

    Path to .ser file for helical parameter ‘gammaC’. File type: input. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_gammaW_path (str) –

    Path to .ser file for helical parameter ‘gammaW’. File type: input. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • output_csv_path (str) –

    Path to .csv file where output is saved. File type: output. File type: output. Sample file. Accepted formats: csv (edam:format_3752).

  • output_jpg_path (str) –

    Path to .jpg file where output is saved. File type: output. File type: output. Sample file. Accepted formats: jpg (edam:format_3579).

  • properties (dict) –

    • sequence (str) - (None) Nucleic acid sequence corresponding to the input .ser file. Length of sequence is expected to be the same as the total number of columns in the .ser file, minus the index column (even if later on a subset of columns is selected with the seqpos option).

    • seqpos (list) - (None) list of sequence positions (columns indices starting by 0) to analyze. If not specified it will analyse the complete sequence.

    • remove_tmp (bool) - (True) [WF property] Remove temporal files.

    • restart (bool) - (False) [WF property] Do not execute if output files exist.

Examples

This is a use example of how to use the building block from Python:

from biobb_dna.backbone.canonicalag import canonicalag

prop = {
    'helpar_name': 'twist',
    'seqpos': [1,2],
    'sequence': 'GCAT',
}
canonicalag(
    input_alphaC_path='/path/to/alphaC.ser',
    input_alphaW_path='/path/to/alphaW.ser',
    input_gammaC_path='/path/to/gammaC.ser',
    input_gammaW_path='/path/to/gammaW.ser',
    output_csv_path='/path/to/table/output.csv',
    output_jpg_path='/path/to/table/output.jpg',
    properties=prop)
Info:
check_alpha_gamma(alphaC, gammaC, alphaW, gammaW)[source]
fix_angles(dataset)[source]
get_xlabels(strand1, strand2)[source]
launch() int[source]

Execute the CanonicalAG object.

backbone.canonicalag.canonicalag(input_alphaC_path: str, input_alphaW_path: str, input_gammaC_path: str, input_gammaW_path: str, output_csv_path: str, output_jpg_path: str, properties: Optional[dict] = None, **kwargs) int[source]

Create CanonicalAG class and execute the: meth: launch() <dna.backbone.canonicalag.CanonicalAG.launch> method.

backbone.canonicalag.main()[source]

Command line execution of this building block. Please check the command line documentation.

backbone.puckering module

Module containing the Puckering class and the command line interface.

class backbone.puckering.Puckering(input_phaseC_path, input_phaseW_path, output_csv_path, output_jpg_path, properties=None, **kwargs)[source]

Bases: biobb_common.generic.biobb_object.BiobbObject

biobb_dna Puckering
Calculate Puckering from phase parameters.
Parameters
  • input_phaseC_path (str) –

    Path to .ser file for helical parameter ‘phaseC’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • input_phaseW_path (str) –

    Path to .ser file for helical parameter ‘phaseW’. File type: input. Sample file. Accepted formats: ser (edam:format_2330).

  • output_csv_path (str) –

    Path to .csv file where output is saved. File type: output. Sample file. Accepted formats: csv (edam:format_3752).

  • output_jpg_path (str) –

    Path to .jpg file where output is saved. File type: output. Sample file. Accepted formats: jpg (edam:format_3579).

  • properties (dict) –

    • sequence (str) - (None) Nucleic acid sequence corresponding to the input .ser file. Length of sequence is expected to be the same as the total number of columns in the .ser file, minus the index column (even if later on a subset of columns is selected with the seqpos option).

    • helpar_name (str) - (None) helical parameter name.

    • stride (int) - (1000) granularity of the number of snapshots for plotting time series.

    • seqpos (list) - (None) list of sequence positions (columns indices starting by 0) to analyze. If not specified it will analyse the complete sequence.

    • remove_tmp (bool) - (True) [WF property] Remove temporal files.

    • restart (bool) - (False) [WF property] Do not execute if output files exist.

Examples

This is a use example of how to use the building block from Python:

from biobb_dna.backbone.puckering import puckering

prop = {
    'sequence': 'GCAT',
}
puckering(
    input_phaseC_path='/path/to/phaseC.ser',
    input_phaseW_path='/path/to/phaseW.ser',
    output_csv_path='/path/to/table/output.csv',
    output_jpg_path='/path/to/table/output.jpg',
    properties=prop)
Info:
check_puckering(phaseC, phaseW)[source]
fix_angles(dataset)[source]
get_xlabels(strand1, strand2)[source]
launch() int[source]

Execute the Puckering object.

backbone.puckering.main()[source]

Command line execution of this building block. Please check the command line documentation.

backbone.puckering.puckering(input_phaseC_path: str, input_phaseW_path: str, output_csv_path: str, output_jpg_path: str, properties: Optional[dict] = None, **kwargs) int[source]

Create Puckering class and execute the: meth: launch() <dna.backbone.puckering.Puckering.launch> method.