For anyone who actually uses this fork, Azimuth no longer works. It relies on Theanets, which in turn relies on Theano and Downhill. Well, Theano is dead and Theanets and Downhill haven't been updated since 2018. If I ever get really bored (or someone threw money at me) I maybe could be arsed to fix those (or replace them if I actually understood what they were used for), but since I don't work on making CRISPR libraries any longer and feel like I could do something more enjoyable at this time, I am abandoning it entirely.
This forked version of Azimuth has been extensively updated and gutted to run under Python 3. Currently, it runs (at least with Python 3.10+), is capable of generating new cutting efficiency models (with Scikit-learn 0.24.0) and passes the included unit tests. However, I have removed a quite a bit of code as compared to the source - in the original version, there were a multitude of dead ends, unused code, and interfaces to compute clusters that I ripped out instead of trying to update. Also, I hate matplotlib with a passion and actively tried to remove anything that used it. I will continue to rip these parts out in an attempt to reduce this to the minimal code necessary so as to improve maintainability. Please regard any and all plotting and metric generating functions as depreciated.
While the package technically passes its unit tests, do note that is because I relaxed the testing tolerance. The unit tests essentially involve scoring a collection of protospacers and comparing the new scores to ones generated by an older version of Azimuth. The scores calculated by this new version agrees with 998 out of 1000 spacers - the two protospacers that score slightly differently do so by a amount that could be explained by the update in scikit-learn or a random number seed. Thus, I believe the module truly passes its test and the library to be in working order.
So, please excuse the mess.
The CRISPR/Cas9 system provides state-of-the art genome editing capabilities. However, several facets of this system are under investigation for further characterization and optimization. One in particular is the choice of guide RNA that directs Cas9 to target DNA: given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the constraints of the CRISPR/Cas9 Protospacer Adjacent Motif sequence. However, only some of these guides efficiently target DNA to generate gene knockouts. One could laboriously and systematically enumerate all possible guides for all possible genes and thereby derive a dictionary of efficient guides, however, such a process would be costly, time-consuming, and ultimately not practically feasible. Instead, one can (1) enumerate all possible guides over each of some smaller set of genes, and then test these experimentally by measuring the knockout capabilities of each guide, (2) thereby assemble a training data set with which one can "learn", by way of predictive machine learning models, which guides tend to perform well and which do not, (3) use this learned model to generalize the guide efficiency for genes not in the training data set. In particular, by deriving a large set of possible predictive features consisting of both guide and gene characteristics, one can elicit those characteristics that define guide-gene pairs in an abstract manner, enabling generalizing beyond those specific guides and genes, and in particular, for genes which we have never attempted to knock out and therefore have no experimental evidence. Based on such a set of experiments, we present a state-of-the art predictive approach to modeling which RNA guides will effectively perform a gene knockout by way of the CRISPR/Cas9 system. We demonstrate which features are critical for prediction (e.g., nucleotide identity), which are helpful (e.g., thermodynamics), and which are redundant (e.g., microhomology); then we combine our insights of useful features with exploration of different model classes, settling on one model which performs best (gradient-boosted regression trees). Finally, we elucidate which measures should be used for evaluating these models in such a context.
(Also see the official project page )
Please cite this paper if using our predictive model:
John G. Doench*, Nicolo Fusi*, Meagan Sullender*, Mudra Hegde*, Emma W. Vaimberg*, Katherine F. Donovan, Ian Smith, Zuzana Tothova, Craig Wilen , Robert Orchard, Herbert W. Virgin, Jennifer Listgarten*, David E. Root. Optimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9. Nature Biotechnology, 2016. (* = equal contributions, corresponding author)
To view all the official releases of the Azimuth package, click on the "releases" tab above or follow this link.
Before installing Azimuth, we recommend downloading and installing Anaconda.
Azimuth is available from the python package index. From the command prompt, type:
pip install azimuthAlternatively, if you want access to the code, you can clone this repository.
To run our unit tests, navigate to the main Azimuth directory, install 'nose' and then from the command prompt, type
nosetestsIf these pass, you will see "OK" as the last printout.
If you prefer not to install python packages or download any code, you can use our model via Excel or as a web service. Instructions on how to do so are HERE
From python, you can get predictions from our model by running:
import azimuth.model_comparison
azimuth.model_comparison.predict(GUIDE, CUT_POSITION, PERCENT_PEPTIDE)[0]where GUIDE, PERCENT_PEPTIDE and CUT_POSITION are numpy arrays.
Note: if CUT_POSITION and PERCENT_PEPTIDE are not provided or provided as None, a
separate model will be used that does not consider protein target site information.
import azimuth.model_comparison
import numpy as np
sequences = np.array(['ACAGCTGATCTCCAGATATGACCATGGGTT', 'CAGCTGATCTCCAGATATGACCATGGGTTT', 'CCAGAAGTTTGAGCCACAAACCCATGGTCA'])
amino_acid_cut_positions = np.array([2, 2, 4])
percent_peptides = np.array([0.18, 0.18, 0.35])
predictions = azimuth.model_comparison.predict(sequences, amino_acid_cut_positions, percent_peptides)
for i, prediction in enumerate(predictions):
print sequences[i], predictionOutput:
No model file specified, using V3_model_full
ACAGCTGATCTCCAGATATGACCATGGGTT 0.672298196907
CAGCTGATCTCCAGATATGACCATGGGTTT 0.687944237021
CCAGAAGTTTGAGCCACAAACCCATGGTCA 0.659245390401
Although the data used for training were in the range 0.0 to 1.0, the predictions made by the final model are not explicitly normalized, so it is possible for Azimuth to make predictions outside of this range. We expect this to be somewhat rare, and it is reasonable to set these values to the closest part of the range [0.0, 1.0] (i.e. negative values to 0 and values greater than 1 to 1.0) if it is easier for your purposes.
Sometimes the pre-computed .pickle files in the saved_models directory are incompatible with different versions of scikitlearn. You can re-train the files saved_models/V3_model_full.pickle and saved_models/V3_model_nopos.pickle by running the command python model_comparison.py (which will overwrite the saved models). You can check that the resulting models match the models we precomputed by running python test_saved_models.py within the directory tests.
You can submit bug reports using the GitHub issue tracker. If you have any other questions, please contact us at [email protected].