Chiron

A basecaller for Oxford Nanopore Technologies' sequencers

Using a deep learning CNN+RNN+CTC structure to establish end-to-end basecalling for the nanopore sequencer.
Built with TensorFlow and python 2.7 by members of the Coin Group at the Institute for Molecular Bioscience (University of Queensland).

Preprint out now at http://www.biorxiv.org/content/early/2017/08/24/179531

Install

Install using `pip` (recommended)

If you currently have TensorFlow installed on your system, we would advise you to create a virtual environment to install Chiron into, this way there is no clash of versions etc.

If you would like to do this, the best options would be virtualenv, the more user-friendly virtualenvwrapper, or through anaconda. After installing one of these and activating the virtual environment you will be installing Chiron into, continue with the rest of the installation instructions as normal.

To install with pip:

pip install chiron

This will install Chiron, the CPU-only distribution of TensorFlow (and it's dependencies), and h5py (required for reading in .fast5 files).

Note: If you are after the GPU version, follow the steps in the following section.

Install from GitHub

This is currently the best install method if you are wanting to run Chiron on in GPU mode (pip install version is coming).

git clone https://github.com/haotianteng/chiron.git
cd chiron

You will also need to install dependencies.

For CPU-version:

pip install tensorflow==1.0.1  
pip install h5py

For GPU-version(Nvidia GPU required):

pip install tensorflow-gpu==1.0.1  
pip install h5py

For alternate/detailed installation instructions for TensorFlow, see their fantastic documentation.

Basecall

If installed from `pip`:

An example call to Chiron to run basecalling is:

chiron call -i <input_fast5_folder> -o <output_folder>

Simples!

If installed from GitHub:

All Chiron functionality can be run from entry.py in the Chiron folder. (You might like to also add the path to Chiron into your path for ease of running).

python chiron/entry.py call -i <input_fast5_folder> -o <output_folder>

Test run

We provide 5 sample fast5 files (courtesy of nanonet) in the GitHub repository which you can run a test on. These are located in chiron/example_data/. From inside the Chiron repository:

python chiron/entry.py call -i chiron/example_folder/ -o <output_folder>

Output

chiron call will create five folders in <output_folder> called raw, result, segments, meta, and reference.

result: Fasta files with the same name as the fast5 file they contain the basecalling result for. To create a single, merged version of these fasta files, try something like paste --delimiter=\\n --serial result/*.fasta > merged.fasta
raw: Contains a file for each fast5 file with it's raw signal. This file format is an list of integers. i.e 544 554 556 571 563 472 467 487 482 513 517 521 495 504 500 520 492 506 ...
segments: Contains the segments basecalled from each fast5 file.
meta: Contains the meta information for each read (read length, basecalling rate etc.). Each file has the same name as it's fast5 file.
reference: Contains the reference sequence (if any).

Training

Usually the default model works fine on the R9.4 protocol, but if the basecalling result is not satisfying, you can train a model on your own training data set.

Hardware request:

Recommend training on GPU with TensorFlow - usually 8GB RAM (GPU) is required.

Prepare the training data set.

Need .signal file and correspond .label file, a typical file format:

.signal file format:
544 554 556 571 563 472 467 487 482 513 517 521 495 504 500 520 492 506 ...
i.e the file must contain only one row/column of raw signal numbers.

.label file format:

Each line represents a DNA base pair in the Pore.

1st column: Start position of the current nucleotide, position related to the signal vector (index count starts from zero).
2nd column: End position of the current nucleotide.
3rd column: Nucleotide, for DNA: A, G, C, or T. Although, there is no reason you could not use other labels.

Adjust Chiron parameters

Go in to chiron/chiron_rcnn_train.py and change the hyper parameters in the FLAGS class.

class Flags():  
    def __init__(self):  
        self.home_dir = "/home/haotianteng/UQ/deepBNS/"  
        self.data_dir = self.home_dir + 'data/Lambda_R9.4/raw/'  
        self.log_dir = self.home_dir+'/chiron/log/'  
        self.sequence_len = 200  
        self.batch_size = 100  
        self.step_rate = 1e-3   
        self.max_steps = 2500  
        self.k_mer = 1  
        self.model_name = 'crnn5+5_res_moving_norm'  
        self.retrain = False

data_dir: The folder containing your signal and label files.
log_dir: The folder where you want to save the model.
sequence_len: The length of the segment you want to separate the sequence into. Longer length requires larger RAM.
batch_size: The batch size.
step_rate: Learning rate of the optimizer.
max_step: Maximum step of the optimizer.
k_mer: Chiron supports learning based on k-mer instead of a single nucleotide, this should be an odd number, even numbers will cause an error.
model_name: The name of the model. The record will be stored in the directory log_dir/model_name/ retrain: If this is a new model, or you want to load the model you trained before. The model will be loaded from log_dir/model_name/

Train!

source activate tensorflow   
python chiron/chiron_rcnn_train.py

adbailey4/chiron