To install pyCUB for now you need to process to a raw install
Make sure to have anaconda installed https://anaconda.org/anaconda/python
Then follow the step to create an environment.
install jupyter the same way
Once this is done you will have to get this project:
git clone https://github.com/jkobject/CodonUsageBias.git
Then enter the project:
cd CodonUsageBias
Then you have to run the requirements to download the required packages:
pip install -r requirements.txt
Make sure to have gzip and R installed and up to date.
You should install also:
git clone https://github.com/DmitryUlyanov/Multicore-TSNE.git
cd Multicore-TSNE/
pip install .
if you run into some problems you might have a look at installationec2.sh which contains further informations when installing remotly from an ec2 instance for more compute power
Once this is done you can:
jupyter notebook pipeline.ipynb
And run it to test it yourself !
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The goals of this project is to try to infer relations in the codon usage of different genes from different species using machine learning and to let researchers use it and improve it as they please. It is also to demonstrate the use of entropy as a convenient measure of CUB and to reflect on reusability and scalability in ML and bioinformatics.
A goal for me at this point is to have a the pipeline running.
Pursue on the ideas defined here and on the TODO file.
I am also preparing a presentation available on slideshare soon.
And at the end of the summer, a Research Document.
Find more information in my INFO and TODO files.
For now the Pipeline is able to :
- let me load and save the data in the right format,
- get the precomputed data from Kent University
- Visualize and cluster homologies
- Compute and visualize a k-mean clustering from a homology
to come
1/ I am getting Yun’s file directly from the dropbox server. Then I am mapping then to the datastructure and I am using currently the entropy-location values. Each amino acid’s entropy location values for one homology defines a dimension of a feature vector describing the codon usage statistics of a species’ gene.
2/ As it is required to do a good comparison, to have species that share a lot of homologies, I am clustering groups of species that the highest number of shared homologies. You can see this comparison as a 2D matrix showing binary values representing if a species has this particular homology. A similarity matrix can be displayed as well to see particular similarities.*
3/ Using each highly similar groups I am taking the homologies one by one and apply it another clustering algorithm on the entropy location vector to group species with similar entropy features. *
Clusters for particular homologies can be visualized using T-sne from Hinton et. al.. http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html To display them on a two dimensional surface (dimensionality reduction). This plot can be made interactive to see species corresponding to each dots
4/ Using the statistical information on the process and data exploration on the outputted clusters, the next steps shall be defined.
Trying to find explanatory statistics of the features that I have as an input using :
- Autoencoders
- Linear factor models
- From the clusters using non linear classifiers that would explain some metadatas that Tobias is gathering.
- Using more data from other databases.
- Using other statistics on the codon usages.
repeated with different clustering algorithm and tested with statistical tests
to come
This work is in partnership of Yun Deng - Dominique Chu - Tobias Van der Harr.
Yun Deng is the sole producer of the statistical technique and files that I am getting for now - the entropy computation function is an implementation of Yun’s.
Thanks to the University of Kent.
Thanks to scikit-learn.
not for use as of right now
This work is under MIT licence
Follow me on twitter: @jkobject
jkobject.com