Dna2vec is an open-source library to train distributed representations of variable-length k-mers.

For more information, please refer to the paper: dna2vec: Consistent vector representations of variable-length k-mers

Note that this implementation has only been tested on Python 3.5.3, but we welcome any contributions or bug reporting to make it more accessible.

1. Clone the dna2vec repository: git clone https://github.com/pnpnpn/dna2vec
2. Install Python dependencies: pip3 install -r requirements.txt
3. Test the installation: python3 ./scripts/train_dna2vec.py -c configs/small_example.yml

1. Download hg38 from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chromFa.tar.gz. This will take a while as it's 938MB.
2. Untar with tar -zxvf hg38.chromFa.tar.gz. You should see FASTA files for chromosome 1 to 22: chr1.fa, chr2.fa, ..., chr22.fa.
3. Move the 22 FASTA files to folder inputs/hg38/
4. Start the training with: python3 ./scripts/train_dna2vec.py -c configs/hg38-20161219-0153.yml
5. Wait for a couple of days ...
6. Once the training is done, there should be a dna2vec-<ID>.w2v and a corresponding dna2vec-<ID>.txt file in your results/ directory.

You can read pretrained dna2vec vectors pretrained/dna2vec-*.w2v using the class MultiKModel in dna2vec/multi_k_model.py. For example:

from dna2vec.multi_k_model import MultiKModel

filepath = 'pretrained/dna2vec-20161219-0153-k3to8-100d-10c-29320Mbp-sliding-Xat.w2v'
mk_model = MultiKModel(filepath)

You can fetch the vector representation of AAA with:

>>> mk_model.vector('AAA')
array([ 0.023137  ,  0.156295 , ...

Compute the cosine distance between two k-mers via dna2vec:

>>> mk_model.cosine_distance('AAA', 'GCT')
0.14546435594464155
>>> mk_model.cosine_distance('AAA', 'AAAA')
0.89000147450211231

The pre-trained data should cover all k-mers for 3 ≤ k ≤ 8

>>> [len(mk_model.model(k).vocab) for k in range(3,9)]
[64, 256, 1024, 4096, 16384, 65536]
>>> [4**k for k in range(3,9)]
[64, 256, 1024, 4096, 16384, 65536]

I would love for you to fork and send me pull request for this project. Please contribute.

This software is licensed under the MIT license