fastDNA is a library for classification of short DNA sequences. It is adapted from the fastText library.
Generally, fastText builds on modern Mac OS and Linux distributions. Since it uses some C++11 features, it requires a compiler with good C++11 support. These include :
- (g++-4.7.2 or newer) or (clang-3.3 or newer)
Compilation is carried out using a Makefile, so you will need to have a working make.
$ git clone https://github.com/rmenegaux/fastDNA.git
$ cd fastDNA
$ make
This will produce object files for all the classes as well as the main binary fastdna.
For a trial run:
$ cd test
$ sh test.sh
This should train and evaluate a small model on the toy dataset provided.
In order to train a dna classifier using the method described in 1, use:
$ ./fastdna supervised -input train.fasta -labels labels.txt -output model
where train.fasta is a FASTA file containing the full reference genomes and labels.txt is a text file containing the genome labels (one label per line).
This will output two files: model.bin and model.vec.
Once the model was trained, you can evaluate it by computing the precision and recall at k (P@k and R@k) on a test set using:
$ ./fastdna test model.bin test.fasta test_labels.txt n
where test.fasta is a FASTA file containing the DNA fragments to be classified, and test_labels.txt contains the labels for each of the fragments.
The argument n is optional, and is equal to 1 by default.
In order to obtain the n most likely labels for a set of reads, use:
$ ./fastdna predict model.bin test.fasta n
or use predict-prob to also get the probability for each label
$ ./fastdna predict-prob model.bin test.fasta n
Doing so will print to the standard output the n most likely labels for each line.
The argument n is optional, and equal to 1 by default.
If you want to compute vector representations of DNA sequences, please use:
$ ./fastdna print-word-vectors model.bin < text.fasta
This assumes that the text.fasta file contains the DNA sequences that you want to get vectors for.
The program will output one vector representation per sequence in the file.
To write the vectors to a file, redirect the output as so:
$ ./fastdna print-word-vectors model.bin < text.fasta > vectors.txt
To get vectors from standard input, just type
$ ./fastdna print-word-vectors model.bin
Press Enter, then type the sequence and finish with Ctrl+D (Linux, Mac) or Ctrl+Z (Windows)
You can also quantize a supervised model to reduce its memory usage with the following command:
$ ./fastdna quantize -output model
This will create a .ftz file with a smaller memory footprint. All the standard functionality, like test or predict work the same way on the quantized models:
$ ./fastdna test model.ftz test.fasta test_labels.txt
The quantization procedure follows the steps described in 3.
Invoke a command without arguments to list available arguments and their default values:
$ ./fastdna supervised
Empty input or output path.
The following arguments are mandatory:
-input training file path
-output output file path
The following arguments are optional:
-verbose verbosity level [2]
The following arguments for the dictionary are optional:
-minn min length of char ngram [0]
-maxn max length of char ngram [0]
-label labels prefix [__label__]
The following arguments for training are optional:
-lr learning rate [0.1]
-lrUpdateRate change the rate of updates for the learning rate [100]
-dim size of word vectors [100]
-noise mutation rate (/100,000)[0]
-length length of fragments for training [200]
-epoch number of epochs [5]
-loss loss function {ns, hs, softmax} [softmax]
-thread number of threads [12]
-pretrainedVectors pretrained word vectors for supervised learning []
-loadModel pretrained model for supervised learning []
-saveOutput whether output params should be saved [false]
-freezeEmbeddings model does not update the embedding vectors [false]
The following arguments for quantization are optional:
-cutoff number of words and ngrams to retain [0]
-retrain whether embeddings are finetuned if a cutoff is applied [false]
-qnorm whether the norm is quantized separately [false]
-qout whether the classifier is quantized [false]
-dsub size of each sub-vector [2]
Most use cases are covered in the python script fdna.py.
To reproduce the results from the paper, download the data then run:
python fdna.py -train -train_fasta /path/to/train_large_fasta -train_labels /path/to/train_large_labels \
-eval -test_fasta /path/to/test_large_fasta -test_labels /path/to/test_large_labels \
-k 13 -d 100 -noise 4 -e 200
NB: Best parameters for classification tasks are k=14, d=50, noise=4
Full usage:
python fdna.py --help
usage: fdna.py [-h] [-train] [-quantize] [-predict] [-eval] [-predict_quant]
[-train_fasta TRAIN_FASTA] [-train_labels TRAIN_LABELS]
[-test_fasta TEST_FASTA] [-test_labels TEST_LABELS]
[-output_dir OUTPUT_DIR] [-model_name MODEL_NAME]
[-threads THREADS] [-d D] [-k K] [-e E] [-lr LR] [-noise NOISE]
[-L L] [-freeze] [-pretrained_vectors PRETRAINED_VECTORS]
[-verbose VERBOSE]
train, predict and/or quantize fdna model
optional arguments:
-h, --help show this help message and exit
-train train model
-quantize quantize model
-predict make predictions
-eval make and evaluate predictions
-predict_quant make and evaluate predictions with quantized model
-train_fasta TRAIN_FASTA
training dataset, fasta file containing full genomes
-train_labels TRAIN_LABELS
training labels, text file containing as many labels
as there are training genomes
-test_fasta TEST_FASTA
testing dataset, fasta file containing reads
-test_labels TEST_LABELS
testing dataset, text file containing as many labels
as there are reads
-output_dir OUTPUT_DIR
output directory
-model_name MODEL_NAME
optional user-defined model name
-threads THREADS number of threads
-d D embedding dimension
-k K k-mer length
-e E number of training epochs
-lr LR learning rate
-noise NOISE level of training noise, percent of random mutations
-L L training read length
-freeze freeze the embeddings
-pretrained_vectors PRETRAINED_VECTORS
pretrained vectors .vec files
-verbose VERBOSE output verbosity, 0 1 or 2
The python scripts require numpy and scikit-learn for evaluating predictions.
The data used in the paper is available here: http://projects.cbio.mines-paristech.fr/largescalemetagenomics/.
The small and large datasets used in the paper can be found here
[1] R. Menegaux, J. Vert, Continuous Embedding of DNA reads, and application to metagenomics
@article{menegaux2018continuous,
title={Continuous Embedding of DNA reads and application to metagenomics},
author={Menegaux, Romain and Vert, Jean-Philippe},
journal={bioRxiv preprint 335943},
year={2018}
}
[2] P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information
@article{bojanowski2016enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.04606},
year={2016}
}
[3] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, Bag of Tricks for Efficient Text Classification
@article{joulin2016bag,
title={Bag of Tricks for Efficient Text Classification},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
journal={arXiv preprint arXiv:1607.01759},
year={2016}
}
[4] A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, FastText.zip: Compressing text classification models
@article{joulin2016fasttext,
title={FastText.zip: Compressing text classification models},
author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas},
journal={arXiv preprint arXiv:1612.03651},
year={2016}
}
- Contact: romain.menegaux@mines-paristech.fr
fastText is BSD-licensed. An additional patent grant is also provided