Useful methods built on top of DNA Cauldron

class dnacauldron.utils.BackboneChoice(record, already_on_backbone=None, error=None, backbone_record=None, final_record=None)

Class to represent the result of a backbone autoselection.

Parameters:

• record – Record that was analyzed, containing an insert, to be backbonized, and potentially an original backbone too.

• already_on_backbone – Was a backbone detected in that record ?

• error

• backbone_record – Record of the backone that was selected for this record among all provided choices.

• final_record – Record featuring the original insert in the given record cloned into the auto-selected backbone.

static list_to_infos_spreadsheet(choices)

Return a pandas dataframe summarizing a list of BackboneChoices.

The dataframe’s columns are ‘original_record’, ‘already_on_backbone’, ‘detected_backbone’, ‘final_record_length’, ‘error’.

to_dict()

Return the object as a dict. Used for dataframe/spreadsheet.

static write_final_records(choices, directory)

Write a list of BackboneChoices final records as genbanks.

dnacauldron.utils.insert_parts_on_backbones(part_records, backbone_records, enzyme='autodetect', min_backbone_length=500, process_parts_with_backbone=False, default_backbone_choice=None)

Autodetect the right backbone for each Golden Gate part.

This method is meant to process a batch of genbank files, some of which might represent a part on a backbone, and some of which represent simply a part (and enzyme-flanked overhangs) which needs to be complemented with the right backbone.

It will return, for each part, whether it has already a backbone, and if not, which backbone was selected and what the final sequence is.

Parameters:

• part_records – List of genbanks of the parts to put on vectors.

• backbone_vectors – Vectors to insert parts in, typically donor vectors for different positions of an assembly standard.

• enzyme – Enzyme to use. Use autodetect for autodetection.

• min_backbone_length – Minimal length of a backbone. Used to determine if a part is represented alone or with a backbone.

• process_parts_with_backbone – If true, parts will be inserted in an autoselected backbone even when they already have a backbone (it will be replaced).

dnacauldron.utils.list_digestion_overhangs(records, enzyme='auto', parts_only=True)

List all overhangs created by restriction in the provided records.

Warning: only overhangs on non-reversed fragments are returned, not their reverse-complement.

Parameters:

• records – List of records

• enzyme – Either ‘BsmBI’, ‘BsaI’, etc. or just “auto” for automatic selection.

• parts_only – If true, overhangs created by restriction which are not on a part (so for instance inside a backbone) will be ignored.

dnacauldron.utils.list_overhangs_from_record_annotations(rec, with_locations=False)

Return a least of the (probable) overhangs used building the construct

dnacauldron.utils.record_contains_backbone(record, enzyme='BsmBI', min_backbone_length=500)

Return True iff it believes the given record contains a backbone.

A backbone is detected if, when cutting the circularized record with the given enzyme, there is one fragment with no site (the insert), and the rest has a total size above the given min_backbone_length.

dnacauldron.utils.substitute_overhangs(record, substitutions, enzyme='auto', return_linear_parts=False)

Replace the record’s subsequence that corresponds to overhangs.

This is practical to change the position of a part in a Type-2S assembly standard

Examples

>>> new_record = replace_overhangs(record, {'ATGC': 'CTCG'})

Parameters:

• record – A Biopython record whose internal sequence needs to be replaced

• substitutions – A dict {overhang: new_overhang} of which overhangs must be replaced

• enzyme – Either ‘BsmBI’, ‘BsaI’, etc. or just “auto” for automatic selection.

dnacauldron.utils.swap_donor_vector_part(donor_vector, insert, enzyme, allow_overhangs_edits=False)

Return the records obtained by cloning inserts into a donor vector.

Meant for Type-2S assembly standards only (Golden Gate, etc.)

This method is meant to quickly go from a linearized sequence of a part to a circular vector (the part in its donor vector) by starting from an existing donor vector (with same overhangs) and swapping this vector’s part for the insert of interest.

Parameters:

• donor_vector – Biopython record of a donor vector. must have an insert producing a restriction-free fragment

• insert – Biopython record of a plasmid or a linear DNA sequence containing an insert (i.e. a fragment that is cut out)

• enzyme – The name of the enzyme to use e.g. ‘BsmBI’, ‘BsaI’, …