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Golden Hinges (full documentation here <https://edinburgh-genome-foundry.github.io/GoldenHinges/>_) is a Python library to find sets
of overhangs (also called junctions, or protrusions) 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:
You can see Golden Hinges in action in this web demo:
Design Golden Gate Overhangs <http://cuba.genomefoundry.org/design-overhangs>_
Finding maximal overhang 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
.. code:: python
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:
.. code:: python
>>> ['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:
.. code:: python
overhangs = selector.generate_overhangs_set(n_cliques=5000)
Result:
.. code:: python
>>> ['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:
.. code:: python
test_overhangs = selector.generate_overhangs_set(n_cliques=5000)
overhangs = selector.generate_overhangs_set(start_at=len(test_overhangs))
Using experimental annealing data from Potapov 2018This study by Potapov et al. <https://www.biorxiv.org/content/early/2018/05/15/322297>
provides insightful data on overhang annealing, in particular which overhangs
have weak general annealing power, and which pairs of overhangs have significant
"cross-talk". You can use the data in this paper via the Python
tatapov <https://github.com/Edinburgh-Genome-Foundry/tatapov> library
to identify which overhangs or overhang pairs you want the GoldenHinges
OverhangSelector to exclude:
.. code:: python
import tatapov
from goldenhinges import OverhangsSelector
annealing_data = tatapov.annealing_data['37C']['01h']
self_annealings = tatapov.relative_self_annealings(annealing_data)
weak_self_annealing_overhangs = [
overhang
for overhang, self_annealing in self_annealings.items()
if self_annealing < 0.05
]
cross_annealings = tatapov.cross_annealings(annealing_data)
high_cross_annealing_pairs = [
overhang_pair
for overhang_pair, cross_annealing in cross_annealings.items()
if cross_annealing > 0.005
]
selector = OverhangsSelector(
forbidden_overhangs=weak_self_annealing_overhangs,
forbidden_pairs=high_cross_annealing_pairs
)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.
.. code:: python
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 dictionnaries, 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:
.. code:: python
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 fileIf 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 <https://github.com/Edinburgh-Genome-Foundry/DnaChisel>_
annotations such as @AvoidChanges or @EnforceTranslation, these will be
enforced to forbid some mutations.
Here is an example of such a record:
.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/GoldenHinges/master/examples/data/sequence_with_constraints.png :alt: [sequence with constraints] :align: center :width: 672px
And here is the code to optimize and decompose it:
.. code:: python
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)Install Numberjack's dependencies first:
.. code:: python
sudo apt install python-dev swig libxml2-dev zlib1g-dev libgmp-devIf you have PIP installed, just type in a terminal:
.. code:: python
pip install goldenhingesGolden Hinges can be installed by unzipping the source code in one directory and using this command:
.. code:: python
sudo python setup.py installIf you have trouble installing NumberJack, you may try using swig v3 (e.g. Ubuntu 20.04 has swig version 4):
.. code:: shell
apt-get remove -y swig
apt-get install -y swig3.0
ln /usr/bin/swig3.0 /usr/bin/swigThen install Numberjack with pip. You may also try and build it from source:
.. code:: shell
wget https://github.com/Edinburgh-Genome-Foundry/Numberjack/archive/v1.2.0.tar.gz
tar -zxvf v1.2.0.tar.gz
cd Numberjack-1.2.0
python setup.py build -solver Mistral
python setup.py installGolden Hinges is an open-source software originally written at the
Edinburgh Genome Foundry <http://edinburgh-genome-foundry.github.io/home.html>
by Zulko <https://github.com/Zulko> and
released on Github <https://github.com/Edinburgh-Genome-Foundry/GoldenHinges>_
under the MIT licence. Everyone is welcome to contribute!