Golden Hinges Documentation¶

Golden Hinges is a Python library to find sets of overhangs (also called junctions, or protusions) for multipart DNA assembly such as Golden Gate assembly.

Given a set of constraints (GC content bounds, differences between overhangs, mandatory and forbidden overhangs) Golden Hinges enables to find:

Maximal sets of valid and inter-compatible overhangs.
Sequence decompositions (i.e. position of cuts) which produce valid and inter-compatible overhangs, for type-2S DNA assembly.
Sequence mutations (subject to constraints) which enable the sequence decomposition, in exterme cases where the original sequence does not allow for such decomposition.

You can see Golden Hinges in action in this web demo:

Examples of use¶

Finding maximal overhangs sets¶

Let us compute a collection of overhangs, as large as possible, where

All overhangs have 25-75 GC%
There is a 2-basepair difference between any two overhangs (and their reverse-complement)
The overhangs ATGC and CCGA are forbidden

Here is the code

from goldenhinges import OverhangsSelector
selector = OverhangsSelector(gc_min=0.25, gc_max=0.5, differences=2,
                             forbidden_overhangs=['ATGC', 'CCGA'])
overhangs = selector.generate_overhangs_set()
print (overhangs)

Result:

>>> ['AACG', 'CAAG', 'ACAC', 'TGAC', 'ACGA', 'AGGT', 'TGTG', 'ATCC', 'AAGC',
>>>  'AGTC', 'TCTC', 'TAGG', 'AGCA', 'GTAG', 'TGGA', 'ACTG', 'GAAC', 'TCAG',
>>>  'ATGG', 'TTGC', 'TTCG', 'GATG', 'AGAG', 'TACC']

In some cases this may take some time to complete, as the algorithm slowly builds collections of increasing sizes. An alternative algorithm consisting in finding random maximal sets of compatible overhangs is much faster, but gives suboptimal solutions:

overhangs = selector.generate_overhangs_set(n_cliques=5000)

Result:

>>> ['CAAA', 'GTAA', 'ATTC', 'AATG', 'ACAT', 'ATCA', 'AGAG', 'GCTT', 'AGTT',
>>>  'TCGT', 'CTGA', 'TGGA', 'TAGG', 'GGTA', 'GACA']

The two approaches can be combined to first find an approximate solution, then attempt to find larger sets:

test_overhangs = selector.generate_overhangs_set(n_cliques=5000)
overhangs = selector.generate_overhangs_set(start_at=len(test_overhangs))

Finding a sequence decomposition¶

In this example, we find where to cut a 50-kilobasepair sequence to create assemblable fragments with 4-basepair overhangs. We indicate that:

There should be 50 fragments, with a minimum of variance in their sizes.
The fragments overhangs should have 25-75 GC% with a 1-basepair difference between any two overhangs (and their reverse-complement). They should also be compatible with the 4-basepair extremities of the sequence.

from Bio import SeqIO
from goldenhinges import OverhangsSelector

sequence = SeqIO.read
selector = OverhangsSelector(gc_min=0.25, gc_max=0.75, differences=1)
solution = selector.cut_sequence(sequence, equal_segments=50,
                                 max_radius=20, include_extremities=True)

This returns a list of dictionaries, each representing one overhang with properties o['location'] (coordinate of the overhang in the sequence) and o['sequence'] (sequence of the overhang).

This solution can be turned into a full report featuring all sequences to order (with restriction sites added on the left and right flanks), and a graphic of the overhang’s positions, using the following function:

from goldenhinges.reports import write_report_for_cutting_solution

write_report_for_cutting_solution(solution, 'full_report.zip', sequence,
                                  left_flank='CGTCTCA',
                                  right_flank='TGAGACG',
                                  display_positions=False)

Sequence mutation and decomposition from a Genbank file¶

If the input sequence is a Genbank record (or a Biopython record) has locations annotated vy features feature labeled !cut, GoldenHinges will attempt to find a decomposition with exactly one cut in each of these locations (favoring cuts located near the middle of each region).

GoldenHinges also allows to modify the sequence to enable some decomposition. Note that solutions involving base changes are penalized and solutions involving the original solution will always be prefered, so no base change will be suggested unless strictly necessary.

If the input record has DNA Chisel annotations such as @AvoidChanges or @EnforceTranslation, these will be enforced to forbid some mutations.

Here is an example of such a record:

And here is the code to optimize and decompose it:

record = SeqIO.read(genbank_file, 'genbank')
selector = OverhangsSelector(gc_min=0.25, gc_max=0.75, differences=2)
solution = selector.cut_sequence(record, allow_edits=True,
                                 include_extremities=True)

Installation¶

Install Numberjack’s dependencies first:

sudo apt install python-dev swig libxml2-dev zlib1g-dev libgmp-dev

Then, if you have PIP installed, just type in a terminal:

(sudo) pip install goldenhinges

Golden Hinges can be installed by unzipping the source code in one directory and using this command:

sudo python setup.py install

Contribute !¶

Golden Hinges is an open-source software originally written at the Edinburgh Genome Foundry by Zulko and released on Github under the MIT licence. Everyone is welcome to contribute!